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Oct 19Mon
  1. arXiv cs.RO55

    Diagnosing Compositional Generalization in Sequential Robot Tasks

    This paper studies compositional generalization in sequential robot manipulation tasks from the perspective of instruction-space coverage, decomposing the generalization gap into three sources: marginal instruction shift, instruction-compositional shift, and context-action shift. Results show that exhaustive instruction-tuple enumeration is unnecessary; a structured subset covering action-relevant dependencies can achieve strong out-of-distribution performance. Sparse-training failures often stem from instruction steering rather than missing low-level skills, and fine-tuning with just one demonstration per task raises out-of-distribution success from 0.4% to 54.7%.

  2. arXiv cs.RO68

    PRISM: Improving Robot Motor Control Policy Learning with Polynomial Representations

    PRISM (Polynomial Representations for Interaction Structured Motor Control) is a physics-inspired policy representation method that learns multiplicative interactions among variables by recursively adding scaled bilinear features. It only requires replacing the first affine layer of a multilayer perceptron (MLP) and can be used for reinforcement learning and imitation learning without changing training objectives, sensing, or controllers.

  3. arXiv cs.RO55

    FELT: Generating Tactile Signals from Vision for Visuo-Tactile Manipulation

    FELT is a framework that uses existing paired visuo-tactile data to learn contact-aware representations from RGB observations, providing them to downstream policies through two interfaces: generated tactile images and contact-aware features. It combines a frozen visual encoder with separate query decoders for the left and right fingers, and the two branches exchange information through gated connections, with a convolutional readout producing spatial contact representations. On four contact-intensive real-robot tasks, both interfaces achieved higher success rates than vision-only policies, and directly integrating contact-aware features into policies raised success rates by 15-22.5 percentage points.

  4. arXiv cs.RO62

    OptCar: Specializing a Generalist FKD Model into a High-Speed MPC-Specific Model with Limited Real-Vehicle Data

    The paper proposes OptCar, a method that specializes a generalist forward kinodynamic (FKD) model into a vehicle-specific model while preserving cross-terrain generalization. At its core, a FiLM mechanism summarizes recent state-action history into a single dynamics context token that conditions multi-step predictions in a Transformer FKD architecture; specialization then combines limited real-vehicle data with targeted synthetic trajectories generated by system identification.

  5. arXiv cs.RO48

    EDAR: Learning Environment-Dependent Action Representations for Robotic Manipulation

    EDAR (Environment-Dependent Action Representation) anchors action tokens in both executable control structure and expected visual consequences. By coupling motor commands with their environment-conditioned effects, it enables the learned action space to capture interaction semantics rather than merely command-level patterns.

  6. arXiv cs.RO62

    NativeMEM: A Native Memory Compression Method for Long-Horizon Robotic Manipulation

    NativeMEM is a vision-language-action (VLA) policy with long-horizon, real-time updated memory. Its core is the Native Memory Compression encoding scheme, which reuses the VLA's own vision encoder to compress each historical frame from each camera view into a single token. Appended to the input sequence, these tokens let the pretrained VLA attend to long-term history with negligible latency overhead, requiring neither an external planner nor a newly initialized memory module.

  7. arXiv cs.RO55

    SPACE: Adaptive Collision-Aware Swarm Exploration Based on Pheromone Fields

    SPACE is a swarm exploration method inspired by ant foraging. On a synchronized shared map, it maintains an attractive frontier pheromone, a repellent exploration pheromone, and a fast robot-density field, providing local field guidance for robots. The authors evaluated it on sixteen home layouts from HouseExpo and eight campus floors from KTH, using up to two hundred fifty-six robots. At congested swarm sizes, SPACE achieved the lowest inter-robot contact rate on HouseExpo and lay on the empirical Pareto frontier on KTH.

  8. arXiv cs.RO40

    PinNet: Keypoint-Aware Local Descriptors with Geometric Embedding for LiDAR SLAM Loop Closure Detection

    PinNet generates point cloud keypoints and their descriptors, and uses a plane-based geometric self-attention module to model spatial relationships between keypoints, thereby improving descriptor discriminability for loop closure detection and point cloud registration. The method was evaluated on datasets collected with various different LiDAR sensors; experimental results show strong place recognition performance, precise relative pose estimation, and successful single-shot localization in different environments.

  9. arXiv cs.RO55

    Research on Asynchronous Upper-Body Task-Space Trajectory Tracking Strategies for Humanoid Robots

    The paper proposes an asynchronous upper-body task-space trajectory tracking framework for humanoid robots, addressing the temporal asynchrony between sparse, low-frequency trajectories output by high-level planners and the high-frequency operation of whole-body controllers. The student policy is initialized through teacher-student distillation, conditioned on the fully cached future trajectory and an execution-time index, and trained with a sliding-window global reward to reduce frame drift; in the task post-training stage, an MPC module completes sparse references into floating-base and upper-body guidance.

  10. arXiv cs.RO59

    WireCraft: A Simulation Benchmark for Industrial Deformable Linear Object Manipulation

    WireCraft is a simulation benchmark for industrial deformable linear object (DLO) manipulation, covering three task families: connector insertion, clip routing, and channel seating. It supports two physics models, articulated and deformable, and the released trajectories come from both simulation and a real UR5 robotic arm.

  11. arXiv cs.RO55

    SidewalkBench: A benchmark for visual navigation on urban sidewalks

    SidewalkBench is a benchmark for visual navigation on urban sidewalks built on NVIDIA Isaac Sim. It provides GPU-accelerated, high-fidelity sidewalk environments, including procedurally generated and real-world scanned scenes, and adds event-driven pedestrian reactive behaviors. The authors evaluated 9 visual navigation models on 330 unit-test scenarios, 800 pedestrian-reactive scenarios, and 105 long-horizon scenarios. They found that pedestrian interaction and long-horizon robustness remain key bottlenecks for existing models, and that scaling up sidewalk training with synthetic data is a viable direction.

  12. arXiv cs.RO56

    CoRL 2026 paper proposes language feedback policy that can steer frozen VLAs harmlessly

    The paper proposes a framework that interactively searches for language sequences that improve closed-loop VLA task performance, distills them into a test-time language feedback policy (LFP), and trains an improvement head to predict when language steering will be effective, then applies conformal calibration to the improvement head to prevent harmful interventions.

  13. arXiv cs.RO55

    Perceptive BFM: A Perception Control Framework for Adapting Human Motion Priors to Robot Terrain

    The paper proposes Perceptive Behavior Foundation Model (Perceptive BFM), a terrain-aware whole-body control framework for humanoid robots that uses local terrain observations from the robot's perspective to adapt contact, posture, and timing while retaining the original kinematic reference as the behavioral interface.

  15. arXiv cs.RO58

    OrbiSim: Recasting World Models as a Differentiable Physics Engine for Embodied Intelligence

    OrbiSim proposes a robot simulation paradigm that recasts world models as a fully differentiable physics engine, connecting structured scene assets, neural dynamics, and downstream reinforcement learning. The entire simulation loop, from explicit state transitions to visual observation generation, supports end-to-end differentiability, enabling tasks such as differentiable contact modeling, gradient-based policy optimization under sparse rewards, and physical reasoning. Experiments show that OrbiSim significantly outperforms existing world models in prediction fidelity and control performance, and responds consistently to asset configurations and physical parameters.

  16. arXiv cs.RO46

    Learning Cross-Coupled and Regime-Dependent Dynamics for Aerial Manipulation

    A study proposes a structured encoder-decoder framework in which a nonlinear history encoder captures the temporal and cross-coupled residual dynamics of an aerial manipulation platform and its robotic arm, while a linear latent-space decoder supports rapid adaptation at deployment time. The framework's linear-in-parameters structure enables closed-form Bayesian updates and integration with real-time model predictive control (MPC). Experiments on a real aerial manipulation platform showed improved residual prediction accuracy, rapid adaptation after dynamics changes, and reduced MPC tracking error under changing payload conditions.

  18. arXiv cs.RO62

    PanoAir: A Panoramic Visual-Inertial SLAM Framework for UAVs with a Cross-Time Real-World Dataset

    PanoAir proposes a panoramic visual-inertial SLAM (VI-SLAM) framework and builds a real-world panoramic dataset covering different illumination, altitudes, trajectory lengths, and motion dynamics. The method uses panoramic feature extraction and panoramic loop closure to strengthen feature constraints and global consistency. On its own dataset and public benchmarks, it completed all 17 sequences without tracking failure and reduced ATE by up to 47.1% compared with the best baseline. Deployment on an embedded platform verified computational efficiency comparable to the PC implementation. The source code and dataset are publicly available.

  19. arXiv cs.RO62

    OxyGen: Unified KV Cache Management for Multi-Task Parallel VLA Inference

    OxyGen proposes an inference design for unified KV cache management that treats the KV cache as a shared resource across tasks and control cycles, eliminating redundant prefill of shared observations through cross-task KV sharing and advancing unfinished language requests within each frame's decoding budget via cross-frame continuous batching.

  20. arXiv cs.RO44

    Gaussian Process-Based Robot Skill Learning and Adaptation Framework

    A study published in IEEE RA-L 2026 and presented at IROS 2026 proposes a robot skill adaptation framework based on Gaussian processes (GPs) and sparse via-points, which preserves the kinematic characteristics of skills by using first- and second-order analytical derivatives of trajectory poses. The framework includes three adaptation methods: an optimization agent, a behavior cloning agent, and a reinforcement learning (RL) agent. In simulation and real-world evaluations on three tasks—opening a drawer, pushing a cube, and manipulating a horizontal bar—it outperformed all baseline methods in success rate and demonstrated high cosine similarity and low velocity magnitude error.

  21. arXiv cs.RO62

    HydroShear: Fluid-elastic shear simulation for tactile sim-to-real reinforcement learning

    HydroShear is a non-holonomic fluid-elastic tactile simulator accepted to RSS 2026, addressing tactile sim-to-real policy transfer for contact-intensive tasks. It models stick-slip transitions, path-dependent force and shear accumulation, and full SE(3) object-sensor interaction, and uses signed distance functions (SDFs) to track the displacement of indenter surface points as they interact with the sensor membrane, generating physically grounded and computationally efficient force fields from arbitrary watertight geometries.

  22. arXiv cs.RO50

    TRAIN: A reinforcement learning framework for terrain-aware interactive navigation for quadruped robots

    TRAIN proposes a two-stage deep reinforcement learning framework that, through the TRAIN-Loco and TRAIN-Nav pipelines, enables quadruped robots to achieve terrain-aware navigation in unstructured terrain and human-populated environments. Benchmarks compared quadruped locomotion and robot navigation baselines, and hardware experiments verified its sim-to-real transfer potential.

  24. arXiv cs.RO55

    Study on confidence calibration for VLA models proposes two lightweight correction techniques

    This study presents the first systematic study of confidence calibration for vision-language-action (VLA) foundation models, establishes a baseline for VLA confidence estimation, examines the relationship between task success rate and calibration error as well as how calibration changes over time, and proposes two lightweight techniques, prompt ensembles and action-wise Platt scaling, to correct the observed miscalibration. The paper is 34 pages long with 7 tables and 22 figures, and has been published in TMLR as a final version.

  25. arXiv cs.RO38

    Multi-Agent Reinforcement Learning-Based UAV Path Planning for Obstacle Avoidance in Stochastic Environments

    This study proposes a centralized training with decentralized execution method based on multi-agent reinforcement learning for UAV obstacle-avoidance path planning in stochastic environments, improved from the model predictive control (MPC) idea. Each agent communicates only with the centralized planner, plans feasible paths through extended observations based on a distance-weighted mean field approach, and uses multi-step value convergence to improve training efficiency. Comparative, ablation, and real-machine experiments verified the method's effectiveness and generalization performance.

  28. arXiv cs.RO71

    Self-Building Labs: An automated system that builds and runs laboratories according to scientific goals

    The authors propose the self-building laboratory (SBL) system, which can design, configure, and operate laboratory setups based on high-level scientific goals. Starting from an empty room or an existing self-driving laboratory, it fills in the laboratory layout, digital twin, robot skills, and experimental workflows. The system was demonstrated on a variety of robots and setups, and can scale from a single workstation to a laboratory with multiple robots working together. Its end-to-end capabilities were validated through a real-world quantum-dot synthesis experiment. The authors say this shifts the role of human researchers from manually configuring and programming device automation to specifying scientific goals.

  29. arXiv cs.RO44

    Structural Affinity: A Regularization Algorithm for Cage-Based Shape Design

    Structural Affinity is an explicit regularization method for cage-based deformation (CBD). It uses a two-cage structure and an affinity matrix precomputed with mean-value coordinates to couple the motion of inner and outer control cages, so that control handle displacements propagate smoothly to neighboring handles, greatly reducing and often eliminating cage self-intersection and mesh distortion, while adding only linear overhead during deformation.

  31. arXiv cs.RO59

    EmbodiedMidtrain: Bridging the gap between vision-language models and VLAs via mid-training

    The paper proposes EmbodiedMidtrain, which bridges the gap between vision-language models (VLMs) and vision-language-action models (VLAs) through mid-training. The authors first characterize the data distribution gap between the two, finding that VLA data occupy compact regions largely separated from the VLM distribution. They then build a mid-training data engine that uses a lightweight learnable proximity estimator to select the candidates most aligned with VLA from a large VLM pool, producing the EmbodiedMidtrain-1M dataset for mid-training VLMs.

  32. arXiv cs.RO70

    Blind assembly strategy: peg-in-hole assembly of diverse parts using proprioception alone

    This study trains blind assembly policies that observe only joint positions, velocities, and a short history of their own actions, without using visual, force, torque, or tactile sensing, and uses reinforcement learning in simulation to complete peg insertion, gear meshing, and nut tightening. The peg insertion policy transfers zero-shot to the real world and successfully inserts pegs of different shapes and sizes in 73% of trials; controlled experiments show that the blind policy generalizes better to unseen part shapes and sizes but may be slower.

  34. arXiv cs.RO58

    PRIME: Learning Precise Progress Estimation from Imprecise Demonstrations

    The paper proposes PRIME, a progress estimator for generating dense reward signals in reinforcement learning for robotic manipulation, learning precise task progress estimation from imprecise demonstration data. PRIME builds a weighted graph over observed states, connecting states across trajectories based on spatial proximity and visual consistency; edge weights encode spatial distance as transition cost, and shortest-path search approximates the geodesic distance to success, whose changes can distinguish progress, stagnation, and regression without assuming that progress is monotonic over time.

  35. arXiv cs.RO58

    Learning Robot Metareasoning: Adaptive Computation Budgets for Real-Time Control

    The arXiv paper Learning Robot Metareasoning proposes modeling the determination of the optimal computation budget as a semi-Markov decision process (SMDP), in which a single decision jointly determines the robot's action commitment duration and the time allocated to the solver. The authors trained a reinforcement learning policy conditioned on the current state, the executing plan, and low-cost planner statistics. Across three task types—ground robot navigation, agile quadrotor flight, and contact-rich manipulation—the policy adjusts its budget according to scene demands and outperforms fixed-budget baselines.

  36. arXiv cs.RO46

    TransNav: Transformer-Based End-to-End Static and Dynamic Obstacle Avoidance for Quadrotors

    TransNav proposes a Transformer-based end-to-end navigation method for quadrotors that achieves obstacle avoidance in environments with mixed static and dynamic obstacles. The method uses a patch-based point-cloud encoding scheme, integrates perception and navigation into a single architecture, and directly predicts a finite-horizon action sequence and executes the first action. It also introduces a lightweight point-mass dynamics model that optimizes trajectory-level costs through numerical gradient propagation, enabling unsupervised training without expert demonstrations.

  37. arXiv cs.RO60

    Foundation-Model-Based 3D Scene Graphs for Mobile Manipulation

    The paper integrates FOUND-IT into a mobile manipulation robot platform, validating that foundation-model-based 3D scene graphs can support object change detection, multiple mapping sessions, navigable path determination, and object grasping in scene rearrangement tasks. Based on rigorous real-robot experiments, the authors provide an analysis of the effectiveness and limitations of a monocular, foundation-model-driven 3D scene graph construction system. The code is open-sourced in the MIT-SPARK/FOUND-IT repository on GitHub.

  40. arXiv cs.RO45

    TRACO: Enhancing Visual Task-Redirection Attacks on Vision-Language-Action Models via Closed-Loop Optimization

    The TRACO framework enhances targeted visual attacks on vision-language-action (VLA) models through closed-loop optimization. Its Execution-Driven Supervision rebuilds supervision signals based on the states actually reached under attack, while Progress-Guided Selection concentrates updates where task progress stalls and maintains supervision of prior behavior. Experiments across multiple VLA models and simulated environments show that TRACO improves task redirection success rates, highlighting the security implications of closed-loop attack optimization.