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Only Research
Oct 28Wed
  1. arXiv cs.RO71

    Research team uses differentiable simulation to learn quadrotor dynamic narrow-gap traversal from scratch

    The research proposes a differentiable moving narrow-gap modeling method that gates centering, alignment, and smooth collision terms by signed distance to a time-varying gap plane, turning sparse traversal events into dense pathwise objectives, and integrates this into a visual-feature-to-command policy trained in a single stage from random initialization, without a teacher, planner, or distillation. In simulation, path gradients clearly outperform score-function gradients, reaching a 99.3% success rate after a few minutes of training on a single GPU. The policy transfers directly to a real quadrotor using only onboard vision and inertial measurements, successfully traversing the dynamic narrow gap in 8 of 10 real-world trials.

  2. arXiv cs.RO58

    CRS: A Joint-Constraint-Referenced Evaluation Method for Articulated Object Manipulation

    The authors propose Constraint-Referenced Strategy (CRS), which projects the end-effector twist commanded by a policy onto the object's joint axis, separating progress along the joint direction from off-constraint waste. On 74 simulated articulated objects, binary success rate only shows a lower transfer failure rate without attribution, whereas the two CRS channels give each a distinct signature.

  3. arXiv cs.RO65

    ARC: Using Human Corrections to Guide Residual Correction of Robot Policies

    The paper proposes Adaptive Residual Correction (ARC), which learns residual corrections via reinforcement learning on top of a frozen imitation policy, using sparse human corrections to decide when to activate residual control and in which direction of the residual action space to explore. On three precision manipulation tasks with a real robot, ARC raises average task success from 33.3% to 75.0%; the human corrections define 2-3 dimensional task-relevant subspaces of the seven-joint residual space, retaining 96.0-97.7% of the squared correction magnitude.

  4. 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.

  5. 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.

  6. 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.

  7. arXiv cs.RO58

    Non-line-of-sight passive object sensing using distributed sensing and hyperdimensional computing

    The paper proposes a framework that relies solely on distributed channel measurements from communication infrastructure to sense passive entities outside a robot's field of view and perform risk-aware classification. The method uses hyperdimensional computing (HDC) to embed measurements into high-dimensional representations without explicit channel reconstruction. Numerical simulations based on commercial ray tracing and validation in a ROS smart factory scenario show accuracy of up to 89% under severe occlusion while maintaining low computational complexity.

  8. 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.

  9. 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.

  10. arXiv cs.RO60

    Reinforcement Learning-Based Autonomous Fixed-Wing Drone Interception

    A team at Delft University of Technology in the Netherlands proposed a fixed-wing interceptor controlled by a neural network that maps filtered monocular vision observations directly to actuator commands; the policy was trained in a custom Python environment using reinforcement learning and proximal policy optimization (PPO). In simulation, the policy outperformed two classical guidance controllers under most conditions, and it has successfully intercepted maneuvering fixed-wing targets in real flight. This work provides validation for the autonomous control of fixed-wing interception drones.

  11. arXiv cs.RO44

    Electric-Field Induction Motor with Discrete Circuit Elements and Improved Rotor Dynamics

    Researchers propose an electric-field induction motor whose rotor is made of discrete circuit elements between a finite number of conductive vanes, overcoming the limits of a single continuous material in resistivity, permittivity, and circuit characteristics. This macroscale motor uses circuitry on the rotor surface to emulate the "deep bar" rotor design of magnetic induction motors, providing higher torque over a wide speed range. The study completed the analysis, design, and demonstration of the motor.

  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.RO62

    QCP: Learning Quality-Controllable Robot Policies with Iterative Hindsight Relabeling

    The authors propose Quality-Controllable Policy (QCP), which enables inference-time control over individual quality attributes such as execution speed and motion smoothness, as well as their combinations. To extend the controllable quality range beyond the initial demonstrations, the authors design an iterative hindsight relabeling loop: collect online rollouts under target quality labels, add successful trajectories to the dataset, then relabel the accumulated trajectories in hindsight according to their measured quality and fine-tune the policy.

  15. 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.

  16. arXiv cs.RO62

    VLA-Stem: Adding a Lightweight Safety Mechanism to Vision-Language-Action Models with Reinforcement Learning

    Researchers propose VLA-Stem, a lightweight reinforcement-learning-based safety mechanism that does not fine-tune the underlying VLA policy; instead, it converts its action chunks into motion references and, at inference time, adaptively corrects execution using proprioception and local obstacle information. Across six constrained simulation settings, VLA-Stem achieved an average collision-free task success rate of 52.8%, versus 23.7% for the strongest safety baseline, while preserving the original policy's performance in obstacle-free environments; real-robot experiments also showed reduced collisions under static and dynamic obstacles.

  17. 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.RO58

    Tokens of Motion: Primitive Motion Learning for Human-Humanoid Skill Transfer

    The paper proposes the Adaptive Failure-Informed Learning (AFIL) framework, which uses a pretrained VLA to generate failure trajectories online as adaptive negative guidance for diffusion- and flow-based VLA policies, avoiding the need for handcrafted failure modes or human intervention for recovery. The method jointly trains two action generators (DAGs) for success and failure on a shared vision-language backbone, and at sampling time adjusts the guidance strength according to the distance between the success and failure distributions at each diffusion step. On in-domain and out-of-domain, short-horizon and long-horizon robotic manipulation tasks, AFIL improves task success rate and robustness compared with existing VLA baselines.

  19. arXiv cs.RO55

    Spiking Neuromorphic Controller Stabilizes Periodic Orbits in Underactuated Robots: A Greedy Lyapunov-Based Approach

    This paper investigates event-driven spiking controllers implemented as spiking neural networks for trajectory tracking in underactuated robots, with the goal of stabilizing a prescribed periodic orbit. The authors reduce orbital stabilization to stabilizing the trivial solution of a periodic linear time-varying system via transverse linearization, and extend a Lyapunov-based greedy design originally developed for LTI systems to this periodic LTV transverse dynamics, thereby constructing a feedback controller. The method's effectiveness is demonstrated on the trajectory stabilization problem for the butterfly robot.

  20. arXiv cs.RO57

    KEPT: Conformal Prediction Keepout Planning for Enclosed-Space Navigation Under Degraded Sensing

    KEPT proposes a navigation method for enclosed spaces such as tunnels and mines, using conformal prediction to calibrate keepouts around mapped walls under degraded sensing, and adjusting the margin according to a label-free degradation severity estimate to maintain coverage. The planning and control layer enforces a braking-distance condition on the keepout at plan time and on every command, using the measured tracking residual as its only margin and never altering the keepout.

  21. arXiv cs.RO71

    Retrieve to Act: Improving VLA Execution Robustness with Motion Primitive Graph Retrieval

    The authors propose Retrieve to Act (R2A), a test-time scaling framework that organizes successful demonstrations into an offline spatiotemporal motion primitive graph. At inference, a query-dependent graph neural network locates the current execution state and retrieves structurally compatible future motion primitives, which are then fused with the frozen VLA's predicted actions through dimension-wise gating.

  22. arXiv cs.RO53

    LSMM: Learning Spatial Memory for End-to-End Legged Robot Exploration

    The authors propose Local Spatial Matrix Memory (LSMM) based on Hadamard memory, which lets reinforcement learning agents store task-relevant information in position-based memory cells. On an autonomous maze exploration task, LSMM's maze coverage is comparable to strong recurrent networks and finite-history baselines, and it is the only architecture that remembers the maze layout, reducing reconstruction error to one third. The work targets the problem that humanoid and legged robots in complex environments need to remember information long after it leaves their field of view before it is used.

  24. arXiv cs.RO67

    How Much Can Best-of-N Improve a World Action Model: Measuring the Selectable Space and Realizing It in Closed Loop

    The paper proposes a denoised counterfactual protocol that uses bitwise-reproducible branching, repeated continuations, held-out oracles, and common random numbers to measure the selectable space a world action model (WAM) can achieve when selecting candidate actions at test time, and builds a fail-gated selector on that basis.

  25. arXiv cs.RO52

    Research on Acoustic Signature Optimization for Bioinspired Flapping-Wing Micro Air Vehicles

    This paper investigates two passive geometric approaches to reduce the acoustic signature of a flapping membrane wing and validates them on an exemplary wing. After optimizing the venation layout based on a dynamic snap-through model of the membrane wing, experiments measured a reduction of up to 8 dB in the stroke-reversal snap noise; an additive trailing-edge fringe inspired by hummingbird feathers further reduced the broadband swoosh by 3–4 dB without loss of lift. The paper notes that engineered wings are 15–30 dB louder than the animals they imitate.

  27. 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.

  29. 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.

  30. arXiv cs.RO55

    A Structure-Actuation Co-Design Framework for All-Electric Heavy-Duty Robotic Manipulators

    This paper proposes a hybrid discrete-continuous multi-objective bilevel framework for the structure-actuation co-design of all-electric heavy-duty robotic manipulators: the upper level optimizes geometry and actuation topology, while the lower level searches for feasible configurations of motors, gearboxes, and screws for each joint. A system-level coordinator selects actuator packages based on hardware mass, co-design cost, and loss-aware supply energy, and feeds the resulting actuator masses back into the robot model until mass consistency is reached.

  31. arXiv cs.RO64

    Omni-IGN: Omnidirectional Vision Onboard Image-Goal Navigation System for Drones

    The authors propose Omni-IGN, a fully onboard drone image-goal navigation (ImageNav) system that integrates quad-fisheye perception, an attention-based reinforcement learning policy, and a collision-aware safety layer. The policy adaptively fuses goal-relevant features across multiple views to provide directional guidance for continuous navigation, and the quad-fisheye RGB images support both ImageNav and obstacle avoidance without a dedicated depth sensor.

  32. arXiv cs.RO59

    Task-Adaptive Friction Margins for Contact Maintenance in Bimanual Non-Prehensile Manipulation

    The paper proposes using signed friction distance to measure friction margin in force space, and derives a task-adaptive margin from the force correction needed to balance pivot moment deviations during bimanual tilting. The method embeds contact margins into a sequence of second-order cone programs (SOCPs), each yielding a globally optimal contact force solution at a given tilt angle, keeping robot contact forces and pivot contact forces away from the nominal friction boundary. Real-world experiments were executed under variations in object properties and model mismatch, and comparisons against fixed-margin and no-margin settings show the importance of adjusting the margin to task demand.

  34. arXiv cs.RO56

    When Black-Box Robot Policies Fail: Online Sensor Diagnosis and Repair with a Transparent Surrogate

    The study proposes a structured surrogate model that separates periodic gait from sensor-dependent correction terms, diagnosing and repairing corrupted inputs externally around a frozen locomotion policy. On simulated Go2, offline diagnosis raises walking success from 259/640 to 429/640, while online repair reaches 382/640, with no repair triggered in 64 held-out fault-free episodes. The authors note that the evidence remains limited to a single policy and a partly screened simulated fault set, and that robot foundation model and hardware validation have not yet been conducted.

  36. arXiv cs.RO62

    HC2Robot: Multi-Terrain Humanoid Parkour with Predicted Foresight and Hyper-Connection Transformers

    HC2Robot proposes a deployable perceptive humanoid parkour framework composed of a Predicted Foresight Module (PFM) and a Hyper-Connection (HC) Transformer. The PFM predicts, from deployable observations, the timing, location, and support quality of each foot's next touchdown event, converting privileged supervision into a deployable latent representation.

  38. 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.

  39. arXiv cs.RO58

    Study applies magnetic inclination and intensity gradient flow to GPS-free navigation

    An arXiv paper proposes modeling bio-inspired magnetic navigation (BIMN) as a gradient flow control and guidance problem, using magnetic inclination and intensity of Earth's magnetic field to guide a system through intermediate waypoints to a destination within a predefined region. The authors note that magnetic coordinates do not map one-to-one to geographic locations and are ambiguous, but that gradient flow can act as a "water current" carrying the system's motion and be steered. The framework could be used in engineering to build geomagnetic navigation infrastructure and to define and follow prescribed routes, and also to analyze animal migration routes that rely on magnetoreception.