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Only Research
Oct 22Thu
  1. arXiv cs.RO51

    Causal world models under physical parameter shifts: explicit affine coefficients improve extrapolative prediction

    This study examines the degradation of world-model predictions when physical conditions fall outside the training distribution, comparing two causal modeling elements: the inputs available to the predictor and how its mechanism computes their effects. On a synthetic benchmark with shortcut features, a frozen dependency mask derived from paired interventions reduced the out-of-distribution RMSE of a fully connected predictor from 1.769 to 0.697, close to the generator-oracle mask's 0.694.

  2. arXiv cs.RO55

    RoPT: A Robust 6DoF Object Pose Tracking Framework for Video

    RoPT is a 6DoF object pose tracking framework for robotic interaction that couples visual perception, temporal kinematics, and global re-localization into a closed loop to counter catastrophic divergence in monocular pipelines under fast motion. Its visual module tracks object masks to handle partial and severe occlusions; a kinematic factor graph regularizes frame-to-frame updates, predicts forward poses, and drives parallel multi-hypothesis verification to avoid local minima; and an active re-localization module gives the system failure awareness and deterministic self-recovery.

  4. arXiv cs.RO56

    AW-0: An Embodied World Action Model Framework Unifying Code-Based Planning and Video-Action Prediction

    The paper proposes AW-0, an agentic world action model framework that combines code-based planning with joint video and action prediction. Within a unified mixture-of-transformers architecture, it simultaneously models three coupled generative streams: code, future video, and robot actions. A code expert generates parameterized skill routines and control logic, while video and action experts generate future visual states and low-level motor commands.

  6. arXiv cs.RO57

    CW-WAM: A Force-Sensitive Manipulation World Action Model That Jointly Predicts Contact Wrench

    CW-WAM is a world action model that jointly predicts video, motion, and contact wrench. Inspired by hybrid motion-force control, it uses a unified interaction module to predict time-aligned motion-wrench chunks as control references. It structures motion-wrench dependencies with pair-causal attention and retains past interaction information in a fixed-length wrench memory; the predicted wrench chunk serves as the force reference for a 20 Hz outer-loop admittance controller.

  8. arXiv cs.RO56

    AtomicRL: An Atomic Reinforcement Learning Post-Training Framework for Long-Horizon Robotic Manipulation

    The paper proposes AtomicRL, a framework that uses atomic task structure to improve reinforcement learning post-training of vision-language-action (VLA) policies. Its core components include Semantic-Aware Policy Optimization (SAPO), which uses semantic gating to prevent TD residuals from failed atomic subtasks from propagating across tasks, thereby mitigating credit contamination; and Bottleneck-Adaptive Target Exploration (BATE), which dynamically coordinates task sampling and local exploration incentives based on the mastery level of each subtask.

  10. arXiv cs.RO58

    RISE: Test-Time Motion Steering for Perceptive Humanoid Fall Recovery

    RISE is a perceptive humanoid fall-recovery method that uses task objectives at inference time to steer how the robot stands up. It learns a recovery vocabulary that links intended movements to motor actions through sensory feedback; a visual diffusion planner learns from executions of this vocabulary to predict motion intent as future body states, and task costs steer that prediction toward a desired heading, rise speed, or path, after which motion latents are generated accordingly. Simulations demonstrate diverse recoveries, terrain adaptation, and test-time steering without retraining, with a real-robot demo on the Unitree G1.

  12. arXiv cs.RO57

    SP-HLM: Sparse-Pose-Driven Motion Generation and Whole-Body Control Framework for Humanoid Loco-Manipulation

    SP-HLM proposes a sparse-pose-driven interface framework that uses five task-critical body poses plus a learned implicit motion prior to represent spatial intent and whole-body coordination in complementary forms. The SP-VLA vision-language-action model first predicts task-relevant sparse poses and finger joint positions from multimodal observations and language instructions via asynchronous action-chunk inference. The SP-MGM motion generation model, trained through two-stage distillation, reconstructs coordinated full-body motion in real time, and the SP-GMT general motion tracker then combines the explicit sparse targets with the implicit motion prior for whole-body control.

  13. arXiv cs.RO58

    TARSphere: A Robot-Oriented Full-Stack Framework and Benchmark for Long-Horizon Embodied Agents

    TARSphere is a robot-oriented full-stack framework for long-horizon embodied agents operating under continuously changing physical and social environments. It includes TARSphere-Infra, which runs robot policies in physics simulation; TARSphere-Harness, which lets a frozen multimodal large language model make decisions through multi-level executable skills; and TARSphere-Bench, an evaluation benchmark comprising 360 episodes across 30 indoor scenes.

  15. arXiv cs.RO50

    Object- and Task-Aware Active Perception Framework for Mobile Manipulators in Semi-Static Environments

    An arXiv paper proposes an object- and task-aware active perception framework that lets a high-degree-of-freedom mobile manipulator use its hand camera to actively inspect uncertain or safety-critical regions during navigation. The framework uses a probabilistic next-best-view (NBV) formulation and is integrated with hierarchical task model predictive control (HTMPC), exploiting kinematic redundancy to adjust viewpoints while preserving the higher priority of base-trajectory tracking. Real-world tests showed that, compared with passive or non-task-aware baselines, the system improved map consistency and reduced task-related map entropy and navigation risk.

  16. arXiv cs.RO56

    HuMiT: Low-Latency Whole-Body Humanoid Teleoperation via Compact Reference Tracking

    HuMiT is a whole-body humanoid teleoperation system whose unified tracking policy requires only a compact reference target—root height, root velocity, and the SE(3) poses of four sparse keypoints at the current frame—yet achieves tracking accuracy comparable to methods that rely on full joint positions, dense body-part transformations, and even future frames.

  18. arXiv cs.RO47

    Spacetime Splines for Adaptive Robot Motions

    This study proposes a spline-based movement primitive representation that unifies spatial geometry and temporal progression into a common set of continuous variables, jointly estimates a shared Bezier curve, and allows spatial and temporal model complexity to be adapted independently. Simulations validated joint recovery accuracy, refinement from modifications to model complexity and continuity, and improved robustness to variance in the demonstration set. In a robot-assisted patient-turning task, online retiming and synchronization capabilities were evaluated with a physical manikin.

  19. arXiv cs.RO51

    Paper proposes path to improve robot zero-shot generalization through data diversity

    An arXiv paper formalizes zero-shot generalization in physical AI as coverage of the deployment distribution, noting that more samples from the same collector can only slowly shrink the unseen region, while each additional contributor can expand coverage. Based on this, the paper proposes a shared latent space for robot actions that combines multimodal alignment, generative augmentation, and noisy-label learning, and suggests that developers build applications around the standard robot Origmi to provide diverse data. It concludes with a testable hypothesis and evaluation protocol linking real-robot zero-shot success rates to data diversity.

  20. arXiv cs.RO51

    OMAR: Open-World Multi-Attribute Reasoning for Scene-Conditioned Shelf Organization

    An arXiv paper proposes OMAR, an open-world multi-attribute reasoning pipeline for inferring a shelf's latent organization and ranking placement positions for new objects without user preference instructions. A vision-language model first proposes object labels and attributes such as content and packaging type, which are grounded through segmentation, depth, and multi-view association; an LLM then reasons over an instance-level 3D scene graph to produce scene-consistent placement rankings, followed by geometric and kinematic feasibility checks before execution.

  22. arXiv cs.RO45

    Sufficient and Necessary Causal Explanations for Autonomous System Failure Analysis

    An arXiv paper proposes distinguishing two goals in autonomous system failure analysis, reproduction and avoidance, and formalizes the concept of "necessary causal explanations," which together with existing "sufficient explanations" forms a unified causal analysis framework. Based on actual causality and causal responsibility, the paper presents an efficient approximation algorithm for generating necessary explanations, evaluates its efficiency across multiple environments, and uses counterfactual generation to verify that it outperforms existing methods on failure avoidance.

  23. arXiv cs.RO67

    SpatialDex: Pre-training 3D-Grounded Bimanual Dexterous Manipulation from RGB-Only Human Videos

    SpatialDex is a framework that learns 3D-grounded bimanual dexterous manipulation from RGB-only human videos through reconstruction-augmented visuomotor pre-training, learning human behavior together with its spatial context. It reconstructs scene geometry and estimates hand motion from each video, aligns the two at a video-level scale using the hand-size prior of 3D hand estimates, and uses the reconstructed geometry as both a policy input and a prediction target.

  24. arXiv cs.RO57

    CausGround: Supporting Adaptive Embodied Planning with Causal Interaction Knowledge

    CausGround proposes a non-parametric causal interaction grounding framework that distills interaction experience into reusable, action-centered causal knowledge, identifies action preconditions and effects, and organizes them into a factorized world-state representation. At planning time, it aligns task-relevant causal dependencies with current observations, supporting backward reasoning over preconditions and forward reasoning over effects.

  26. arXiv cs.RO55

    FlowVLA: Flow-Aware Physical Completion Estimation for Quantitative Robotic Pouring

    FlowVLA separates action generation from physical-completion estimation. A shared π0.5 action expert learns approach, pour, and return, while a target-independent estimator fuses visual, joint, wrist-wrench, and recurrent force-belief histories to estimate transferred mass. It triggers return when this estimate plus predicted residual outflow reaches the requested mass.

  28. arXiv cs.RO25

    From Acceptance and Avoidance to Affective Viscosity: Designing Social Agents That Intervene in Human Relationships

    The paper connects research on an agent's acceptance of and avoidance of gaze with the affective viscosity model, which describes how negative emotions accumulate, persist, are suppressed, and are expressed over time. It argues that avoidance should not be seen merely as an interaction failure, but as a relational signal marking the boundary between humans and agents. On this basis, the paper proposes a design perspective in which agents maintain, suppress, or express negative states depending on social relationships and interaction histories. It discusses design cases including triadic jealousy, discomfort suppression, patience timing, and spatiotemporal stickiness, and then raises questions about whether interventions can be traceable, limited, contestable, and repairable.

  29. arXiv cs.RO47

    Reading the Room: Scale-ambiguous Sparse Semantic VSLAM Map Registration to Floor Plans of Partially Built Environments

    Reading the Room (RR) proposes a global-local method for registering scale-ambiguous sparse semantic VSLAM maps to floor plans of partially built environments. Its core is a single robust, differentiable, class-balanced semantic-geometric energy function that runs through the entire pipeline. Evaluated on 33 sequences from 3 construction sites, it improves mean absolute trajectory error by 4.5 times over the strongest baseline in the general unconstrained setting, rising to 7.6 times when anchor correspondences are provided.

  31. arXiv cs.RO60

    End-to-End Reinforcement Learning for Quadrupedal Jumping in Lunar Gravity

    The study proposes a multistage reinforcement learning method that produces a single end-to-end control policy for commanded point-to-point jumping by a quadruped robot under lunar gravity. The policy architecture is simple and relies only on mission-compatible onboard sensing; in simulation, landing errors are smaller than the robot's body size. The work also analyzes torque and power demands and characterizes performance degradation under extreme outlier events, including control-update freezes of up to 1 second during flight. In hardware transfer tests, the policy successfully completed a vertical jump under lunar-gravity offloading, and measured torque and power profiles matched the simulation and stayed within hardware limits.

  32. arXiv cs.RO45

    Joint-Angle Invariance of Jacobian-Based Performance Indices for Serial Robotic Manipulators

    The study reveals the kinematic conditions under which Jacobian-based performance indices of serial robotic manipulators, such as manipulability and stiffness, are independent of the first and last joint angles, i.e., joint-angle invariance (JAI). This property systematically reduces the dimensionality of the effective configuration space and simplifies performance evaluation. In two scenarios, workspace performance map construction and task layout optimization, the JAI strategy reduces computational effort and storage requirements without compromising effectiveness. The work has been accepted to IROS 2026.

  33. arXiv cs.RO55

    ContactFlowFilter: An Online Contact Estimation Method for Robotic Arms Based on Flow Matching and Particle Filtering

    The paper proposes ContactFlowFilter for estimating the number, locations, and force vectors of active contacts on a robotic arm equipped only with joint torque sensors and a base force/torque sensor. The method uses a flow matching model as an auxiliary proposal distribution for a particle filter, leveraging the particle filter's recursive structure to capture temporal dependencies while tracking multimodal beliefs and detecting contact addition and removal events.

  34. arXiv cs.RO44

    Actuator-Constraint-Aware Master-Slave Control Method for Teleoperation of Manipulators with Coupled Transmissions

    An actuator-constraint-aware master-slave teleoperation method that jointly models master-side gravity compensation, master-slave joint mapping, and slave-side differential wrist control, addressing the problem that independent motor saturation in differential wrists disrupts pitch-roll coordination and that fixed mappings struggle with abrupt tracking changes caused by gravitational loads and actuator constraints.

  36. arXiv cs.RO51

    Make it Walk: Computational Design Methods for LEGO Walkers

    The paper proposes the first computational approach for designing LEGO walkers that support multiple gaits beyond stable walking. The method has two parts: first, a specially designed gear-linkage mechanism controls the foot trajectory of each leg, incorporating a pair of 3D-printed non-circular gears working with a LEGO Technic mechanism; second, each leg is driven by an independent motor, with a simple yet flexible control algorithm synchronizing the timing of multiple leg motions.

  38. arXiv cs.RO47

    Linear Twisting Sidewinding Gait Motion Planning for Snake Robots on Complex Terrain

    The study proposes a linear twisting function that distributes torsional load across the entire body of a snake robot, replacing the existing rolling vector method's approach of concentrating it in two middle twisting joints. Under matched turning speed and total output power, the new gait reduces the peak output power of a single twisting joint by 42%. Real-robot experiments show it can move on various terrains that the existing rolling vector method cannot traverse, and is up to 49% faster than the traditional sidewinding gait without twisting.

  39. arXiv cs.RO55

    Distance-Field-Based Reinforcement Learning for Reactive and Robust Obstacle Avoidance in Robotic Arms

    This paper proposes a reinforcement learning framework that integrates the Euclidean Distance Field (EDF) directly into the observation space, providing the agent with continuous geometric cues to achieve stable and responsive obstacle avoidance behavior and simplifying reward design; it effectively avoids obstacles both with and without artificial potential field guidance.

  40. arXiv cs.RO56

    A2I-Calib++: A Noise-Robust Active Calibration Framework for Whole-Body Multi-IMU Systems on Legged Robots

    The paper proposes A2I-Calib++, a kinematics-driven, noise-robust active calibration framework for multi-IMU systems mounted on legged robot bodies. It can estimate temporal offsets, extrinsic rotations and translations, and inertial biases without requiring artificial calibration targets or complex parameter tuning.