Due Diligence Research

2026-09-24 industrial-arm

Latent world models are usually trained to predict the factual transition that actually happened, but model predictive control needs to compare actions that never happened. A model can therefore reach low factual prediction error while barely distinguishing candidate actions. AD-WM addresses this with residual latent dynamics plus predictor-level action-recovery regularization — an inverse-dynamics head and a normalized recovery objective motivated by conditional mutual information — so that imagined transitions retain the information needed to identify the action that produced them. The auxiliary heads are dropped at test time, leaving the MPC loop unchanged. On OGBench-Cube, hard-start success rises from 3.7% to 52.0% over a matched baseline, mean success improves in four of five simulated environments, and with a frozen V-JEPA 2 encoder and matched DROID post-training, zero-shot Franka pick-and-place improves from 42.2% to 71.1% without lab-specific adaptation.

RAPID: Turning a Single Human Demo Into a Reusable Robot Program

Coding agents have proven effective at complex programming tasks, and RAPID asks whether that strength can be redirected at robot systems. Given a single visual human demonstration, RAPID automatically generates, verifies, and refines robot programs in an iterative agentic loop. To make this work it infers three ingredients from the demonstration alone: a testable task specification, the action primitives for execution, and an interactive environment for program execution and verification. To let the resulting program generalize beyond the demonstration setting, RAPID uses an object-centric relational program representation that captures the structure of the demonstrated strategy rather than the specific motion, expressing primitives as trajectory-optimization programs that realize object-level motion effects and composing them through relational constraints resolved against scene geometry at run time. It was evaluated in simulation on eight challenging contact-rich nonprehensile manipulation tasks and on LIBERO-Pro, and deployed on a real Franka arm for all eight nonprehensile tasks, showing generalization over object pose, shape, material, and environment.

Self-Adaptive VLA: Keeping Robot Policies Calibrated When the Hardware Drifts

2026-09-24 general

Vision-language-action models manipulate impressively but are memoryless, which makes them brittle to test-time environment shifts — especially hardware shifts caused by wear or imperfect calibration. Enabling these policies to self-adapt during deployment, without continuous on-site recalibration, is a critical bottleneck for real-world scalability. Self-Adaptive VLA is a post-training recipe that lets a policy iteratively adapt to deployment-time hardware shifts using its own rollouts as context. It first collects rollouts under deliberately injected hardware shifts, then transforms the base policy's training data into shift-conditioned expert demonstrations by pre-compensating expert actions for the known shifts. A lightweight plug-in context encoder compresses the visual observation, proprioception, and actions from the shifted environment into a latent context token, which modulates the policy through adaptive layer normalization. The context tokens can also be ensembled, letting the policy self-correct and mitigate failures step by step. Across four precision-critical bi-manual and dexterous manipulation tasks, Self-Adaptive VLA recovers more than 80% of the base policy's performance under shifts such as actuation bias and joint encoder offsets, and deploys more robustly to new workstations than the base policy.

RACaP: Move Coding to Evolution Time, Call Frozen Policy APIs at Run Time

Code-as-Policies methods let a model write and repair programs at run time, paying for it in latency and entangling reusable mechanisms with task-specific decisions. RACaP moves code generation out of deployment and into evolution: at run time a Reasoning-and-Acting loop calls a set of frozen, typed policy APIs, and tasks are adapted through arguments rather than rewritten source. A two-phase strategy — capability curriculum learning followed by autonomous self-evolution — improves the APIs, the ReAct harness, and an experience memory together. RACaP reaches 54.4% success on LIBERO-90, 45.0% zero-shot on LIBERO-PRO, and 46.0% on LIBERO-Long, where Code-as-Policies baselines top out at 4.0% on long-horizon tasks; on LIBERO-PRO that is 2.5x the baseline success rate and a 1.9-fold speedup in median policy time. For on-robot deployment, rejection-sampled fine-tuning distils GPT-5.6 ReAct decisions into Qwen3-VL-8B-Instruct, giving a 13.2x per-decision speedup and cutting repeated physical calls from 16 to 4.

More Room for Magnets: Expanding Electromagnetic Navigation Workspace with Feedback Control

Magnetically guided surgical tools are limited less by what magnets can do than by how much current a coil array can push before it overheats: the usable workspace of an electromagnetic navigation system (eMNS) is carved out by power and thermal budgets. This paper shows that the shape of that workspace is largely a control-design choice. By moving from field-centric field alignment to motion-centric torque and force objectives, allocating coil currents in an energy-optimal way, closing the loop with real-time pose estimation, and using high-bandwidth hardware, the authors stabilise a 3D inverted pendulum on an eight-coil OctoMag system at 0.1–0.2 A instead of 8–14 A. The same ideas extend to multi-agent control, with two inverted pendulums balanced in a shared workspace, and transfer to the clinically oriented Navion platform, which maintains stable balancing up to 50 cm from the coils.

World In Your Hands: A Thousand Hours of In-the-Wild Manipulation Data, Made Open

2026-09-24 general

Robot manipulation is gated less by policy architecture than by data, and World In Your Hands (WIYH) attacks that constraint at the ecosystem level: a wearable capture kit with an automatic labelling pipeline, a dataset of more than 1,000 hours of in-the-wild multimodal manipulation spanning hundreds of skills, and annotations plus benchmarks that reach from perception to action. The authors report millimetre-scale motion accuracy from the capture hardware and show that folding WIYH's human data into robot training lifts manipulation success in cluttered scenes from 8% to 60%. Both the dataset and the hardware designs are released openly, positioning WIYH as infrastructure for cross-embodiment policy learning rather than a single-task result.

Whole-Body UMI: Turning Hand-Held Manipulation Demos into Humanoid Whole-Body Skills

2026-09-24 humanoid

Humanoid whole-body skills are usually taught by teleoperation, which is expensive and hard to scale. The Universal Manipulation Interface (UMI) offers a cheaper, scalable way to record hand-held manipulation, but end-effector trajectories alone do not determine how a humanoid should coordinate its entire body. Whole-Body UMI (WB-UMI) closes that gap with a task-agnostic, real-time, end-effector-conditioned motion generator that learns whole-body coordination separately from task semantics and shares only the end-effector interface between them. A diffusion policy consumes native UMI demonstrations while WB-UMI learns from retargeted motion capture, so task-specific collection needs neither body trackers nor paired image and whole-body data. On a G1 humanoid the stack transfers in closed loop across four tasks, reaching 90% success on drawer closing and 80% on shelf pick-and-place, though the more dynamic ball toss (30%) and loco-manipulation (40%) remain difficult.

StenoVLA-3D: A 3D-Aware VLA for Navigation Through Gastrointestinal Stenoses

Autonomous navigation through stenotic segments asks a policy to predict actions from texture-poor monocular observations, make safe control decisions, and retain evidence of lesions after they leave the field of view. Existing VLAs lean on visual appearance and short-term context, which limits geometric grounding and episode-level reporting. StenoVLA-3D injects point-maps into a Cosmos-Reason 2 backbone through learned geometry-gated fusion and adds a temporal state branch to model traversal progress; the reasoning-and-action backbone emits grounded reasoning alongside actions, while dedicated heads estimate stenosis shape and generate the final lesion report. The authors also release EndoCausal, an episode-level dataset with lesion annotations, actions, and temporally grounded reasoning. On 40 held-out recorded test episodes StenoVLA-3D reaches 95.2% semantic accuracy and 83.4% action accuracy, and on a physical 3-DoF endoscope it attains 88.9% and 77.8% task success in esophageal and colonic phantoms (36 trials each), substantially ahead of the evaluated baselines.

JAMB: Denoising Bimanual Actions and Future Geometry Together

2026-09-24 general

The hard part of bimanual manipulation is that two arms share one 3D scene: whatever either arm does rewrites the spatial relations the other arm depends on. JAMB's answer is to denoise bimanual actions and future 3D point tracks together inside a single Transformer, so the two hypotheses correct each other at every denoising step — geometry constrains the action, and the action reshapes the predicted geometry — instead of demoting future state to auxiliary supervision or fixed conditioning, as earlier predictive diffusion policies do. Multimodal representations are grounded in a shared spatiotemporal coordinate system so that this cross-modal interaction is geometrically meaningful. Across 16 bimanual tasks in RoboTwin 2.0, JAMB averages 83.4% success, 23.9 percentage points above the strongest baseline, and on a real robot it beats action-only and auxiliary geometry-prediction methods by 50.0 and 21.2 percentage points, with better generalization to cluttered scenes and out-of-distribution backgrounds.

Learning Surgery From the Surgeon's Own Hands: In-Vivo Laparoscopic Appendectomy Without Robot-Collected Demonstrations

Most minimally invasive surgery is still performed with hand-held laparoscopic instruments, and when the operation ends almost all of the surgeon's instrument motion is discarded — only the endoscope video is kept. This paper builds an end-to-end pipeline that recovers that lost motion in the operating room and turns it into a surgical robot policy, validated on live animals. The sensing half is a surgical instrument-state logger that mounts on the shaft of a standard laparoscopic instrument and recovers its pose and jaw state from an inertial sensor, a time-of-flight sensor and a Hall sensor, with no external camera or tracker. A data pipeline then measures the latency of every sensor channel against robot ground truth and aligns the channels before forming observation-action pairs. On those demonstrations the authors train a diffusion policy with a fine-tuned DINOv3 backbone, choosing its design by closed-loop rollouts in a physics simulator reconstructed from depth maps of an ex-vivo rabbit appendix. The policy is retrained on 849 in-vivo demonstrations from four live rabbits and then deployed on four additional live rabbits with electrosurgery armed; with the surgeon selecting the surgical phase, it completed the appendectomy in three of the four animals. Both demonstration corpora are released.

MachEmbodied-U0: One Foundation Model That Both Understands the Task and Predicts How the Scene Will Change

2026-09-24 industrial-arm

General-purpose robot control asks a model to do four things at once: understand what the user wants, work out where to interact, anticipate how the scene will evolve, and produce precise actions. Vision-language-action models supply strong semantic priors but do not explicitly model scene dynamics; world-action models couple visual prediction to control without necessarily exposing the semantic and spatial structure that fine-grained manipulation needs. MachEmbodied-U0 (ME-U0) is a unified embodied foundation model that connects an understanding expert and a generation expert through a Mixture-of-Transformers architecture. Subtask prediction and affordance grounding steer joint visual-dynamics and action generation through flow matching, where the predicted visual dynamics span future RGB, depth, surface normals and optical flow, giving complementary supervision over appearance, geometry and motion. A Multi-rate Rotary Position Encoding scheme aligns those visual dynamics with fine-grained control. ME-U0 is pretrained on roughly 4,200 hours of curated demonstrations drawn from robotic and egocentric datasets, and evaluated with only the supervision each downstream benchmark natively provides: an average score of 17.66 on the RoboDojo simulation benchmark, and average success rates of 99.0 percent on LIBERO and 82.5 percent on LIBERO-Plus. It is additionally validated on real-world manipulation tasks, and shows zero-shot subtask prediction, affordance grounding and visual dynamics on simulated and real observations.

PAKT: Kinesthetic Teaching That Respects What the Robot Can Actually Do

Real-world reinforcement learning still struggles with contact-rich industrial manipulation, where the demands are micrometre-level precision, success rates above 99 percent and human-level cycle times. Off-policy methods can get there by exploiting demonstrations and human interventions, but the bottleneck is a missing interface: never mind the algorithm, how does a person on a factory floor supply guidance that is both intuitive and physically consistent with what the robot and the policy can actually reproduce? PAKT is a framework for kinesthetic teaching in reinforcement learning. Instead of teleoperation it uses kinesthetic guidance, the method industry already relies on, but it fixes that method's classic weakness: an operator can drag the robot along trajectories whose velocities, accelerations and jerk the robot or policy cannot physically reproduce. In PAKT the operator guides the robot through admittance control, which maps human-applied forces to motion, and a downstream reference generator applies the same kinematic limits used during policy execution, so every collected trajectory stays inside what the system can reproduce. Supporting this interface is a high-performance control stack that maps low-frequency RL actions to high-frequency torque commands through a reference generator and an impedance controller, where the reference generator preserves the impedance controller's tracking performance while improving contact handling and producing smoother policy actions. Across reported runs on four insertion and industrial assembly benchmarks, including a data centre compute tray, the end-to-end system cuts cycle time by 23 to 48 percent and cumulative intervention count by 62 to 86 percent relative to the HIL-SERL baseline.

MedVLA: Function-Constrained Execution Lifts Precision Medical Manipulation From Single Digits to 95%

2026-09-24 industrial-arm

Precision medical robotics demands adaptive decision-making under strict safety, interpretability, and execution constraints. Vision-language-action models show strong multimodal reasoning, but their continuous action-generation paradigm fits precision medical tasks poorly, where reliable closed-loop operation may also depend on non-action system function calls. MedVLA addresses this with a hierarchical framework that couples high-level multimodal reasoning to low-level function-constrained execution, paired with a scalable multi-agent pipeline that generates skill-oriented chain-of-thought data for structured training. Built on several multimodal large-model backbones, it improves consistently after fine-tuning. Across 100 closed-loop flexible electrode implantation trials under identical initial conditions, MedVLA reaches a 95.0% task success rate, substantially outperforming representative VLA baselines — OpenVLA at 8% and pi0 at 15% — in accuracy, stability, and safety.

StrataVLA: Layering 3D Geometry Into the VLA Backbone for Up to 88% Fewer Geometry Calls

Vision-language-action models inherit strong semantic priors from large-scale vision-language pretraining but remain limited in robotic manipulation by insufficient 3D spatial awareness. Existing remedies either require explicit depth or point-cloud inputs, compress geometry into training-time supervision, or inject it only at the model input or action expert, so the vision-language backbone never has persistent access to task-relevant spatial information. StrataVLA introduces a plug-and-play framework for hierarchical geometric grounding: a frozen geometry foundation model extracts shared geometric features from RGB observations, and sparse, layer-specific Geometry Adapters let visual representations at selected backbone depths retrieve geometric evidence through cross-attention. Task-aware routing and an LRU feature cache exploit temporal redundancy during manipulation. On LIBERO, SimplerEnv, and real-world manipulation it delivers consistent gains over strong VLA baselines, reaching 98.53% average success on the LIBERO suites while reducing geometry-model invocations by up to 88%.

Silent Sabotage: A Stealthy Backdoor Triggered by a Robot's Own Action History

2026-09-18 industrial-arm

Wiring large language models into robot control systems unlocks complex reasoning and planning, and with it a class of security risks that remain largely unexplored. Existing LLM backdoor research revolves almost entirely around external stimuli — particular words, visual objects, or environmental states. Those attacks are potent, but they overlook a more insidious vulnerability in which the trigger lives inside the agent's own operating logic. This paper presents the first comprehensive study of history-based backdoor attacks on LLM-powered robotic systems: by manipulating its instructions, an attacker can embed a stealthy backdoor in an LLM-based robot controller whose trigger is not an external cue but a specific, rare sequence of the robot's own past actions. The backdoor stays dormant during normal operation and preserves the robot's utility, yet can be activated to induce malicious behavior such as a complete stop or a collision. In simulated experiments spanning a variety of robots and LLMs, the history-based attack achieves a near-perfect success rate while remaining exceptionally difficult to detect.

ε4P: Upcycling Imperfect Data for High-Precision Robotic Manipulation

2026-09-22 general

High-precision manipulation is where vision-language-action policies break down, and the usual remedy is task-specific teleoperation data, which is slow and expensive to collect. ε4P attacks the data problem instead of the model: it upcycles two sources that are normally thrown away, namely low-precision data from the target task and high-precision data from mismatched tasks, and controls where each source contributes along the flow-matching trajectory rather than mixing them uniformly. Low-precision target-task data is applied at high noise to preserve high-level task context, while high-precision mismatched-task data is applied at low noise to transfer low-level action precision. On real-robot sub-millimeter precision tasks and coarse-grained tasks, the method improves policy performance by up to 31.7 percentage points and can replace an equal amount of task-specific high-quality data with an average drop of only 4.2 points.

MATE: Multi-Agent Virtual Teleoperation Platform for Humanoid Collaboration Data Collection

2026-09-22 humanoid

Humanoid robots need embodied experience of collaborative work, but physical multi-robot data collection does not scale: it demands several robots, a dedicated space, and repeated resets. MATE replaces the hardware with a shared physics simulation in which multiple geographically distributed operators simultaneously teleoperate whole-body humanoids, preserving the physically coupled interactions between humanoids, objects, and environments that make collaboration data valuable in the first place. Using MATE the authors build a multi-humanoid collaboration dataset of 24.1 hours across 2,500 joint episodes and five long-horizon tasks, including object handover, relay delivery, environment interaction, and cooperative transport, and introduce EAIS, an execution-aligned interaction sampling strategy that prioritizes task-progressing and interaction-critical behavior. Imitation learning and vision-language-action policies trained on this data learn effectively and transfer zero-shot to a physical humanoid without real-world fine-tuning.

Skytopia: Monocular Drone Navigation with Action-Conditioned Latent World Models

2026-09-22 drone

World models for robot navigation are normally built to be executed: a prediction is produced at deployment and fed back into action generation at every control step. Skytopia argues that this is the wrong contract — what a policy needs from a world model is the representation required to produce a prediction, not the prediction itself, because in flight the executed action already explains most of the change between observations and prediction reduces to reprojecting a static scene under a known displacement. The policy is therefore built on an action-conditioned latent world model, trained on a purpose-built 3D Gaussian Splatting platform, with a forward objective predicting the next observation's representation from intended motion and an inverse objective recovering that motion from the predicted transition. Because the prediction never reaches action generation, the predictor is discarded at deployment, and one policy serves point-goal, image-goal, and goal-free navigation at 57.8, 66.0, and 49.0 percent success while cutting inference cost by 59.4 percent. The same policy flies on a physical drone without fine-tuning in indoor, open outdoor, and woodland settings.

DexTacWAM: A Visuo-Tactile World-Action Model for Dexterous Manipulation

2026-09-21 general

Dexterous manipulation rests on contact dynamics that cameras only partially reveal, yet the current generation of World-Action Models couples predictive video with action generation while remaining vision-centric, so the signal that decides success in contact-rich tasks never enters the predicted world state. DexTacWAM encodes each fingertip independently, aggregates the features through a finger- and pose-aware tactile compressor, and injects the resulting tactile latent into a video diffusion world model so contact evolution becomes part of the imagined future. Across six contact-rich tasks on a 22-DoF bimanual platform it scores highest on every task, averaging 70.6 against 38.0 for the strongest baseline; an ablation that removes tactile world modeling while keeping the same tactile features and action expert collapses the four-task mean from 74.7 to 26.6, locating the gain in the world state rather than in tactile conditioning. Adaptation to touch takes roughly four hours and around 100 demonstrations per task with a frozen pretrained vision VAE.

PredActor: Predictive Action Diffusion for Steerable Onboard Humanoid Control

2026-09-21 humanoid

Diffusion models generate flexible motion, but turning that flexibility into feedback-responsive humanoid control is awkward: hierarchical systems steer through motion references that a separate tracker may not be able to execute, action-only diffusion has no explicit future-state trajectory to steer, and joint state-action diffusion controllers typically depend on privileged full-body state estimates while leaving behavior selection and test-time steering fragmented. PredActor consolidates these capabilities into one directly executed policy driven by proprioceptive observations: conditioned on proprioceptive history and optional task context, it jointly generates executable actions and an internal future-state trajectory, with classifier-free guidance strengthening text-conditioned behavior and classifier guidance steering predicted states toward test-time objectives. Only the actions are executed, with no separate motion-reference tracker and no externally estimated full-body state as input. In simulation it reaches all 15 destination targets with a text retrieval score of 0.580 against 0.373 for conditional action diffusion; rolling denoising and computation-preserving runtime optimizations bring the complete callback to 16.790 ms median and 19.383 ms p95 on a Jetson Orin NX, both inside the 20 ms control period. It is deployed on a Unitree G1.

Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies

2026-09-21 software

Vision-Language-Action models map observations to actions with no objective that accounts for how the world actually responds, so their robustness is bounded by data coverage; world models carry exactly that missing objective but cost seconds per decision and cannot sit in a control loop. This paper argues the two can be separated: physical grounding lives in a world model's internal features, while future generation is merely the objective that produced those features, so the grounding can be inherited and the generative machinery discarded. One feature-alignment term is added to ordinary VLA training, with a frozen world model run once over the training frames and cached; no teacher is loaded during training, the projector is dropped afterwards, and the deployed policy is identical to the undistilled baseline at 32 ms and 1.86 GB on a consumer RTX 5090. A 0.8B student reaches 97.9% on LIBERO, lifts RoboCasa-GR1 humanoid manipulation from 48.2% to 50.5%, and transfers to real single-arm and bimanual hardware.

LIMBO: Learning Safety Into the Policy Instead of Filtering It at Runtime

2026-09-18 humanoid

Safe whole-body control has to hold collision avoidance and balance together under high-dimensional nonlinear dynamics, and safety certificates are typically hand-designed per behavior, then re-derived and re-argued whenever the task changes. LIMBO moves certificate synthesis from design to learning. It learns a state-action control barrier function around a frozen base controller, over residual actions, with a state-based failure specification, which makes Q-CBF synthesis tractable in the full control dimension while placing the certificate in the control space of the task policy. The learned safety value then drives risk-guided sampling near the estimated boundary of recoverability, and later serves as a teacher supplying action-level safety feedback during task learning, so the safety structure is internalized and no online safety filter is needed at deployment. The authors demonstrate the framework on a 29-degree-of-freedom humanoid performing dodgeball avoidance and locomotion beneath low obstacles, with both policies transferring to hardware without online filtering; notably, under a fixed safety specification, varying only the sampling concentration yields strategies ranging from crouching to a novel backward-leaning limbo maneuver.

Packing the Gripper Into the Plan: Closed-Loop Dense Packing of Irregular Objects

Automatic dense packing is what warehouse operations actually want, and it has stayed largely in simulation: prior work treats the object as an isolated rigid body with idealized contact, inserts the gripper as a discrete post-hoc feasibility check if at all, and leaves perception and contact drift during execution unaddressed. This paper delivers a closed-loop pipeline that runs on real hardware, integrating perception, gripper-aware placement optimization, and force-guided execution. The optimizer represents the object together with the gripper as one composite body of hierarchical sphere trees and searches five degrees of freedom on a GPU inside a CMA-ES framework, with the vertical coordinate grounded analytically against the current heightmap. During execution a force-monitored vertical descent stops on first contact, and a post-release consolidation push closes the residual lateral clearance that gripper-aware planning leaves behind, while the container is re-perceived between placements so drift does not accumulate. The system is validated on a Franka Emika Panda packing 3D-printed flat, curved, and concave objects plus a YCB subset, with an ablation isolating gripper-aware optimization, the consolidation push, and mesh-derived geometry against end-to-end success, achieved density, and computational cost, and a benchmark against a heightmap-minimization baseline representative of prior work.

LunaDrive: Pushing a Flat BLDC Motor to a 96 V Bus with GaN FETs

2026-09-18 components

How fast a dynamic robot moves is ultimately capped by the rotational speed limit of its actuators, and most commercially available servo drives assume a bus voltage of 48 V or lower, which puts a hard ceiling on instantaneous maximum speed. The obstacle is a property of the power devices themselves: voltage rating and current capacity trade off against each other in semiconductors, so conventional Si MOSFET drivers struggle to deliver both high voltage and high current. GaN FETs are promising, yet compact drivers that can be mounted on the rear side of a flat BLDC motor remain scarce. This work develops a GaN FET driver for high-power flat BLDC motors and studies the effect of delay compensation in the high-speed region beyond the rated operating range. Under 96 V operation the driver achieves 30 A continuous current with a heat sink, a peak current of 80 A, and a maximum electrical frequency of 3110 Hz, and a high-speed load-lifting experiment powered by a 24S (100 V) LiPo battery demonstrates applicability to dynamic robot operation.

SafeHarness: Making Safety a Planning Priority for Coding Agents on Robots

2026-09-17 software

Coding agents are emerging as a route to robot manipulation: a language model writes the controller as a program, and agents built this way operate robots with no robot-specific training. Whether that route is safe had not been asked. The authors pair each manipulation goal with an obstacle the robot must not touch, and find the agent collides with it in most cases. The agent discusses the obstacle in its traces and the prompt already forbids touching it, so the fault lies neither in perception nor in instruction but in planning, where the constraint never becomes a priority. Decomposing manipulation into a route phase and a contact-rich moment localizes the failure: along the route the model has no notion of a clearing route and no replanning once a route becomes infeasible, and at contact it is unaware that execution is bounded by the same constraint. SafeHarness supplies two obstacle-aware harnesses: route planning grounds objects as bounding boxes and draws candidate waypoint routes over them so the agent plans, verifies and replans before executing, while contact execution selects the contact position so the contact itself avoids the obstacle. The result is 71.9% task success and 87.5% collision avoidance, beating the previous state of the art by 6.5 and 27.0 points and reaching 2.3x and 1.5x the same agent without harnesses.

GeoAAC: Geometry-Based Adaptive Action Chunking for Flow-Matching VLA Policies

2026-09-17 humanoid

Action chunking is standard practice for generating and executing actions in vision-language-action (VLA) policies, yet existing methods use a fixed action horizon. Within a single rollout, different task stages call for different levels of action continuity, control precision and closed-loop feedback, so one fixed horizon cannot accommodate all of them. The authors propose GeoAAC, a geometry-based adaptive action chunking method for flow-based VLA policies that sets the action horizon from the reliability of the current prediction. The observation behind it is that the geometry of flow-matching denoising trajectories carries process-level information about prediction reliability, with geometric variation across action prefixes positively correlated with predictive uncertainty. GeoAAC turns prefix-wise geometry into a horizon-wise geometric profile and determines the action horizon adaptively within a single generation, with no additional training. Experiments with GR00T N1.5 and pi0.5 on LIBERO, LIBERO-Pro, RoboCasa365 and real-world manipulation show consistent gains over fixed-horizon baselines and existing adaptive methods: up to 8.7 percentage points in simulation and real-world average success rising from 53.3% to 74.4%.

Agile-WAM: A Lightweight Tactile World Action Model for Contact-Rich Control

2026-09-17 general

World Action Models (WAMs) go beyond conventional visuomotor policies by jointly predicting future world states and robot actions, letting the policy learn the physical dynamics that support control. Recent tactile WAMs, however, tend to lean on large-scale pretrained generative backbones to capture contact-rich dynamics, which limits inference efficiency and flexible deployment. Agile-WAM addresses this by encoding visual and tactile observations into a shared latent that serves as the source of a direct vision-tactile-to-action flow-matching process, jointly generating latent representations of action chunks and of future visual and tactile latents. The key observation is that vision and touch evolve at inherently different timescales: adjacent visual frames are highly similar, while tactile signals can change abruptly upon contact. Agile-WAM therefore uses multi-horizon multimodal prediction, supervising the visual latent at a larger temporal offset while predicting the tactile latent for the next frame. Across nine simulated and five real-world contact-rich manipulation tasks it outperforms the strongest baseline in success rate while maintaining low inference latency; in five real-world experiments it delivers a 29.4% relative gain in overall success rate at 11.9 ms inference latency.

PointZero: 3D Point Track Completion for Learning Transferable 3D Dynamics

2026-09-16 general

World models let perceptual systems predict how a scene will evolve under interaction, and they benefit most from large and diverse training volumes. Existing methods typically need robot action labels to learn action-conditioned 3D dynamics, which excludes web video from the training pool. This paper studies 3D point track completion as a pretraining objective that learns transferable 3D dynamics without any robot data: given a single RGB-D observation and sparse partial 3D trajectories, the model must predict the future 3D tracks of all observed points. The authors contribute a 2.9-million-frame synthetic dataset spanning deformable, articulated and rigid objects, and train PointZero, an expressive transformer, on it. Post-trained for action-conditioned 3D dynamics, PointZero beats baselines on the recent PGND benchmark; post-trained to predict robot actions and 3D tracks, it outperforms or matches baselines on 6 of 7 simulated and real-world manipulation tasks. Training from scratch isolates architecture gains from those of the objective and dataset, and the dataset, checkpoints and full training recipe are released.

PASSAGE: Scaling Scene-Aligned Motion Learning for Perceptive Humanoid Traversal in Cluttered Environments

2026-09-16 humanoid

PASSAGE is a perception-conditioned planner-tracker framework for humanoid traversal of cluttered environments. The authors collect 100 hours of scene-aligned human motion with virtual reality and inertial motion capture across 1,500 cluttered scenes; a conditional flow-matching planner generates short-horizon references from motion history, a local destination and a robot-centric multi-layer elevation map, and a perceptive whole-body tracker executes them at 50 Hz with geometric feedback. Real-time chunking promotes inter-chunk consistency, and planner-side reinforcement learning on top of the frozen tracker further improves closed-loop performance. The system needs no skill annotations and no obstacle-specific policies: one planner-tracker pair learns to select and compose stepping, squeezing and ducking on geometries it has never seen. Scaling captured data from 6 to 100 hours raises mean contact-free success on held-out scenes from 48.1% to 68.9%, and 70.3% with validated scene augmentation. The fully onboard system integrates egocentric 3D LiDAR, online occupancy mapping, 6.25 Hz planning and 50 Hz control on a Jetson AGX Orin, traversing 50 unseen physical layouts without prebuilt maps or offboard computation.

VLA-ULAP: Interleaving Cloud VLA Calls with Ultra-Lightweight Local Action Prediction at the Edge

2026-09-16 software

Billion-parameter vision-language-action (VLA) policies demand substantial onboard power, while putting inference in the cloud runs into communication delay. VLA-ULAP interleaves remote VLA calls with an Ultra-Lightweight Local Action Predictor (ULAP). At roughly 7.4M parameters including a frozen vision encoder, ULAP fuses the current view, proprioception and executed action history in a single pass to predict action chunks, and trains independently: it needs no VLA hidden states, no online verification and no server round trips. On a Jetson Orin Nano, ULAP takes 19.9 ms and 0.183 J per inference, against 284.3 ms and 50.55 J for GR00T on an RTX A6000. Across three simulated base-policy/benchmark pairs, selected operating points remove 48.8-76.7% of VLA calls while retaining 95.0-97.5% of baseline success. On physical SO-101 experiments the system retains 95.2-100% of baseline success across seen and held-out placements, cutting estimated inference time by 47.9-58.0% and inference-device energy by 52.1-62.5%. In latency-aware LIBERO-Safety simulation it also exceeds pi-0.5 by 11.0 and 15.5 percentage points on two dynamic tasks.

ModAR: Modality-Autoregressive World-Action Models

2026-09-15 general

World-action models (WAMs) jointly model future observations and actions, and they almost always represent the predicted future as RGB images; yet depth maps, pretrained visual features and point tracks capture geometric, semantic and motion information far more efficiently. How best to combine these modalities inside a WAM has remained open. ModAR is the first WAM to autoregressively denoise multiple future modalities before predicting actions, so every prediction conditions on the modalities generated so far. Trained from scratch, it systematically studies training-data mixtures, which modalities to predict, and WAM formulations. The findings are that predicting point tracks, DINO features and depth maps helps, while additionally predicting future RGB gives no consistent benefit. ModAR's sequential generation attains the highest average success rate at every evaluated data scale, and when a video-model-initialized WAM, Flex-pi, is fine-tuned on the same data, ModAR reaches a slightly higher observed average success rate (75% vs 72%) using roughly 20x fewer training FLOPs and no pretraining. On three real-world bimanual tasks it beats baselines and improves further with human videos.

XPACE: Joint World and Action Modeling from Heterogeneous Experience

2026-09-15 humanoid

XPACE is a unified embodied world model that serves at once as a world action model, jointly predicting executable robot actions and future video, and as a world simulator that predicts the visual consequences of prescribed actions. A shared video backbone lets action-unlabeled video teach visual dynamics while action-labeled human and robot demonstrations jointly teach video and action prediction. A coarse-to-fine curriculum progressively shifts emphasis toward robot control while retaining human experience, so the policy acquires behaviors absent from robot demonstrations. The model then adapts its simulator to its own generated context, synthesizes deviation-recovery trajectories around expert demonstrations, and fine-tunes the policy on filtered recovery data. On XPENG's IRON humanoid, heterogeneous training improves robustness and transfers human-observed skills, and self-generated recovery data raises real-world task completion.

TIO-Former: Ultra-Lightweight 6-Directional ToF-Inertial Odometry for Nano-UAVs

2026-09-15 drone

Autonomous nano-UAV navigation demands accurate ego-motion estimation under severe size, weight, power and computing constraints, yet cameras and LiDAR exceed payload limits, optical flow degrades in low-texture scenes, and inertial-only estimation drifts. Multi-zone time-of-flight arrays are a lightweight metric complement, but recovering 6-DoF motion from only 384 ranges per frame must contend with invalid returns, anisotropic observability and computation that scales with flight time. TIO-Former is a camera-free, optical-flow-free and mapless range-inertial odometry framework driven by an IMU and a 15 g payload of six orthogonal 8x8 ToF arrays. Its frontend pairs consecutive range grids with a bilateral gated difference, while IMU-guided cross-attention routes directional features conditioned on platform kinematics; a streaming causal transformer couples an uncompressed local KV cache with compressed Chunk-FIFO memory so inference cost and memory stay bounded regardless of flight duration. In real flights it cuts open-loop position error by 54.4% versus nano-UAV optical flow and by 66.4%-89.1% versus learned inertial baselines, running on an edge RISC-V companion at 10.466 ms P95 latency and 6.324 MiB peak resident memory. Code is released.

Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence

2026-09-14 software

Pelican-Sim 1.0 is a general world model simulator for embodied intelligence: it predicts future observations from visual context and robot actions to support downstream learning and decision making. Four design choices carry the system — a 28-dimensional action value space covering most mainstream embodiments so that one model stays valid across heterogeneous devices, action-visual injection that bridges actions and pixels with URDF- and camera-rendered action videos, sparse mixture-of-experts layers that absorb the action modality while reducing inter-modality conflict, and a four-step autoregressive simulator produced by causal adaptation and few-step distillation. Trained on roughly one million real-world and simulated trajectories, it improves video quality and action controllability substantially; in four downstream RoboTwin applications, adding 500 generated trajectories per task to 50 demonstrations raises policy success from 70% to 93%.

Size Doesn't Matter: Material-State Reinforcement Learning for Transferable Excavator Soil Manipulation

2026-09-14 industrial-arm

The paper casts autonomous earthmoving as material-state-conditioned reinforcement learning, training policies inside a GPU-parallel Material Point Method particle simulation so the controller can condition on soil shape and compactness, use multiple contact faces of the bucket, and displace material both into and out of the shovel. Policies are learned in a normalized end-effector space and deployed through a calibrated machine interface, so the same learned weights drive an 11.5 t hydraulic excavator and a 500 g tabletop robot. In the field the system autonomously built a 42 m long, 2.1 m high embankment, completing 201 consecutive policy strokes in 45 minutes with no failures, retries, or operator intervention, matching an expert operator's pace.

Dynin-Robotics: An Omnimodal Unified Diffusion Vision-Language-Action Model

2026-09-14 general

The paper brings visual goal prediction and dynamics prediction into the generation and selection of actions by sharing a single trajectory model between them. It is built on Dynin-Omni, an omnimodal masked-diffusion backbone that represents language, visual observations, goals, and actions as discrete tokens. By varying only the conditioning and target spans, the same model learns action prediction, action-conditioned next-observation prediction, terminal goal-state prediction, and trajectory-to-instruction reconstruction. Those interfaces support test-time scaling through goal prediction, action-candidate evaluation, and joint refinement of action and future-state predictions. The authors continually pretrain on roughly 1.33 million trajectories from 48 Open X-Embodiment datasets and reach a 78.4% average success rate across four manipulation conditions on a Franka Research 3 robot.

EVPeriscope: Event-Camera Propeller Tracking Gives Air and Ground Robots Mutual Perception

2026-09-10 drone

EVPeriscope equips a ground robot with an upward-facing event camera that picks up the high-frequency visual signature of a quadrotor's propellers, enabling detection, localization, and closed-loop control of the aerial vehicle without fiducial markers and without the motion-blur and lighting sensitivity of frame-based cameras. The quadrotor can therefore act as an extended perception system for the ground robot when its own sensors degrade or are occluded, forming a marsupial air-ground team. The authors run field experiments in winds up to 15 mph, in daylight and at night, and demonstrate localization and closed-loop navigation through dense foliage where the ground robot's sensors are blocked; quadrotor control runs at 200 Hz entirely on onboard sensing and computation.

In-Hand Pen Writing for an Anthropomorphic Hand, Learned in Real Time Without Demonstrations

2026-09-10 humanoid

The authors present an embodied control approach that estimates the task Jacobian of the combined hand-and-object system in real time on the physical robot. Using only a laptop CPU, the controller begins in-hand pen writing after roughly 18 seconds of initialization and keeps adapting online, with no analytic hand-object kinematic or contact model, no simulation training, and no precollected demonstrations. The same estimator/controller formulation works on three anthropomorphic hands (one physical, two simulated), demonstrating an embodiment-independent method. On the physical robot it reaches sub-millimeter in-plane precision (mean 0.6 mm across runs) for letters and shapes written both in the air and on paper, which the authors describe as the first demonstration of an anthropomorphic hand writing arbitrary single-stroke trajectories with a grasped pen through purely in-hand motion.

ActSafeGuard: Training-Aligned Hard-Constraint Enforcement for Flow-Matching Robot Policies

2026-09-10 software

Vision-language-action models and world-action models generate capable manipulation behavior, but their actions can violate hard physical constraints and therefore be unsafe or infeasible to deploy. Existing safety approaches either optimize statistical safety objectives without deterministic per-step guarantees, or correct unsafe actions only at inference time, creating a mismatch between policy training and execution. ActSafeGuard is a differentiable, training-aligned safeguard layer for flow-matching policies that integrates hard action feasibility into policy learning rather than treating safety as an inference-time add-on. An analytic ray-scaling operator yields boundary-aware gradients that guide the model to learn constrained manifolds. Across multiple standard foundation backbones (π0.5 and Fast-WAM) and a range of tasks, it consistently achieves a 100 percent step safety rate while fully preserving or even boosting task success.

Show-Harness: Just a VLM Agent Can Play Robots

Show-Harness is an embodied harness that converts the world knowledge of foundation vision-language models into robot control through a compact semantic interface. The VLM reasons over discrete semantic action units, and embodiment-specific interpreters deterministically ground those decisions into local robot actions, keeping the VLM directly responsible for fine-grained physical decisions. The same interface directly unlocks closed-source frontier VLMs for zero-shot robot control and adapts small-scale open-source VLMs for low-cost deployment with only a few GPU-hours of fine-tuning. Its companion GUMI (GUI Manipulation Interface) extends the same semantic action space to GUI-based demonstration collection, so humans and agents can play robots across embodiments without specialized teleoperation hardware. Extensive experiments show that Show-Harness-equipped VLM agents generalize robustly across tasks, embodiments, and environments, outperforming representative agentic and VLA paradigms.

Frequency-Conditioned Flow Matching for Vision-Language-Action Models

Robot actions are temporally correlated trajectories whose frequency components encode motion at different scales with highly non-uniform energy distributions, yet flow-matching vision-language-action models typically generate actions in temporal coordinates without explicitly modeling or systematically exploiting this frequency heterogeneity. FreqFM raises action frequency from an implicit trajectory property to an explicit conditioning dimension that spans the entire generation pipeline. Concretely, in DCT frequency coordinates it constructs a spectrum-matched source distribution, adaptively balances the objective across frequencies, and constrains per-frequency guidance residuals using the corresponding reference transport scales. FreqFM integrates into existing flow-matching action experts without changing the VLA backbone, improving performance consistently across LIBERO, LIBERO-Plus, and VLA-Arena, including a 9.3-point gain on LIBERO-Plus, and further demonstrating effectiveness on six real-robot tasks.

A Traffic Management System for Large and Heterogeneous Vehicles in Narrow Industrial Environments

2026-09-09 agv

Coordinating automated guided vehicles (AGVs) in high-density industrial environments is a critical challenge within Logistics 4.0, where traditional negotiation-based priority assignment in traffic management often causes inefficiency. This paper presents an AGV traffic management system based on lifelong multi-agent path finding (L-MAPF) operating on roadmaps generated with non-uniform rational B-spline (NURBS) curves, guaranteeing locally optimal coordination and safe operation of large, heterogeneous AGVs. The framework integrates a modified bounded-horizon conflict-based search (CBS) within a rolling-horizon conflict resolution strategy, using an extended time horizon per agent to resolve conflicts effectively in corridors identified by a topological map. Unlike state-of-the-art AGV fleet traffic management, the solution targets real-world, non-standardized (non-grid-like) industrial settings with narrow bidirectional corridors and high traffic density, where AGVs of various sizes and capabilities operate simultaneously. Key contributions include an anytime conflict resolution strategy with adaptive time-horizon regulation, an execution layer for safe and standard-compliant interaction with real AGVs, and an advanced deadlock detection and resolution mechanism. Experiments in realistic industrial environments show throughput improvements of up to 11% over a conventional rule-based traffic management system, a state-of-the-art industrial method, and a priority-based L-MAPF variant, while maintaining continuous operation.

Learning Terrain-Adaptive Humanoid Locomotion on Granular Terrain

2026-09-09 humanoid

This work builds a physics-grounded granular contact model from three-dimensional resistive force theory (3D RFT) and simulates granular terrain efficiently for reinforcement learning, replacing rigid or heuristic contact solvers with physically accurate intrusion dynamics that capture penetration and tangential drag. A terrain-adaptive controller is trained via teacher-student RL, with a variational autoencoder compressing terrain information into a compact latent for zero-shot terrain identification and adaptation. MPM/NVIDIA Newton studies show generalization to unseen granular terrains and higher success rates than baselines, and hardware experiments on basalt, dry sand, and beach sand deliver what the authors state is the first demonstration of agile humanoid locomotion on real-world granular terrain.

FolDeX: A Physical-World Benchmark for Long-Horizon Robotic Manipulation of Deformable Objects

FolDeX is a physical-world benchmark built entirely from real-robot data, centered on garment folding, that targets long-horizon deformable-object manipulation where policies must track changing states and execute reliable multi-stage bimanual interactions. Because real-robot data is costly, it studies efficient reuse of heterogeneous physical experience along four axes: human intervention and recovery data collected during deployment; transfer across tasks including garment categories and rigid-to-deformable manipulation; reuse across scenes with changing lighting, background, and layout; and transfer across embodiments. The benchmark provides 2,000+ hours of real-robot data spanning 20+ tasks and 10+ embodiments, plus a fair evaluation platform for externally submitted policies with standardized tasks, held-out physical objects, controlled initializations, and a unified execution protocol.

GTA-2: A Multi-VLM Framework for Synthesizing Robot Manipulation Skills via Grounded Task Axes

2026-09-05 industrial-arm

GTA-2 is a modular multi-VLM framework that constructs executable, task-bespoke manipulation skills from reusable object-centric task-axis components rather than predicting actions end-to-end or composing fixed primitives. Each skill is represented as semantic subtasks comprising task-relevant keypoints and axes, controller compositions, and scene-dependent parameters. Four specialized VLM agents separately decompose the task, construct an abstract task-axis skill, assign controller parameters, and ground required visual features from RGB-D observations, enabling zero-shot skill generation with no task-specific demonstrations, policy training, or fine-tuning. On 14 real-robot manipulation tasks it achieves 73.9% average zero-shot success, exceeding the strongest baseline by 31.4 percentage points, and targeted human feedback refines an incorrect stage to raise the average to 90.7%.

TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model

2026-09-08 humanoid

TANGO is the first whole-body vision-language navigation framework for humanoid robots in cluttered indoor environments. Unlike 2D path-planning approaches, it treats traversal as a continuous geometry-aware whole-body problem: given a natural-language instruction and egocentric RGB observations, TANGO directly predicts 29-DoF joint-space actions. It is trained entirely in simulation using synthesized collision-free traversal behaviors built from global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. TANGO achieves state-of-the-art vision-language navigation results in simulation, outperforming strong modular baselines in obstacle-negotiation scenes, and deploys zero-shot on a Unitree G1 humanoid in cluttered real-world scenes without any real-world navigation training data.

Proxy Policy Steering

2026-09-08 software

Proxy Policy Steering (PPS) adapts a frozen generalist robot policy to a new task at inference time. Instead of modifying the base policy, PPS trains two lightweight proxy policies — a reference proxy that models the frozen base's behavior on target-task observations and a task proxy capturing how behavior changes under task supervision — and uses their calibrated velocity-space difference to steer the base sampler at every denoising step. Because the base is never modified, its broad capabilities remain intact. On 8 real-world and 4 simulation manipulation tasks, PPS lifts the state-of-the-art pi0.5 base policy by 53% absolute success rate on average, including zero-to-one gains on tasks the base never solved, outperforming LoRA fine-tuning, from-scratch specialists, residual policies, and prior inference-time steering methods.

Ostrich: Taking Large Strides Through Stiff Contact in Differentiable Dynamics

2026-09-08 software

Ostrich is a GPU-accelerated differentiable rigid-body simulator that resolves hard contacts and friction with non-smooth Newton iteration at large timesteps (~0.1 s) and differentiates the converged residual via the implicit function theorem, reusing the forward Schur complement to compute the adjoint at O(1) memory per timestep. On real-robot trajectories over a pallet obstacle, Ostrich holds MuJoCo's sim-to-real accuracy at up to 50x larger timesteps. Its gradients converge from random initializations where MJX descends slowly and Newton Semi-Implicit stalls; a warm iteration runs 211x faster than MJX and 4.7x faster than Semi-Implicit. Ostrich differentiates 8,192 parallel worlds on a single 24 GB GPU, sustaining 29x checkpointed MJX's optimization throughput. It closes with gradient-based trajectory optimization over triangle-mesh terrain across a 10 s horizon.

Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation

2026-09-06 amr

COSTER is a framework for generating safety-critical traffic scenarios by leveraging learned traffic priors to determine plausible collision times and locations. It constructs a collision snapshot by inserting a new vehicle in contact with the target vehicle at an identified collision state, then uses a conditional variational autoencoder to perform a time-reversed rollout that reconstructs the inserted vehicle's trajectory backward toward earlier timesteps. Experiments show COSTER outperforms existing methods in plausibility, diversity, and data efficiency; agents trained on COSTER-generated scenarios reduce collision rates by 31% on safety-critical scenarios from the Waymo Open Motion Dataset while also improving ego task completion.

GloVLA: Let Geometry Move and Local VLA Interact for Robust Object-Centric Manipulation in Unstructured Environments

GloVLA is a hybrid framework that separates object-centric manipulation into two complementary regimes: a geometric transport controller moves the end-effector into interaction-centric handoff regions, while local Vision-Language-Action (VLA) policies handle only the short-horizon, contact-rich interaction phases. It is model-agnostic, integrates with different VLA backbones without additional demonstrations, and requires no changes to the action space or success predicate. Under clutter, distractors, illumination changes, visual shifts, and obstruction, full-trajectory GR00T N1.6 execution degrades to 20.9% average success while GloVLA retains 88.5%. On a physical UR10e, overall success improves from 35.6% to 90.0% while mean inference time is more than halved.

GLoRI: Closed-Loop Whole-Body Tracking with Global-Local Reference Interaction for Humanoid Loco-Manipulation

2026-09-05 humanoid

GLoRI is a closed-loop whole-body controller for humanoid loco-manipulation that integrates structured global reference and feedback with local motion guidance. Its GLoRI-Net uses Global-Local Cross Attention (GLCA) to refine local keypoint features with global target and pose-difference features, preserving motion structure while correcting world-frame placement. GLoRI achieves 100% completion and a g-MPJPE of 6.44 cm on held-out HuMoTo motions, and this accuracy remains robust under direct Isaac Gym-to-MuJoCo transfer without fine-tuning. The accuracy and generalization enable autonomous loco-manipulation with a single policy on a real Unitree G1 interacting with diverse unseen objects.

Same Trajectory, Contradictory Rewards (ROBORMBENCH): Paraphrase Fragility in Vision Language Reward Models

ROBORMBENCH measures a neglected failure mode in VLM-based reward models for robot learning: paraphrase fragility. Using 2,390 real-robot trajectories with ground-truth progress labels and 21,673 verified paraphrases (lexical, syntactic, and action-goal rewrites), the authors show that merely rewording an instruction can substantially change predicted reward and even flip identical behavior between success and failure. The instability is widespread across proprietary and open-source VLMs, grows with more divergent rewrites, and is not reliably fixed by model scale or explicit reasoning; dedicated reward models trained with trajectory-grounded supervision are markedly more stable. The work establishes paraphrase robustness as a core requirement for reliable VLM reward modeling.

What Matters, When? Diagnosing and Improving Conditional Visual Grounding in Visuomotor Imitation Policies

This paper diagnoses why visuomotor imitation policies fail when visually similar distractors appear, framing the problem as conditional visual grounding: the visual target needed for control changes with the manipulation phase and, in complex tasks, with the observed task state. Using Action Chunking with Transformers (ACT), the authors systematically introduce distractors with controlled color and shape similarity and localize failures to picking and placement stages, finding sensitivity depends on both similarity type and stage. Guided by the diagnosis, they combine distractor augmentation, phase-dependent attention regularization, and appearance-based visual prompting, substantially improving robustness in simulation and on a physical UR3e, and show the same failure pattern in a pretrained vision-language-action policy on a state-conditioned instrument-handling task.

FIRE-LIVWO: Robust LiDAR-Inertial-Visual-Wheel Odometry via Failure-Immune mmWave Radar Enhancement

FIRE-LIVWO is a failure-immune multi-modal odometry framework for large-scale underground coal mines. Built on an iterated error-state Kalman filter (IESKF), it tightly couples 4D mmWave radar, LiDAR, visual features, and wheel odometry inside a unified VoxelMap, jointly constructing LiDAR-radar point-to-plane residuals and sparse visual photometric residuals. In smoke-filled environments it exploits mmWave radar penetration with pointwise Doppler velocity constraints to preserve observability; in geometrically degenerate corridors it couples wheel odometry via non-holonomic constraints with online lever-arm compensation. Its central contribution is a degeneration-detection and adaptive fusion-model switching strategy grounded in geometric and visual observability analysis that quantifies observability online and dynamically adjusts modality weights. Real-world underground coal-mine experiments show reliable failure-boundary identification and modality switching with a 5.677 m average localization error, outperforming baselines; code is open-sourced.

RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?