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#Data and simulation
Oct 28Wed
  1. arXiv cs.RO59

    Study: Active sampling with vision-based tactile sensors for hardness classification

    The paper investigates active sampling strategies based on vision-based tactile sensors (VBTS) for sample-efficient hardness classification, using information-theoretic measures of model uncertainty. The authors evaluated three probabilistic classifiers, two active sampling strategies, and a random sampling baseline on a hardness classification task using their own VBTS dataset collected by a robot and a previously published dataset, and also conducted a human study on hardness classification with the same objects. Results show that the active sampling methods using variance and entropy as uncertainty measures performed best, reaching 90% accuracy, compared with human accuracy of 48%.

  2. arXiv cs.RO62

    Research team builds egocentric tennis dataset and evaluates vision models' spatiotemporal prediction capabilities

    The research team collected an annotated egocentric tennis dataset and trained and evaluated image encoders, video encoders, and vision-language models on seven spatiotemporal prediction tasks. The best models achieved current-frame localization errors of 0.14 m for the egocentric player and 0.54 m for the ball, with trajectory prediction errors of 0.34 m and 0.91 m respectively within 1.1 seconds, and a mean error of 0.14 seconds for contact-time prediction. Auxiliary depth information notably improved 3D ball localization, while longer image histories did not improve ball trajectory prediction; the team also proposed a conditional diffusion model that generates full-body tennis kinematics from egocentric video at real-time rates.

  3. arXiv cs.RO55

    MCP-Net: A Real-Time Coronary Perception Network and Topological Analysis for Robotic Guidewire Navigation

    The authors propose a simulation-driven framework centered on a multi-task coronary perception network, MCP-Net, for robotic guidewire navigation, integrating coronary vessel segmentation, vascular topology extraction, and roadmap generation. The method generates simulated fluoroscopy images and simulation-derived ground truth from three-dimensional coronary CT angiography, including simulated guidewires, stenotic lesions of varying severity, and diverse vascular geometries. MCP-Net jointly performs vessel segmentation, guidewire-tip localization, and stenosis localization from a single fluoroscopic frame.

  4. arXiv cs.RO64

    ThinkWorld: A VLA Learning Framework That Reinforces Chain-of-Dynamics Reasoning with World Imagination

    ThinkWorld is a vision-language-action (VLA) learning framework that represents each action thought as a Chain-of-Dynamics (CoD), a sequence of discrete motion tokens that captures the intermediate dynamics connecting the current world to a future state and serves as a common action representation across embodiments.

  5. arXiv cs.RO75

    In-hand object pose reaching strategy and safety filter for the LEAP dexterous hand

    The research team proposed a training recipe for in-hand object pose reaching with the LEAP dexterous hand. It trains in simulation without human data and, after system identification, transfers zero-shot to the real robot, using either motion capture or a single wrist-mounted monocular depth camera. In a real-robot evaluation with 4 objects and 4,800 unseen targets over 13.3 hours, the state-based policy reached 95.2% of targets within 2 cm and 0.4 rad; adding a learned residual safety filter with adjustable conservativeness reduced drops by 75% while still reaching 91.5% of targets.

  6. arXiv cs.RO72

    RESPLE2: Inertial-Informed Non-Uniform B-Spline Multi-Sensor Odometry with Global Optimization

    RESPLE2 proposes a recursive multi-sensor state estimation framework based on non-uniform B-splines for 6-DoF motion representation, using the knot-clamping boundary property to predict control points directly from inertial kinematics. Prediction and reset are performed within an iterated extended Kalman filter, and the update step fuses LiDAR, camera, IMU, and leg kinematics at their native timestamps; a separate lightweight global optimization module refines control points using LiDAR-visual loop-closure constraints.

  7. arXiv cs.RO80

    Rendering-Free Event Frame Simulation Accelerates Reinforcement Learning for Autonomous Robot Navigation

    The authors propose an analytic event simulator that synthesizes event streams directly from scene geometry and ego-motion without image rendering, generating 64x64 event observations in just 17 microseconds on a single GPU, over 400 times faster than state-of-the-art rendering pipelines. Reinforcement learning training for an event-camera obstacle-avoidance policy based on this simulator converges in under 30 minutes, three orders of magnitude faster than training on rendered events, and transfers zero-shot to ray-traced event simulation in Isaac Sim.

    Why it matters: This work uses analytic event simulation to bypass image rendering, cutting event-camera reinforcement learning training time to under 30 minutes, and completes real-world deployment on a 56-gram drone.

  8. arXiv cs.RO60

    StabBot: Robotic Pipe Stabbing with Learned Stereo Vision and Residual Reinforcement Learning

    The StabBot robotic stabbing system uses a pendulum Kalman filter to track pipe joints in stereo images, approaches at matched speed, and confirms the final few millimeters by touch. In 200 randomized jobs, StabBot completed every stab, versus 74% for a conventional fixed guide; median contact speed fell from 18.4 cm/s to 2.3 cm/s, and median peak head-pipe force fell from 11.3 kN to 1.4 kN.

  9. arXiv cs.RO55

    Which Hand Was Late? Diagnosis for Robot Failure Recovery in Bimanual Tool Use

    The study measures, on a simulated bimanual tool-use task, which failure causes each sensing channel lets a diagnoser identify: a tool slipping out of its socket can be read on the tool-hand side; which hand was late requires information from both arms, while a sensor on one hand can only read that hand's own delay and part of the other hand's delay; and when the tool hand is late, passive sensing cannot determine whether the push force was also insufficient.

  10. arXiv cs.RO70

    World Dreamer v5-lite: Multi-Robot World Action Model with a Single Dreamer

    World Dreamer v5-lite is a multi-robot world action model that predicts separate action sequences for each robot through a shared world state. The model builds the world state in workspace coordinates from calibrated features of a pretrained video encoder, and its spatial size does not change as views are added; a vision-language model provides scene context and one query per robot, and the queries read the shared state and exchange pose-aware information while their action streams remain separate.

  11. arXiv cs.RO58

    Ego-CEVR: Learning Cross-Embodiment Visual Representations from Human Egocentric Videos

    Ego-CEVR proposes a data-native hierarchical visual representation learning framework that assigns distinct learning objectives to global and interaction representations. The global module learns object relations, task state, and temporal progress through future prediction on egocentric video; the interaction module interprets wrist observations in global context and learns local alignment and contact cues through robot action supervision. Two residual paths feed both representations into the action policy simultaneously, and the global video module can be pretrained independently on large-scale human egocentric videos and videos from multiple robot embodiments, without robot action labels or paired wrist observations.

  12. arXiv cs.RO70

    MARL-Exo: Multi-Agent Reinforcement Learning Co-Adapts Hip Exoskeleton Control to Reduce Metabolic Energy Cost

    The study proposes MARL-Exo, a multi-agent reinforcement learning framework in which a full-body musculoskeletal model and a bilateral hip exoskeleton policy are co-trained in simulation in two stages; the exoskeleton observes only delayed hip kinematics and past commands. In simulation, the metabolic policy reduced modeled muscle metabolic power by 13.8% compared with walking without the device, and by 8.8% after adding mechanical-power shaping.

  13. arXiv cs.RO60

    AuRaSim: A Ray-Tracing Automotive FMCW Radar Simulator for Dynamic Scenarios

    AuRaSim is a ray-tracing-based automotive FMCW radar simulation framework that generates coherent multi-channel ADC data for dynamic scenarios. It builds dynamic scenes with Blender, performs GPU-accelerated electromagnetic ray tracing with Sionna RT, and adds path-level complex-coefficient propagation, motion-aware Doppler modeling, and waveform-configuration-driven ADC cube synthesis. In a digital twin scenario, the simulated and measured range-Doppler maps achieve an RMSE of 0.147 and a PSNR of 16.93 dB.

  14. arXiv cs.RO65

    PDRVLA: A vision-language-action model with physical dynamics representations

    PDRVLA proposes a lightweight Physical Dynamics Module that extracts physical tokens from vision-language features to condition action generation, without online video generation. The module learns through direct prediction of future RGB, depth, and optical-flow features, with auxiliary supervision from a frozen pretrained video world model. All training data comes from public data and was expanded through human-to-robot synthesis, subtask annotation, and embodied chain-of-thought (ECoT) annotation.

  15. arXiv cs.RO60

    Target-free joint camera-LiDAR intrinsic and extrinsic calibration method

    The authors propose a fully automatic, target-free framework that jointly estimates camera intrinsics, radial-tangential distortion, and camera-LiDAR extrinsics from raw images and LiDAR scans. The method extends deep pixel-point correspondence-based calibration from rectified images to raw cameras through intrinsic- and distortion-aware correspondence learning, and iteratively couples correspondence prediction with nonlinear optimization during calibration. Zero-shot evaluations on an unseen KITTI configuration and on three real robotic platforms show that joint calibration improves extrinsic accuracy while recovering accurate intrinsics.

  18. arXiv cs.RO70

    RAVEN: A Unified Active Visual SLAM System for Quadrotor Localization Uncertainty

    RAVEN is an active SLAM framework for quadrotors that unifies planning of exploration and active loop closure under localization uncertainty, and uses a priority parameter from 0 to 1 to adjust the emphasis between exploration efficiency and localization accuracy. The framework first identifies frontiers and active loop closure candidates and generates global tour plans, then evaluates them with a utility vector, and finally a perception-aware navigation planner generates smooth trajectories based on an online Fisher information field to reduce visual state estimation error.

  20. arXiv cs.RO54

    State Estimation for a Hybrid Rigid-Continuum Space Manipulator System

    This study proposes a state reconstruction pipeline for a hybrid rigid-continuum space manipulator whose distal link is a tendon-driven continuum robot. The method formulates the estimation problem as maximum a posteriori inference conditioned on joint encoders and vision measurements, transcribes it as a factor graph, fuses heterogeneous measurements with the rigid-soft kinematic model, and uses the Geometric Variable Strain (GVS) approach to obtain a minimum-order parameterization of the configuration. Closed-loop trajectory simulations verified the state reconstruction accuracy and computational performance.

  24. arXiv cs.RO36

    Integrated Design and Reproducible Feasibility Analysis of a Four-Wheel Rover: Packet-Commanded Imaging, LiDAR Ranging, and Learning-Assisted Target Recognition (NASA HERC 2027)

    For the high-school remote-control division of the 2027 NASA Human Exploration Rover Challenge, a study presents an integrated, reproducible design assessment of a four-wheel educational rover: 10.919 kg, 215 mm wheels at 200 RPM give a theoretical rolling speed of 8.105 km/h, and the nominal motor specifications are insufficient to demonstrate compliance with the 8 km/h design ceiling.

  26. arXiv cs.RO58

    An Auditable Framework for Autonomous Driving Laboratories: LAM, OpenSDL, Safe-SDL, and L3-Bench

    The authors propose a traceable framework comprising four components—LAM, OpenSDL, Safe-SDL, and L3-Bench—to connect experiment planning, automated execution, and measurement feedback. LAM is a measurement-conditioned policy architecture that extends the vision-language-action paradigm to generate both continuous robot trajectories and typed instrument API calls; OpenSDL maps role-level operations to heterogeneous devices and records timestamped causal events.

  27. arXiv cs.RO55

    Learning Side-by-Side Navigation for Personal Mobility Vehicles from Human Drivers

    The paper proposes a method for enabling two personal mobility vehicles (PMVs) to drive autonomously side by side: after a global path is given, each vehicle runs its own sampling-based model-predictive planner, the two exchange only the odometry information each can observe of the other, and a common rule is used to make a joint decision on a utility matrix over pairs of candidate trajectories.

  28. arXiv cs.RO56

    Study: Effects of Non-Rigid Bases on Mobile Manipulators in Sustained Contact Tasks

    Using a sanding task as a case study, the paper combines simulation with experiments on two lightweight, unactuated wheeled testbeds to characterize non-rigid base effects in mobile manipulators. Under low base-ground resistance, only 54-70% of a 100 N command was realized, with tool-center-point errors reaching 100 mm; increasing base-ground resistance during contact improved force realization and arrested further tool displacement, while base vibration and force-tracking error showed no consistent relationship under the tested conditions. The study was conducted by Zongyuan Zhang et al. and has been accepted to ACRA 2026.

  29. arXiv cs.RO60

    RoboSTD: A Bimanual Learning Method for Zero-Shot Single-to-Dual Transfer via Sagittal Mirroring

    RoboSTD is a zero-shot single-to-dual data construction method for bimanual robot learning. It applies sagittal-plane mirroring to observations, proprioception, and actions to generate executable contralateral pseudo-demonstrations, then uses an LLM to generate structured spatio-temporal coordination constraints that guide the reconstruction of pseudo-bimanual supervision across workspace regions and task stages. The study evaluates RoboSTD on the AgileX ALOHA platform in RoboTwin simulation and real-world settings. Without additional bimanual demonstrations, it achieves an average of 2.11 times the number of cups collected by the single-arm baseline in the cup-collection task.

  30. arXiv cs.RO60

    From Biological Curves to Modular Soft Robots: Learning-Based Inverse Design with Uncertainty-Guided Refinement

    The paper proposes a learning-based real-to-sim-to-real framework that converts biological curves into cable-driven modular soft robot configurations through surrogate-assisted inverse design and uncertainty-guided refinement. The learned forward surrogate reduces deformation prediction time from 1.53×10^5 ms to 0.0008 ms, a speedup of about 1.9×10^8 times; inverse design time drops from 1.99×10^6 ms to 4.18 ms.

  31. arXiv cs.RO58

    SWIM: Learning State Weights from Behavior to Train JEPA World Models

    SWIM proposes using an inverse dynamics model to learn state feature weights from offline trajectories for state-supervised training of JEPA world models. The method predicts recorded actions from current states and observed future displacements, quantifies each state feature's contribution to action prediction, and uses this to build a weighted state metric that aligns the latent space with state distances. The inverse dynamics model is used only during training; planning still relies solely on images. On five simulated robotic manipulation tasks, SWIM's planning success rate consistently outperforms uniform state weighting.

  32. arXiv cs.RO67

    HumiTransfer transfers human-human interactions from in-the-wild videos to humanoid robots

    The authors propose HumiTransfer, a framework that converts in-the-wild human-human interaction videos into physically feasible human-humanoid interaction motions. The method first reconstructs 3D human motion with foot-ground contact, then transfers one participant to the target humanoid embodiment through interaction modeling, and uses post-retargeting optimization to ensure balance, contact, and collision avoidance.

  33. arXiv cs.RO58

    QuadPose estimates full-body 18-DoF pose of quadruped robots from monocular video sequences

    The authors propose QuadPose, which estimates the full-body 18-DoF pose of a quadruped robot from monocular video sequences, including the 6-DoF base pose and 12-DoF joint angles, and release QuadSyn, the first camera-to-quadruped robot pose benchmark. On QuadSyn, QuadPose achieves 92.18% PCK@2.5px and 85.94% ADD@20cm, with a mean joint-angle error of 3.65 degrees and a runtime of about 33 FPS.

  34. arXiv cs.RO71

    OpenJaws: An open-source hardware UMI framework for generalist manipulation, using stereo localization to improve demonstration accuracy

    OpenJaws is a fully open-source sensorized UMI hardware and software framework that pairs a modular 3D-printed gripper with a low-cost stereo camera to recover metric camera trajectories, improving action-label localization accuracy in handheld demonstrations. The authors also propose UMI-Loc-Bench-v1, a benchmark for quantifying the real-world localization accuracy of handheld data-collection devices, and show that stereo localization clearly outperforms monocular alternatives. Policies trained on data collected with OpenJaws have been deployed on real robots across multiple tasks, and the hardware, software, and data are released under a commercially friendly license.

  35. arXiv cs.RO71

    REACH: An RL-based hierarchical humanoid crawling framework for confined-space traversal

    REACH is an RL-based hierarchical framework that enables a Unitree G1 humanoid robot to traverse height-constrained environments through variable-height bipedal walking and crawling. Its policies regulate body height over complementary command ranges: 1.27–0.50m for bipedal walking and 0.50–0.20m for crawling, and a bidirectional transition policy connects crouched walking and crawling. The framework also combines a LiDAR planner that converts point clouds into collision-free three-dimensional paths and locomotion commands. Real-world experiments validated height and speed tracking, terrain traversal, mode switching, and confined-space traversal; the code and videos have been released.

  36. arXiv cs.RO64

    CARD: A Contact-Anchored Retargeting Framework for Dexterous Manipulation

    CARD is a simulator- and object-model-free framework that retargets human demonstrations into robot actions by explicitly preserving contact force targets, and it handles video, motion capture, and tactile glove data in a unified way. On video, glove, and motion-capture data and the GRAB dataset, CARD maintains contact on 43%–78% of contact frames, whereas purely kinematic and object-aware retargeting methods stay below 10% and 22.5%, respectively.

  37. arXiv cs.RO58

    MyoAcross: A Generalist Muscle-Driven Control Policy for Musculoskeletal Models Across Individuals

    The study proposes the MyoAcross framework, which combines individual musculoskeletal model construction, morphology-conditioned policy learning, and virtual model augmentation to achieve muscle-driven reference-motion tracking without per-individual fine-tuning. Across 31 held-out individual models, it achieved a 99.24% completion rate and reduced tracking error by 22.60% relative to an unconditioned multilayer perceptron. Policy reuse also extended to 22 held-out individual models constructed from single-image shape estimates, height, and body mass, as well as to a model with unilateral lower-limb shortening after asymmetry augmentation.

  38. arXiv cs.RO69

    RoboAuto: Autonomous Robot Program Search through Multi-Level Physical Understanding

    RoboAuto proposes a robot program search framework that organizes physical understanding at three levels—object, scene, and interaction—to guide LLM agents in generating and revising manipulation programs. The method achieves better performance across five benchmark suites, reaching an 89.1% success rate on short-horizon LIBERO-PRO tasks and averaging more than twice the success rate of ASPIRE on LIBERO-PRO Long and LIBERO-Plus.

  39. arXiv cs.RO67

    SVIn+: An Underwater State Estimation Framework Integrating 3D Sonar, Visual-Inertial, Water Pressure, and DVL

    SVIn+ is a multi-sensor state estimation framework for autonomous underwater vehicles (AUVs). Building on the open-source visual-inertial SLAM system SVIn, it integrates water-pressure and DVL measurements into tightly coupled nonlinear optimization and uses a KISS-ICP decoupled front end to generate relative pose factors, incorporating 3D sonar structural information into the factor graph. The authors conducted field deployments in shipwreck and coral reef scenarios, validating drift suppression and global consistency. The paper was accepted to IROS 2026, and the code and datasets have been released on the project website.

  40. arXiv cs.RO71

    Whole-Body Humanoid Control via Stochastic Motion Tokenization

    This study proposes using a stochastic motion tokenizer to generate concise discrete motion representations that can be fed directly into a whole-body tracking policy to execute dance, fighting, and walking motions on the Unitree G1. At matched capacity, the tokenizer reconstructs Unitree G1 motions better than FSQ and VQ-VAE. In real-robot demonstrations, each generated motion lasted 16.8 seconds, and a 35-second fighting-to-dance sequence was completed.