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#Data and simulation
Oct 27Tue
  1. arXiv cs.RO58

    Patchwork: A Resource-Sharing System for Domain-Randomized Embodied Simulation

    The authors propose Patchwork, a resource-sharing system for domain-randomized simulation built on the E=T+Δ representation, which uses shared templates plus patches to record environment changes, implemented on robosuite and MuJoCo. On LIBERO-plus and RoboCasa365 workloads, at 32 replicas, shared-template execution reduces CPU RSS by 88-92% compared with process-per-environment execution. Experiments show that memory growth depends more on template diversity and unique asset content than on the number of instances.

  2. arXiv cs.RO62

    GEMS: A Long-Term Spatiotemporal Memory Framework Combining 3D Gaussian Splatting Maps with Agentic Reasoning

    The authors propose the GEMS framework, which combines a persistent spatiotemporal 3D Gaussian Splatting (3DGS) map with an agentic reasoning pipeline. The map is reconstructed once and thereafter updated only in regions where the scene changes, with those modifications saved. Using this map, the agentic pipeline renders novel views on demand, progressively accumulates evidence to answer spatiotemporal questions, and grounds objects from natural-language queries.

  3. arXiv cs.RO72

    FLORE: Learning Force-Reactive Humanoid Loco-Manipulation

    The research team proposes the FLORE framework, which uses two force-sensing UMI-style handheld grippers and wearable trackers to collect robot-free demonstrations, and trains a force-reactive diffusion policy that takes vision, proprioception, and measured force wrenches as input and outputs actions and per-hand force commands. A companion hybrid compliant whole-body controller uses integral force error from real force/torque sensors to achieve single-hand force tracking while maintaining balance. In real-robot tests on three contact-rich tasks, the force-reactive policy achieved an 80% success rate, compared with 26.7% for a position-only policy under the same controller.

  4. arXiv cs.RO71

    Differentiable Contact Dynamics for Legged Locomotion: Policy Learning and Contact-Implicit Trajectory Optimization

    This paper proposes a smooth analytic gradient for frictional contact that replaces the exact contact sensitivity only in the backward pass, leaving the forward hard-contact solve and the hardware-feasible trajectory unchanged. A policy trained on 100,000 samples in under 2 minutes on a desktop PC transfers to hardware without fine-tuning; under a trotting policy the robot withstands pushes and traverses gravel and slippery ground. In trajectory optimization, the gradient finds contact-rich solutions that the exact gradient and the Signorini relaxation gradient miss.

  6. arXiv cs.RO62

    Specola: An STL Specification-Driven Robot Simulation Evaluation Tool

    Specola is a tool for evaluating robot behavior in simulation against Signal Temporal Logic (STL) specifications. It provides a Python API and command-line interface that can be integrated into existing policy execution loops. It offers an adapter interface and already provides adapters for nine simulation environments and benchmarks, including LIBERO, RoboCasa, Isaac Lab, and RoboDojo, to supplement success rate with information such as degree of satisfaction, parameter sensitivity, and violated requirements.

  7. arXiv cs.RO62

    VPR-Space: A Benchmark for Visual Place Recognition in Space Analog Domains

    The paper proposes the VPR-Space benchmark to evaluate visual place recognition (VPR) capabilities in extraterrestrial surface scenarios such as the Moon and Mars. The authors built 16 database-query datasets based on existing data from Space Analog Domains (SAD), covering challenges including environment, viewpoint change, and illumination conditions.

  9. arXiv cs.RO67

    3D Gaussian Splatting-Based Precise Reaching for Wrist-Camera VLA

    This work proposes a 3D Gaussian Splatting (3DGS)-based data generation pipeline for adapting wrist-camera VLA policies to fine reaching tasks. The method reconstructs the real workspace and renders wrist-camera observations from robot-aligned poses, pairing them with smooth, structured reaching trajectories for VLA fine-tuning, and adds an instruction-conditioned reach verifier that judges termination before physical contact.

  11. arXiv cs.RO60

    DEX-PHD: A Portable Handheld Device for Multimodal Data Collection for Dexterous Manipulation

    The authors propose DEX-PHD, a portable handheld device that integrates a dexterous robot hand, continuous per-finger control, and multimodal logging, and can collect proprioceptive information, fingertip contact data, and wrist-camera video, with the operator directly controlling the robot hand through joint-level mapping. Its untethered design supports data collection in a variety of environments. The authors demonstrate it on contact-rich tasks that require multi-finger coordination, and execute the collected demonstrations on a physical humanoid robot through open-loop replay; the results show that open-loop replay preserves the main temporal patterns of finger motion and task-relevant contact.

  12. arXiv cs.RO63

    EmbodiedLab releases embodied agent benchmark: letting robots "act to know" in physics experiments

    EmbodiedLab is a benchmark that requires robots to "act to know": the agent receives an objective and experimental apparatus, chooses its own experimental procedure, reads instruments via camera in a physics simulation laboratory, and is evaluated on the knowledge it ultimately gains as well as the physical state required by the task.

  13. arXiv cs.RO59

    LRIO: Resilient Decentralized Radar-LiDAR-Inertial Odometry for Subterranean Environments

    The authors propose LRIO, a decentralized LiDAR-radar-inertial odometry framework that fuses LiDAR, FMCW radar, and inertial sensing for pose estimation in GPS-denied subterranean tunnels affected by smoke. At the core of the framework is an Adaptive Residual Thresholding (ART) mechanism, which detects sensor degradation over time and adjusts the fusion process, combined with a Robust Error-Tolerant Optimal Information Filter (RETOIF) that fuses LiDAR-inertial and multiple radar-inertial odometry estimates in a fault-aware manner.

  15. arXiv cs.RO60

    Labference: A Benchmark for Evaluating the Reasoning-Action Connection in Robotic Laboratories

    The authors propose Labference, a benchmark for evaluating how robots adjust subsequent operations based on actual physical execution outcomes, separately measuring operation selection, physical execution, and task completion. Preliminary evaluation shows that the tested VLA policies still made target-selection errors even when object transfer succeeded, while GPT-driven control, though able to select correctly, encountered execution failures and higher computational cost.

  16. arXiv cs.RO58

    RetroNav: A Self-Evolving Lifelong Navigation Framework Combining Scene Retrospection and Retry

    RetroNav is a training-free lifelong navigation framework that integrates navigation, memory accumulation, and failure reflection, achieving state-of-the-art performance on five navigation benchmarks. It uses a unified memory system to link geometric and semantic scene knowledge with historical navigation experience, distinguishing objective observations from task-dependent interpretations. After each navigation attempt, an independent evaluator determines whether a retry is needed; when it is, a retrospection agent uses scene evidence to generate a retry brief. The authors say the method will be open-sourced.

  17. arXiv cs.RO60

    Reproducible Simulation Study of How Tuning, Reference Preview, Actuator Modeling, and Fallback Policies Affect Quadrotor Controller Comparisons

    The study compares cascade PID, hover LQR, BL-SMC, linear MPC with LQR fallback (LMPC-F), a preview-enabled LMPC-F, and a geometric SO(3) controller on a nonlinear plant with rotor gyroscopic coupling, motor lag, and saturation. It finds that a unified twelve-candidate tuning protocol changed the mean-RMSE ranking obtained under fixed baseline settings, moving BL-SMC from last to first; the new ranking recurred on 50 fresh realizations and remained unchanged under a 48-candidate budget.

  18. arXiv cs.RO72

    Zero-HLM: A humanoid loco-manipulation framework without real robot data

    Zero-HLM proposes a real-to-sim-to-real framework that turns smartphone captures and simulated source demonstrations into scalable training data for humanoid loco-manipulation. The study generated 4,800 physically validated demonstrations and achieved zero-shot transfer on three Unitree G1 tasks; GR00T N1.7 trained in simulation reached a mean full-task success rate of 66.7%, versus 46.7% with real-data training.

  19. arXiv cs.RO62

    PushAround: Physics-Informed Hybrid Search for Multi-Robot Collaborative Path Clearing

    PushAround proposes a physics-informed hybrid search method that combines geometric connectivity reasoning with short-horizon simulation to generate executable clearing plans for mobile robot teams. The method uses repeated W-clearance connectivity graph (WCCG) queries to identify frontier gaps that need widening and ranks them by estimated downstream cost, then generates direct or recursive pushing candidates, selecting obstacles, directions, contacts, and forces.

  20. arXiv cs.RO60

    VISTA: An Active Exploration Framework for Self-Improvement in Embodied Agents

    VISTA is a meta-agent framework that replaces post-hoc replay reflection with active self-exploration to improve the harness of embodied agents. It maintains a harness graph that records the lineage of harness versions, enabling the meta-agent to retrieve past hypotheses, reuse effective mechanisms, and avoid repeating failed directions. Each round starts from a retained version and its execution evidence, autonomously generates new rollouts, probes failure and recovery behavior, and on that basis iteratively revises the harness and re-evaluates it.

  21. arXiv cs.RO59

    Study: Behavioral quality of human behavior models does not equal robot learning utility

    The paper evaluates multiple types of human behavior models using common generative model metrics in two scenarios, high-speed driving and object handover, and compares them with the downstream performance of reinforcement learning policies trained using these models. The results show that existing behavioral evaluation metrics cannot reliably predict downstream policy performance, and that evaluation results are sensitive to the trajectory representation; even when the true test behavior distribution is known, training with a different distribution may yield better downstream performance.

  22. arXiv cs.RO67

    SceneAgent: An agentic pipeline from 3D and 4D captures to simulation environments

    SceneAgent is an agentic pipeline that converts 3D Gaussian Splatting, photogrammetry, LiDAR captures, AI-generated 3DGS scenes, and 4D dynamic videos into interactive simulation environments through simulator-based visual inspection and repair. The pipeline covers object segmentation, physical parameter estimation, part decomposition, articulation, and deformable simulation.

  23. arXiv cs.RO40

    Resource-Performance Trade-offs in Intra-Aisle Robotic Picking: A Discrete-Event Simulation Comparison of Shuttle Configurations With and Without an Onboard Tote Lift

    A discrete-event simulation study compared two implemented intra-aisle robotic picking configurations: without an onboard tote lift, two level-captive shuttles serve the two tested levels; with the lift, one shuttle remains on its horizontal level and its onboard mechanism moves the order tote to the adjacent upper level to complete picks.

  24. arXiv cs.RO71

    RobotAgentBench released: a benchmark for open-world robot autonomy

    The authors propose RobotAgentBench (RAB), a benchmark for evaluating robot agents in realistic open-world household environments. Using an agent-assisted, human-in-the-loop real-to-sim pipeline, it constructs 50 household layouts and includes 90 atomic tasks, 18 short-horizon tasks, 8 long-horizon tasks, and 4 open-ended scenarios.

  25. arXiv cs.RO60

    What Does World Modeling Actually Improve in Robot Action Learning?

    The study conducts controlled future-video prediction loss ablations in FastWAM and OpenWAM, raising success rates by 24.38 and 72.91 percentage points, respectively, under clean conditions on seen tasks. Trajectory clustering shows higher success rates within several coarse trajectory clusters, but cluster frequencies change little, indicating the gains do not come solely from changes in the distribution of trajectory patterns.

  26. arXiv cs.RO64

    PhysLadder: A Hierarchical Sim-Real Matching Benchmark for Contact-Rich Manipulation, Evaluating GPT-6 Astra

    The authors propose PhysLadder, a hierarchical sim-real benchmark for evaluating physical interaction capabilities in contact-rich manipulation, comprising 16 household tasks divided into three cumulative levels: contact selection and geometric alignment (L1), force regulation under sustained contact (L2), and coordinated contact reconfiguration and force regulation (L3).

  28. arXiv cs.RO59

    AuRA: Reconstructing Digital Twins from Atoms to Bits through Autonomous Robot Interaction

    AuRA is an automated robot interaction and data preparation system for digital-twin reconstruction, combining execution-aware grasp selection, gripper-motion measurements derived from robot states, and registered pre-interaction geometry and appearance priors. Across 13 deformable objects, it reduces active human labor and wall-clock collection time per successfully reconstructed asset by 88.5% and 55.4%, respectively; compared with the human-collected PhysTwin baseline, it improves 7 of 9 metrics for future prediction under known gripper motion, including a 28.1% reduction in one-way L1 Chamfer distance.

  29. arXiv cs.RO77

    RynnVLA-Latent: Scaling Robotic Manipulation Foundation Models with 3D-Grounded Latent Actions

    The research team proposes the RynnVLA-Latent framework, which uses RynnLAM to learn continuous, 3D-grounded latent actions from visual changes without native robot control signals, converting 30.1K hours of human, robot, and simulation video into 745.52M latent action descriptors for policy pretraining.

    Why it matters: This research uses 3D-grounded latent actions to turn heterogeneous interaction videos into pretraining supervision, offering a reusable path for VLA models to bypass the scarcity of action annotations.

  30. arXiv cs.RO58

    ScheNav: A Sketch-Guided Embodied Object Navigation Framework

    The authors propose ScheNav, a closed-loop navigation framework that turns rough sketch guidance into exploration and stopping decisions. Its core is SchemaField, a learning module that fuses guidance with accumulated observations and predicts spatial value fields. A supporting data pipeline converts 2D floorplans into navigation training environments, producing the Furnished-RPLAN dataset, which contains over 33k furnished layouts with paired navigation episodes and noise-controllable sketches.

  32. arXiv cs.RO68

    HumanoidNDM: Closing the training-deployment gap in humanoid motion tracking with a neural dynamics model

    The study proposes HumanoidNDM, an online fine-tuning framework for humanoid motion tracking driven by a neural dynamics model trained on real robots, aimed at closing the dynamics gap between training and deployment. The method includes low-frequency reference perturbation data collection with an adaptive intensity mechanism, training a dynamics model based on proprioceptive history in Euclidean space combined with differentiable forward kinematics, and directly fine-tuning the policy through backpropagated model gradients.

  34. arXiv cs.RO72

    X-World: Transferring video priors to variable-view robot control

    X-World proposes a framework that transfers video priors to a shared action expert through fixed-length token sequences, and uses residual cross-view attention to extend a pretrained video backbone to one, two, or three views. The policy reaches 98.5% mean success on LIBERO, and its LIBERO-trained checkpoint obtains 76.5% on LIBERO-Plus without additional adaptation; across six dual-arm real-world tasks, it achieves a mean success rate of 81.7% over 20 trials per task.

  35. arXiv cs.RO64

    Millimeter-Wave Radar Uses Doppler Information to Track Occluded Targets' Position and Velocity

    The authors propose a method that uses millimeter-wave radar Doppler information to track non-line-of-sight occluded targets, achieving 8.95 cm position error and 1.75 cm/s velocity error on held-out trajectories. The study also releases the first indoor non-line-of-sight radar dataset, containing 576 sequences totaling 720,856 frames and covering 12 trajectories with different speeds, starting positions, and material configurations. The method models non-line-of-sight tracking as continuous position-velocity estimation, uses a shared 3D encoder to process the full range-azimuth-Doppler representation, and estimates position and velocity separately through two recurrent branches before fusing them into motion-consistent trajectories.

  36. arXiv cs.RO59

    LiDAR-based GNN framework learns interaction-aware path corrections from unlabeled trajectories for construction robots

    The study proposes a real-time, LiDAR-only framework that learns interaction-aware path corrections from unlabeled demonstrations: constant-velocity extrapolation generates residual learning targets, a GNN models relationships among the robot, pedestrians, and goal context, and at deployment an extended Kalman filter tracks LiDAR detections to build dynamic graphs, with the GNN offsetting planner-generated waypoints through a planner-agnostic interface.

  37. arXiv cs.RO49

    Lightweight Change Detection for Adaptive Collective Perception in Dynamic Environments

    For dynamic environments where tile proportions and the spatial correlation of surface patterns change over time, this work integrates a lightweight change detection mechanism into a Bayesian collective decision-making framework, resetting robot beliefs when distribution shifts are detected. The detector requires only constant memory and constant per-sample computation, unlike two recent methods that must store the full sample stream, making it suitable for memory-constrained robots. Webots simulations show competitive change detection latency, more consistent accuracy across different communication settings, and improved decision accuracy when spatial correlation changes.

  38. arXiv cs.RO58

    T-ConSG: A LiDAR-Based Real-Time Temporal Scene Graph for Dynamic Construction Environments

    The paper proposes T-ConSG, a LiDAR-based real-time temporal construction scene graph that uses spatial-relation edges and event-participation edges to jointly represent free space, static objects, dynamic objects, and environmental events. The framework incrementally builds the graph from LiDAR geometric evidence and explicitly represents changes in accumulated spatial evidence as installation and removal events. The authors validated the representation in three studies: dynamic-aware navigation on a bricklaying testbed, construction progress monitoring on the ConSLAM dataset, and online change detection in a simulated environment.

  39. arXiv cs.RO60

    Cut a Wall, See a Wall: Identity Substitution in Occluded Point-Cloud Perception for Construction Robots

    The paper finds that when entire regions of a scene are occluded at test time, point-cloud segmentation networks relabel the hidden elements into a single sink class; under fixed layouts this class absorbs up to 70% of test points, and baseline mIoU drops to 0.15-0.27. The authors append 14 hand-computed geometric features per point (GPE), restoring mIoU to 0.54-0.92 on PointNet++ and a PTv3-style transformer and removing 64-100% of whole-object losses; on real S3DIS scans, whole-wall losses drop from 10 to 0.

  40. arXiv cs.RO58

    A Motion-Induced WiFi-CSI Sensing Simulation Framework for Multi-Robot Systems

    This study proposes an open-source simulation framework that integrates the WiFi Sensor for Robotics (WSR) sensing pipeline into Gazebo. It can generate synthetic WiFi-CSI measurements with realistic physical signal characteristics and supports processing both simulated and real WiFi-CSI data for direct comparison and validation. The authors used TurtleBot3 robots to compare simulated and real experiments, demonstrating the framework's use in bearing estimation and robot localization. The code and experimental details have been made public.