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#Research
Oct 27Tue
  1. arXiv cs.RO47

    Robot Motion as a Keyed Communication Channel: Transmitting Messages via Motion Steganography

    Researchers propose a "motion communication" method that encodes arbitrary short messages (such as an agent's current intent) as noise into the actions of any pre-trained policy, making robot motion detectable via remote vision or motion capture without degrading policy performance. This physical channel does not rely on wireless communication and requires no additional hardware or direct link to the robot. On real robots running at 50 Hz, four robots jointly recovered an 8-bit message at a total rate of 0.8 bits/s, with validation completed in both simulated environments and real-robot deployment.

  3. arXiv cs.RO71

    Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL

    This work proposes using sample-based model predictive control (SMPC) in simulation as an automated expert to generate large-scale offline datasets, then training an off-policy reinforcement learning agent with purely sparse task rewards, eliminating the need for manual dense reward design. Combined with a low-level dynamic stability controller, the high-level agent's learned policies can exceed the original optimal control teacher on task objectives. The authors completed sim-to-real deployment on an arm-equipped Spot quadruped and a G1 humanoid; the paper is the accepted version for CoRL 2026.

  4. arXiv cs.RO58

    SHRIMP: Iteratively Revising Robot Task Plans with Natural Language

    The SHRIMP system lets users automatically generate hierarchical robot primitive plans using natural language, and iteratively revise those plans through re-prompting and explicit correction. After each revision, users can validate the plan in simulation and, once satisfied, execute it on the physical robot. A user study with 35 participants on tabletop kitchen task planning showed that SHRIMP improved users' perceived control and robot transparency.

  5. arXiv cs.RO72

    DSMS: Contact-Implicit Multiple Shooting for Dynamic Motion Retargeting

    The study proposes DSMS, a contact-implicit, direct-simulation-based multiple shooting framework that converts kinematically feasible reference motions into dynamically feasible whole-body trajectories. It handles contact, friction, collision, and joint limits within a nonlinear program without presetting a contact schedule. Compared with existing retargeting methods, DSMS accelerates motion-imitation reinforcement learning training and yields policies with high success rates and low tracking error. The authors achieved zero-shot sim-to-real transfer on the Unitree G1, completing walking, jogging, contact-rich crawling, and a 180-degree jump-turn.

  6. arXiv cs.RO72

    SMAT: Jointly training human-exoskeleton co-adaptation policies in simulation and validating on real subjects

    The study proposes Staged Multi-Agent Training (SMAT), a method that jointly trains a musculoskeletal human agent and a bilateral hip exoskeleton control policy in physics simulation to learn an assistance policy that accounts for human-device co-adaptation. The policy was transferred to a physical hip exoskeleton without subject-specific retraining and tested in eight healthy adults; indirect calorimetry showed that active assistance reduced net metabolic rate by 19.7% relative to the passive device (p < 0.001), the hip mechanical positive-power ratio was 0.984, and effective assistance was maintained at walking speeds and terrains beyond the training conditions.

  7. arXiv cs.RO60

    Plug, Play and Comply: A Plugin-Based Compliant Control Framework in ROS 2

    The paper proposes a robot-agnostic compliant control framework that separates ROS controller boilerplate from control-law implementation through a plugin-based execution architecture, extending the ROS control ecosystem. The framework uses Pinocchio to compute robot kinematics and dynamics from URDF models, and its standardized joint and Cartesian command interfaces support motion references, stiffness and damping gains, nullspace targets, desired interaction wrenches, and independent feedforward inputs.

  9. arXiv cs.RO67

    LaWAM: Efficient Dynamics-Aware Robot Policies with Latent World Action Models

    LaWAM proposes a Latent World Action Model that exposes predictive dynamics to robot policies through compact latent visual subgoals rather than reconstructed future video. Its core LaWM trains a latent action model in the latent space of a pretrained vision foundation model and reuses its forward decoder to predict future observation features.

  11. arXiv cs.RO60

    MPPI-Based Trajectory Planning for Drifting Target Search and Capture with Unmanned Surface Vehicles

    This study proposes a spatiotemporal informative planning method based on model predictive path integral (MPPI) control for unmanned surface vehicles (ASVs) to search for and capture multiple drifting targets. The planner couples target drift prediction with exploration objectives through a cost design, optimizes continuous trajectories over long horizons, and directly generates kinematic commands; in the interception stage, pure pursuit guidance is used to achieve physical capture. In simulation, the method captured 18% more targets than the strongest baseline, and the approach was validated in real-boat trials.

  12. arXiv cs.RO60

    PRISM: Prior-Guided Imagination Sampling in World Model Representations

    The authors propose PRISM, which trains a lightweight action head on features from a frozen latent world model encoder to obtain a goal-conditioned action prior, then uses its mean and predicted uncertainty to guide action search, with the world model evaluating the consequences of actions. Experiments on four simulated control tasks and two real-robot platforms show that the lightweight action head improves planning performance, and that the same fusion interface can accept a diffusion prior over the same frozen features without modification.

  13. arXiv cs.RO61

    MRO-GWM: Action-Conditional and Object-Centric Gaussian Splatting World Model for Rigid Objects

    The authors propose MRO-GWM, an object-centric Gaussian world model for learning action-conditional dynamics of 3D rigid objects. The model represents arbitrary shapes and multi-object scenes with Gaussians in an object-centric frame, and uses a temporal-spatial Transformer to predict rigid body motion from past Gaussians and future actions; training is based on multi-view reconstruction, which requires handling partial observations caused by occlusion.

  14. arXiv cs.RO74

    Crazyflow: A JAX-based GPU-accelerated differentiable drone simulator

    The authors propose Crazyflow, a differentiable drone simulator built on JAX that combines high-fidelity dynamics with GPU acceleration, scaling to millions of environments or swarms of thousands of drones on a single CPU and GPU. Real-world experiments show that it supports analytical-gradient policy learning, achieving high-accuracy tracking without domain randomization, and sampling-based obstacle avoidance at over 500 million steps per second. The authors also demonstrate in-flight reinforcement learning: after tossing a physical drone into the air, training a recovery policy from scratch took only 0.37 seconds, and the drone was caught before landing.

  15. arXiv cs.RO58

    Haptic Sorter: A Unified Planning Framework for Online Shape Estimation and Real-Time Pose Inference

    The authors propose Haptic Sorter, a unified model-based geometric framework that integrates robotic haptic perception, modeling, and manipulation planning for manipulation when object shape and pose are unknown. The framework uses Bayesian optimization to guide haptic exploration and approximates geometric boundaries with superellipses, describes quasi-static robot-object interaction with an adaptive manipulation potential that encodes object geometry, and proposes an online ordinary differential equation based on model prediction and tactile feedback for real-time pose inference. The system is deployed on a 2D robotic sorting task, varying object geometries to validate the framework's robustness and generalizability in both simulation and a real-world multi-arm setup.

  17. arXiv cs.RO58

    LLM-Guided Future Hypotheses for Horizon-Aware Exploration in Multi-Step Robot Manipulation

    The paper proposes Future-Experience Conditioning (FEC), which uses a latent representation of a short future video as a condition for closed-loop policies, for exploration and policy adaptation in multi-step robot manipulation. Future clips are generated in three stages: an LLM reasoner performs task-ontology reasoning initialized from the current scene state, a robot-free digital twin rolls out the intended object motion, and a mask-free video diffusion model synthesizes a robot-consistent future clip.

  18. arXiv cs.RO67

    WorldArena 2.0 released: expanding the embodied world model benchmark across modality, functionality, and platform

    WorldArena 2.0 has been released, expanding embodied world model evaluation along three dimensions: modality, functionality, and platform. In modality, it extends from vision-only to visuotactile; in functionality, it extends beyond policy evaluation and planning to using world models as interactive reinforcement learning environments for policy optimization; in platform, it extends from simulation-only to multiple simulators and real robot embodiments. The benchmark evaluates perception quality, interaction utility, and cross-platform performance under a unified protocol, and the project page provides code and demos.

  19. arXiv cs.RO72

    RoboEvolve: A Planner-Simulator Co-Evolution Framework for Robotic Manipulation

    RoboEvolve couples a VLM planner and a VGM simulator into a mutually reinforcing co-evolutionary loop, bootstrapped from unlabeled seed images to alleviate manipulation data scarcity. Experiments show it raises the base planner by 30 absolute points and improves simulator success by 48% on average, surpassing fully supervised baselines with only 500 unlabeled seed images and reducing initial reinforcement learning data by 50 times.

  20. arXiv cs.RO60

    BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation

    BEACON proposes a theory-guided cross-domain co-training framework that trains diffusion-based visuomotor policies using abundant source demonstrations and limited target demonstrations, and formulates co-training as a discrepancy-aware importance reweighting problem. The method introduces scalable instance-level discrepancy proxies, stochastic alternating updates of policy and weights, and a multi-source extension, improving robustness and data efficiency over target-only, fixed-ratio co-training, and feature-alignment baselines in sim-to-sim, sim-to-real, and multi-source manipulation settings.

  21. arXiv cs.RO60

    SAS: A Semantic Autonomy Framework for VLM-Integrated Indoor Mobile Robots

    The paper proposes the Semantic Autonomy Stack (SAS), a six-layer integration framework that builds semantic reasoning, mission interface, and operational memory layers on a validated ROS 2/Nav2 foundation. It uses a deterministic-first cascade that escalates unresolved or guarded cases to a pretrained vision-language model (VLM).

  22. arXiv cs.RO62

    Visibility-aware mobile grasping system for dynamic environments

    The authors propose a unified system that simultaneously constrains collision risk along the whole-body trajectory and target visibility during grasping in unknown and dynamic environments, comprising three parts: a velocity-aware gaze policy, a hierarchical subgoal policy, and a receding-horizon whole-body planner. In 400 randomized simulation scenarios and real-world trials on a Fetch mobile manipulator, the system achieved success rates of 70.4% in unknown static environments and 57.9% in dynamic environments, 16.3 and 9.3 percentage points higher than decoupled navigation and manipulation policies, respectively, while significantly reducing collisions.

  23. arXiv cs.RO62

    High-Speed, Scalable Sensor Readout Architecture for Dexterous Hands Based on Shift Registers

    The paper proposes a scalable analog sensor readout architecture based on serial-in parallel-out (SIPO) shift registers, in which sensor modules can be expanded using only three signal lines. The authors integrated 16 joint sensor modules and one four-channel tactile sensor module on a tendon-driven dexterous hand, achieving full scanning of 20 channels at 1 kHz and stable operation up to 1.5 kHz; the maximum slope absolute percentage error of the joint sensors was 0.446%, and on the tactile side an LSTM model performed force estimation and contact-location classification at 1 kHz.

  24. arXiv cs.RO62

    SentryVLA: Uncertainty-Guided Action Selection and State Monitoring for Vision-Language-Action Robotic Control

    SentryVLA is a lightweight runtime wrapper for frozen VLA policies that requires no retraining or changes to the model architecture. At the action level, it uses conformal quantile regression to estimate an upper bound on the deviation between sampled actions and expert behavior, and ranks multiple stochastic candidate actions accordingly; at the execution level, it uses a Mahalanobis-distance anomaly score to monitor abnormal states during closed-loop control. Simulation and real-world experiments show that the method improves action selection and provides useful runtime warning signals.

  25. arXiv cs.RO67

    AffordSim: A Scalable Data Generator and Benchmark for Affordance-Aware Robotic Manipulation

    AffordSim is a scalable data generator and benchmark that integrates open-vocabulary 3D affordance prediction into simulation trajectory generation. Given a natural-language task description, it synthesizes scenes, generates affordance queries, and samples region-constrained grasps, then uses motion planning to filter executable candidates.

  26. arXiv cs.RO60

    ArrayTac: A Closed-Loop Piezoelectric Tactile Platform for Continuously Tunable Shape, Stiffness, and Friction

    ArrayTac is a closed-loop piezoelectric tactile platform that can simultaneously and continuously render three tactile dimensions—shape, stiffness, and friction—on a 4×4 actuator array. Each unit integrates a three-stage micro-lever amplifier and end-effector Hall-effect feedback, achieving up to 5 mm displacement, an array refresh rate above 500 Hz, and a closed-loop bandwidth of 123 Hz.

  27. arXiv cs.RO60

    PrepTurn: An anticipatory turning reinforcement learning framework for fast sequential quadruped navigation

    PrepTurn is a reinforcement learning framework for anticipatory maneuvering in continuous local navigation. It maps the current and next pose goals directly to joint actions, letting the upcoming goal influence how the robot approaches the current one. The framework uses a persistent-credit reward to encourage completing the entire goal sequence while in motion, and an automatic curriculum that gradually increases goal difficulty to stabilize turning behavior; simulation and onboard Unitree Go2 experiments show high sequential traversal completion rates and reduced stop-and-go motion.

  28. arXiv cs.RO71

    RoboGene: A Multi-Arm Morphology-Diverse Task Generation Framework for VLA Pre-Training

    RoboGene is an agentic task generation framework for single-arm, dual-arm, and mobile robots that automatically produces diverse manipulation tasks before VLA pre-training. It combines diversity-driven sampling, agentic self-reflection to assess task quality, and human-verified feedback memory for execution-grounded validation and continual improvement. It collected 18,000 trajectories and introduces task feasibility and diversity metrics.

  29. arXiv cs.RO70

    FlowHOI: Flow-Matching-Based Semantics-Grounded Generation of Hand-Object Interaction Sequences for Dexterous Manipulation

    FlowHOI is a two-stage flow-matching framework that generates semantically grounded hand-object interaction sequences, including hand poses, object poses, and contact states, conditioned on first-person observations, language instructions, and 3D Gaussian splatting scene reconstructions.

  30. arXiv cs.RO71

    UniManip: An Open-World Robotic Manipulation Framework Based on Agentic Operational Graphs

    UniManip proposes a unified framework centered on the Agentic Operational Graph (AOG) for task orchestration, state tracking, execution verification, and closed-loop failure recovery in open-world robotic manipulation. The method grounds high-level semantic intent into executable primitives through a physically grounded scene graph, and combines a safety-aware local planner to generate collision-free trajectories. It achieves state-of-the-art performance on LIBERO-Pro, and in real-world experiments its task success rate is 22.5% and 25.0% higher than leading VLA models and hierarchical baselines, respectively.

  31. arXiv cs.RO65

    DWPP: Dynamic Window Pure Pursuit Considering Velocity and Acceleration Constraints

    The authors propose dynamic window pure pursuit (DWPP), which moves command velocity computation into the v-ω velocity space and selects, within the dynamic window, the point closest to the line ω=κv, thereby explicitly incorporating velocity and acceleration constraints. Experiments show that DWPP avoids constraint-violating commands and achieves better path-tracking accuracy than conventional pure pursuit methods. The method has been integrated into the official Nav2 repository and made publicly available, and the paper has been accepted by Robotics and Autonomous Systems.

  32. arXiv cs.RO57

    Collaborative Continuum Robots: A Survey Constructing a Closed-Loop Task Architecture and Three Collaboration Modes from an Automation-System Perspective

    This survey constructs a closed-loop task-execution architecture for collaborative continuum robots (CCRs) from an automation-system perspective, explaining how target-task specifications are transformed into coordinated execution by multiple continuum robots. Based on this, the authors classify CCRs into three modes: separated collaboration, assistance collaboration, and parallel collaboration. They then review research progress in terms of multi-robot task allocation, structural design, modeling, motion planning, control strategies, and sensing mechanisms, and finally discuss key technical opportunities and the system-integration challenges of real-world deployment.

  33. arXiv cs.RO58

    CRAFT-E: An Interpretable and Editable Neuro-Symbolic Framework for Affordance-Grounded Object Retrieval

    CRAFT-E is a modular neuro-symbolic framework that combines an LLM-derived verb-property-object knowledge graph with CLIP-based visual alignment and energy-based grasp reasoning for affordance-grounded object retrieval. The system provides interpretable grounding paths, incorporates grasp feasibility into affordance inference, and supports graph edits without retraining.

  34. arXiv cs.RO71

    Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models

    The study proposes an automatic framework for generating robotic planning and execution failure data, builds the GuardianFail-36k failure reasoning dataset based on RLBench and BridgeDataV2, and trains Guardian, a multi-view reasoning vision-language model. Guardian achieves state-of-the-art performance on three unseen real-world benchmarks: RoboFail, RoboVQA, and the newly introduced UR5-Fail. When combined with LLM-based manipulation policies, it improves task success rates in both simulation and real-world deployment. Code, data, and models are publicly available.

  35. arXiv cs.RO62

    IndustryNav: Evaluating Spatial Reasoning and Safety of Embodied Agents in Dynamic Industrial Navigation

    The authors propose IndustryNav, a simulation environment with 24 warehouse scenes for closed-loop navigation to specified destinations, and introduce safety metrics that record collisions and near-collision warnings. Experiments with 11 leading open- and closed-source VLLMs and 3 navigation baselines show that agents can reach their goals yet collide with moving obstacles, exposing a gap between task success and safe navigation.

  36. arXiv cs.RO62

    A Planning-Level Intention Prediction Framework for Mobile Robot Shared Control

    The paper proposes a shared control framework for mobile robots with planning-level intention prediction, using an intention domain to constrain path replanning and jointly modeling intention-domain prediction and replanning as a Markov decision process solved via deep reinforcement learning. The authors also design a Voronoi-based human trajectory generation algorithm that allows the model to be trained entirely in simulation without human participation or demonstration data; simulations and real-world user studies show the method reduces operator workload and improves safety without degrading task efficiency.

  37. arXiv cs.RO38

    Toward Physical Recursive Self-Improvement: A Call for Body-Control Co-Design for Humanoid Robots

    A NeurIPS 2026 Position Paper proposes body-control co-design to achieve physical Recursive Self-Improvement (RSI) in humanoid robots: using experience from the current body and policy to recursively improve both morphology and behavior at the same time. The paper discusses co-design methods grounded in strategic exploration, Sim2Real transfer, and meta-policy learning, argues for their necessity from methodological, application, and community perspectives, and lists open research questions ranging from short-term innovations to long-term goals.

  38. arXiv cs.RO34

    The Special Galilean Group SGal(3) and Its Applications to Several Robotics Problems

    The special Galilean group SGal(3) is a 10-dimensional Lie group whose important subgroups include the special orthogonal group, the special Euclidean group, and the group of extended poses. This work briefly describes SGal(3) and its Lie algebra, explains how the group structure supports a unified representation of uncertainty in space and time, and points out the group's potential uses for several robotics problems.

  39. arXiv cs.RO53

    Study proposes virtual nonholonomic constraints for nonprehensile robotic catching

    The paper proposes using virtual nonholonomic constraints (VNHCs) to address the problem of nonprehensile robotic catching, designing and controlling the motion of the end-effector directly based on the object's state before it is caught, without the need to predict the object's trajectory and generate a time-varying robot trajectory. In the planar case, the method can catch the object at a desired height and minimize the relative normal velocity at the instant of contact; the same VNHC function depends only on the target catching height and can cover a range of initial conditions. After contact, model predictive control (MPC) is used to bring the object and end-effector to rest at a specified position. The approach has so far been validated in simulation.

  40. arXiv cs.RO66

    OSWAM: A World Action Model with One-Step Action Generation

    OSWAM proposes a world action model that generates actions with just one diffusion step, for autonomous driving and embodied control. The authors initialize the diffusion process with imagination tokens to provide physical priors and action-conditioned representations, and introduce privileged information distillation with future conditioning, making action quality comparable to or better than direct-policy WAM and Joint-WAM, which require tens of steps. It is about 3 times faster than the direct-policy WAM baseline, with compiled inference latency as low as 40ms on an NVIDIA RTX 5090 GPU.