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Only Research
Oct 27Tue
  1. 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.

  2. 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.

  3. 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.

  4. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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).

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  21. 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.

  22. 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.

  23. 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.

  24. 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.

  25. 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.

  26. 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.

  27. 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.

  28. 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.

  29. 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.

  30. arXiv cs.RO58

    Multi-Objective Reinforcement Learning with Routed Ensembles and Trajectory Attribution

    This paper studies policy learning in multi-objective reinforcement learning, proposing objective-conflict coefficients and using them to derive near-optimal regret bounds for online settings, quantifying the impact of objective misalignment on learning efficiency. The authors design a routed ensemble policy learning algorithm that maintains a small set of basis policies and dynamically routes trajectories to the policy with the highest potential reward; they also propose a kernel surrogate trajectory attribution algorithm that estimates how trajectory feedback signals affect the policy learning of other trajectories.

  31. arXiv cs.RO62

    OpTTA: Test-time adaptation of world models for planning, not just prediction

    The paper proposes OpTTA, which adds the planner's goal-reaching cost term to the test-time adaptation objective for world models, keeping the model optimistic about reaching the goal. Under visual and environmental shifts in manipulation, reaching, and navigation tasks, when only the predictor is adapted, OpTTA raises the final planning success rate by an average of 13.5 percentage points, compared with 6.9 percentage points for AdaJEPA, which uses only the prediction objective; when the encoder is also adapted, the two achieve 13.5 and 7.5 percentage points, respectively.

  32. arXiv cs.RO62

    High-resolution tactile observation makes hidden racket-control mechanisms learnable

    The research team used an 864-node tactile-inertial glove to synchronously collect more than 14,000 strokes at 100 Hz from over 70 participants across five skill levels, and proposed a mechanics-based two-layer framework that infers the control driving the racket and stabilizing the grip from tactile history and reconstructs its spatiotemporal force realization.

  33. arXiv cs.RO52

    Uncertainty Evaluation for SOTIF Analysis of ML LiDAR Object Detection: Transfer Testing of Performance Targets

    The paper proposes an uncertainty evaluation method for ISO 21448 SOTIF analysis: an uncertainty threshold selected under one condition is transferred to a triggering condition, and a test decides at a given error probability whether the performance target is applicable, not applicable, or undetermined under that condition, and provides a lower bound on the number of independent units required for an applicable conclusion.

  34. arXiv cs.RO72

    Video World Models Need Predictable Latents

    This paper proposes making the latent space of video world models balance predictability with pixel reconstruction accuracy, achieved through temporal objectives for representation learning paired with a past-conditioned decoder architecture. Under the same training budget, inference cost is reduced by up to 5x, training speed increases by more than 7x, and pixel and perceptual errors in out-of-distribution and long-horizon prediction drop by 15-24%. The authors argue that temporal predictability should be an important design principle for video world model latent spaces and provide a concrete method for incorporating it into autoencoder training.

  35. arXiv cs.RO49

    Whole-brain recordings in fruit flies and a neuromechanical digital twin: how intrinsic goals in hierarchical exploration capture future volitional action

    The study combines whole-brain recordings in fruit flies with a neuromechanical digital twin to test whether agents trained with intrinsic motivation algorithms can reproduce the fly's spontaneous exploratory behavior in a virtual tethered-ball experiment. Agents select actions from a discrete library of low-level motor primitives and are trained without external reward using 20 intrinsic motivation algorithms; among them, DIAYN came closest to the fly in behavior and neural prospective encoding, and reproduced the additive and subtractive rules of interhemispheric kinematic encoding.

  36. arXiv cs.RO26

    Quadrotor Trajectory Generation and Control: A Simulation Study Based on Differential Flatness and Minimum-Snap Trajectories

    This project explores controller design and trajectory generation for a quadrotor. It uses a differential flatness approach to generate minimum-snap trajectories based on waypoint navigation, and a simple PD controller to track the trajectories. Simulation results show that the method can generate smooth trajectories for the quadrotor's position and velocity.

  37. arXiv cs.RO42

    Nonlinear Control of Aerial Manipulator Robots with a Quadrotor and a Two-DOF Robotic Arm

    For a quadrotor equipped with a two-DOF robotic arm that can physically interact with its environment, this study implemented and compared two controllers: Cartesian impedance control and adaptive sliding mode control. Under bounded disturbances, both controllers performed a simple hovering task with the same initial conditions and target position, and were validated based on a full dynamics model.

  39. arXiv cs.RO60

    TLP: Single-Parameter Robust Point Cloud Registration with Truncated Lp Functions

    The authors propose TLP, a point cloud registration method with only one tunable parameter. It uses truncated Lp functions with different p values to assign priority scores to correspondences, select hypotheses, and refine the final model. Targeting the high outlier rates in real-world 3D feature matching, the method uses GPU parallelism to evaluate hypotheses and can on average complete registration from thousands of correspondences within 1 second. On public benchmark datasets, TLP is more accurate than existing single-parameter methods and outperforms two commonly used multi-parameter methods on three of the four evaluated datasets, while maintaining competitive runtime.