Skip to content
Only Research
Oct 27Tue
  1. arXiv cs.RO34

    PARS: Post-Quantum IoT Blockchain Security for Mission-Critical Physical AI Agents

    Addressing the threat quantum computers pose to public-key authentication and key agreement, the study proposes the "rotational secrecy" architectural objective and the PARS reference mechanism, which coordinates policy-triggered key establishment, rotation, and reconciliation among authorized Physical AI agents. The optional PARS-ESL permissioned chain, maintained by authorized validators or edge gateways, records only authentication state such as key epochs, group membership, revocation, and algorithm-suite policies, not high-frequency business transactions, allowing resource-constrained agents to join as lightweight clients.

  2. arXiv cs.RO49

    A Framework for Segmenting Moving Objects During Camera Motion Based on the OWL Function

    Researchers propose an analytical framework based on the OWL perception function for segmenting multiple independently moving rigid bodies during single-camera motion. OWL encodes perceived looming L and perceived rotation ω into a closed-form representation of relative 3D structure, from which the unit direction of relative translation can be analytically recovered per pixel from L and ω alone; points on the same non-rotating rigid body share the same direction, so clustering completes the segmentation. The method is derived in 3D using quaternions, is parallel and camera rotation-invariant, and requires no training data, depth information, or semantic priors.

  3. 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.

  4. arXiv cs.RO40

    Contract-Based Agentic Systems Engineering: Using Quotient Operations to Achieve Assured System Design

    The study proposes treating SysML v2 requirements with assumptions and required constraints as assume/guarantee contracts. After decomposing system requirements over fixed neighbors, the weakest sound requirement for an open component is the contract quotient, which can be expressed in Signal Temporal Logic.

  5. 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.

  6. 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.

  7. 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.

  10. 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.

  11. arXiv cs.RO60

    TrySee: Modular Perception-Enhanced VLM Closed-Loop Robot Task Planning for Specialized Domains

    TrySee proposes a modular closed-loop robot task planning pipeline that combines general-purpose VLM reasoning with domain-specific perception and existing robotics components, without requiring VLM fine-tuning or end-to-end demonstrations. The system uses reactive cyclic planning and adds domain-specific modular vision preprocessing to provide online grounding, while a dynamic preprocessing selection mechanism improves scalability.

  12. arXiv cs.RO53

    FISRF: An Adaptive Reflection-Guided Framework for LLM-Based Robot Task Planning

    FISRF proposes an agent-and-mentor architecture that estimates a robot's Zone of Proximal Development (ZPD) in task planning and adaptively selects reflection strategies and mentor guidance, mitigating the accumulation of erroneous experience during early interactions. Experiments in ALFWorld and on real-world robotic platforms show that the method reduces average error-correction rounds by 37% and redundant reasoning by 25.1%, while improving overall task success rate by 8%.

  13. arXiv cs.RO62

    Continuous-Time Safe Planning and Control for Agile Quadrupeds

    The research proposes a real-time motion planning framework that retains the full SE(2) model of a quadruped robot along with its anisotropic, heading-coupled velocity constraints, and accepts the resulting nonconvexity. The method parameterizes trajectories as composite Bernstein polynomials, uses the convex hull property to prove that obstacle avoidance and input constraints hold throughout the entire planning horizon, and runs in closed loop at online replanning rates, sending body velocities to the motion controller.

  14. arXiv cs.RO58

    FERRET: Using Environment Edits as a Corrective Signal for Reward Learning

    The authors propose environment edits as a complementary form of feedback for robot corrections: with the robot's motion held constant, a human edits the scene so that the only explanation for the edit is the moved object. They introduce FERRET (Feature Learning from Environmental and Robot-Relational Edit Traces), which learns preferences from both robot corrections and environment corrections, and they show that environment edits can disambiguate features and learn preferences that robot corrections alone cannot.

  15. 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.

  17. arXiv cs.RO52

    From Language to Drives: A Multimodal Framework for Aligning Autonomous Robots with Human Intent

    The study proposes a multimodal framework that uses large language models and vision-language models to translate human purposes expressed in natural language into robot missions and drives, aligning the behavior of open-ended learning autonomous robots with human intent. The system has three layers—semantic alignment, mission generation, and drive modeling—forming a bidirectional interaction loop in which human input defines high-level purposes, the robot expands them into executable missions, and feedback maintains alignment. Simulation and physical robot experiments show that the pipeline can generate effective missions and behaviors.

  18. 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.

  19. arXiv cs.RO60

    Context-Aware Generation and Modulation of Expressive Motion Behavior Using Multimodal Foundation Models

    This research proposes a method to automatically generate and modulate complex full-body expressive gesture sequences from multimodal context descriptions spanning text, audio, and video. It implements the principles of animation in a unified way based on Dynamic Movement Primitives (DMP) and uses multimodal foundation models to generate highly context-aware parametric motion variants.

  20. arXiv cs.RO52

    RoboDoctor: A Training-Free Recursive Self-Improvement Framework for Robot Policies

    RoboDoctor is a training-free framework that, under fixed model weights, lets generalist robot policies and language-model agents adapt during execution through three revision targets: diagnosis, action execution, and persistent experience records. On 54 RoboDojo-Sim task entries, the framework achieved a five-dimension mean score of 57.10 and a native success rate of 49.44%; recorded interactions show policy switching, selective correction, and grasp revision. The authors note that these results characterize execution-time adaptation in the evaluated system and that controlled experiments are still needed to isolate the contribution of cross-episode experience.

  21. arXiv cs.RO58

    Distributed Predictive Flocking Control for a Swarm of Miniature Vibration-Driven Robots in a Confined Arena

    The study proposes a distributed predictive flocking controller in which each miniature vibration-driven robot independently solves a nonlinear model predictive control (NMPC) problem and exchanges only predicted trajectories with local neighbors, comparing six barrier and control barrier function (CBF) safety formulations on a digital twin of an open-source vibration-driven bristlebot in a 0.64-square-meter arena.

  22. arXiv cs.RO58

    PIFR: A Benchmark for Preemptive Instruction Following and Task Resumption in Long-Horizon Embodied Navigation

    The authors propose the PIFR benchmark to evaluate preemption and resumption capabilities in long-horizon embodied navigation when a higher-priority instruction interrupts the current task sequence. PIFR includes single, repeated, and nested interruption settings, with stage-specific metrics for inserted-task completion, resumption of the original task, and completion of the full sequence. The authors also propose a lightweight candidate-ranking baseline to improve target re-grounding after interruption.

  24. 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.

  25. arXiv cs.RO55

    RoKON: Efficient and Adaptive Data-Enabled Predictive Control for Robotic Manipulation

    The paper proposes RoKON, a data-driven predictive control method for robotic manipulation that does not require learning an explicit dynamics model. The method uses a Gaussian-kernel nonlinear predictor to represent the input-output data relationship, proposes a damped Gauss-Newton method that exploits the least-squares structure of the control problem to achieve real-time control, and introduces a hybrid buffer management strategy that incorporates informative online data while retaining relevant offline data, allowing adaptation to changes in dynamics.

  26. arXiv cs.RO62

    MATE-BT: A Hierarchically Decentralized Behavior Tree Framework for Mission Autonomy in Heterogeneous Multi-Robot Teams

    The paper proposes MATE-BT, a decentralized framework for heterogeneous multi-robot teams that hierarchically decouples mission execution, task allocation, and task execution. An onboard active leader manages mission progression and team-level task assignment, and authority transfers after a leader failure.

  27. 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.

  28. 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.

  29. arXiv cs.RO58

    VisGeo-PipeTrack: A visual-geometric servo control framework for UAV dynamic pipe tracking

    The paper proposes the VisGeo-PipeTrack servo control framework, which uses YOLOv5 to detect pipe labels and combines visual geometric constraints for localization, enabling UAVs to track pipes with dynamic motion such as vibration. On the control side, it adopts a model predictive control-based real-time corrected adaptive PID (MPC-RCAPID), which estimates the model online via recursive least squares and adjusts PID parameters through rolling optimization.

  31. 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.

  33. 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.

  34. 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.

  35. arXiv cs.RO47

    The Language of Deformation: Semantic Failure Analysis as a Prerequisite for Dexterous Manipulation of Deformable Objects

    The paper proposes that semantic failure analysis is a key step toward mature dexterous manipulation of deformable objects and supports this claim with proof-of-concept experiments. Unlike rigid-object manipulation, cables, plastic parts, cloth, and similar objects exhibit diverse and hard-to-detect failure modes such as buckling, plastic deformation, pressure changes, and tearing due to differences in materials, geometry, and behavior, requiring different corrective strategies. The authors define the components and challenges of semantic failure analysis and present experimental results indicating its potential as a research direction.

  36. 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.

  37. 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.

  38. arXiv cs.RO55

    Factor-Graph-Based 3D Force Estimation for Dual-Arm Manipulation of Flexible Cables

    This study proposes using factor graphs to probabilistically model deformable linear objects (DLOs), estimating the magnitude, direction, and location of 3D forces acting on them while simultaneously reconstructing the cable's shape. Because cables are thin and unactuated, strain gauges and actuation readings are generally impractical; the method uses only one type of sensing information and performs force estimation in simulation tests that add measurement noise and have the cable move along a prescribed path. The authors state that this general method can apply to a broader range of tasks.

  39. 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.

  40. arXiv cs.RO58

    Multi-Candidate Simplex Architecture: From Design-Time Identification to Runtime Construction of Context-Dependent Safe Regions of Attraction

    The study proposes a two-stage Region of Attraction (RoA) certification procedure: contraction-based stability analysis first determines a target set, which Taylor-model reachability analysis then verifies, thereby constructing a context-dependent safe RoA at runtime and enabling safe switching among high-assurance controller candidates.