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Oct 28Wed
  1. arXiv cs.RO36

    Integrated Design and Reproducible Feasibility Analysis of a Four-Wheel Rover: Packet-Commanded Imaging, LiDAR Ranging, and Learning-Assisted Target Recognition (NASA HERC 2027)

    For the high-school remote-control division of the 2027 NASA Human Exploration Rover Challenge, a study presents an integrated, reproducible design assessment of a four-wheel educational rover: 10.919 kg, 215 mm wheels at 200 RPM give a theoretical rolling speed of 8.105 km/h, and the nominal motor specifications are insufficient to demonstrate compliance with the 8 km/h design ceiling.

  2. arXiv cs.RO58

    Study applies magnetic inclination and intensity gradient flow to GPS-free navigation

    An arXiv paper proposes modeling bio-inspired magnetic navigation (BIMN) as a gradient flow control and guidance problem, using magnetic inclination and intensity of Earth's magnetic field to guide a system through intermediate waypoints to a destination within a predefined region. The authors note that magnetic coordinates do not map one-to-one to geographic locations and are ambiguous, but that gradient flow can act as a "water current" carrying the system's motion and be steered. The framework could be used in engineering to build geomagnetic navigation infrastructure and to define and follow prescribed routes, and also to analyze animal migration routes that rely on magnetoreception.

  4. arXiv cs.RO62

    Torch-Hand: A 21-DOF Linkage-Driven Humanoid Dexterous Hand

    The research team proposed Torch-Hand, a humanoid dexterous hand featuring an all-linkage transmission architecture with 21 degrees of freedom, including 5 DOF in the thumb and 4 DOF in each of the other four fingers. The hand integrates its actuators into the palm in a serial-robot-like arrangement, so that motor motion corresponds directly to joint kinematics while balancing grasp force and payload capacity. Experiments evaluated the reachable workspace, fingertip coordinated trajectories, thumb opposition, coverage of 33 grasp categories, grasp force, payload, and task-level manipulation.

  5. arXiv cs.RO58

    An Auditable Framework for Autonomous Driving Laboratories: LAM, OpenSDL, Safe-SDL, and L3-Bench

    The authors propose a traceable framework comprising four components—LAM, OpenSDL, Safe-SDL, and L3-Bench—to connect experiment planning, automated execution, and measurement feedback. LAM is a measurement-conditioned policy architecture that extends the vision-language-action paradigm to generate both continuous robot trajectories and typed instrument API calls; OpenSDL maps role-level operations to heterogeneous devices and records timestamped causal events.

  6. arXiv cs.RO55

    Learning Side-by-Side Navigation for Personal Mobility Vehicles from Human Drivers

    The paper proposes a method for enabling two personal mobility vehicles (PMVs) to drive autonomously side by side: after a global path is given, each vehicle runs its own sampling-based model-predictive planner, the two exchange only the odometry information each can observe of the other, and a common rule is used to make a joint decision on a utility matrix over pairs of candidate trajectories.

  7. arXiv cs.RO56

    Study: Effects of Non-Rigid Bases on Mobile Manipulators in Sustained Contact Tasks

    Using a sanding task as a case study, the paper combines simulation with experiments on two lightweight, unactuated wheeled testbeds to characterize non-rigid base effects in mobile manipulators. Under low base-ground resistance, only 54-70% of a 100 N command was realized, with tool-center-point errors reaching 100 mm; increasing base-ground resistance during contact improved force realization and arrested further tool displacement, while base vibration and force-tracking error showed no consistent relationship under the tested conditions. The study was conducted by Zongyuan Zhang et al. and has been accepted to ACRA 2026.

  8. arXiv cs.RO67

    TOPS: Reshaping Flow Source and Transport for Multi-Task Robot Manipulation

    TOPS proposes a task-oriented prior and scheduling method that adapts the flow-matching source prior and interpolation schedule for each task without modifying the network architecture. On LIBERO-10, single-step inference raises the success rate from 53.44% for standard flow matching with ten steps to 69.67%, with inference time about one-sixth of the baseline. The method improves performance on LIBERO, LIBERO-Plus, and CALVIN, and when transferred to the π0.5 policy, and on a real robot it raises the success rate on an upper-drawer-opening task from 20% to 80%.

  9. arXiv cs.RO60

    RoboSTD: A Bimanual Learning Method for Zero-Shot Single-to-Dual Transfer via Sagittal Mirroring

    RoboSTD is a zero-shot single-to-dual data construction method for bimanual robot learning. It applies sagittal-plane mirroring to observations, proprioception, and actions to generate executable contralateral pseudo-demonstrations, then uses an LLM to generate structured spatio-temporal coordination constraints that guide the reconstruction of pseudo-bimanual supervision across workspace regions and task stages. The study evaluates RoboSTD on the AgileX ALOHA platform in RoboTwin simulation and real-world settings. Without additional bimanual demonstrations, it achieves an average of 2.11 times the number of cups collected by the single-arm baseline in the cup-collection task.

  10. arXiv cs.RO60

    From Biological Curves to Modular Soft Robots: Learning-Based Inverse Design with Uncertainty-Guided Refinement

    The paper proposes a learning-based real-to-sim-to-real framework that converts biological curves into cable-driven modular soft robot configurations through surrogate-assisted inverse design and uncertainty-guided refinement. The learned forward surrogate reduces deformation prediction time from 1.53×10^5 ms to 0.0008 ms, a speedup of about 1.9×10^8 times; inverse design time drops from 1.99×10^6 ms to 4.18 ms.

  11. arXiv cs.RO58

    SWIM: Learning State Weights from Behavior to Train JEPA World Models

    SWIM proposes using an inverse dynamics model to learn state feature weights from offline trajectories for state-supervised training of JEPA world models. The method predicts recorded actions from current states and observed future displacements, quantifies each state feature's contribution to action prediction, and uses this to build a weighted state metric that aligns the latent space with state distances. The inverse dynamics model is used only during training; planning still relies solely on images. On five simulated robotic manipulation tasks, SWIM's planning success rate consistently outperforms uniform state weighting.

  12. arXiv cs.RO67

    HumiTransfer transfers human-human interactions from in-the-wild videos to humanoid robots

    The authors propose HumiTransfer, a framework that converts in-the-wild human-human interaction videos into physically feasible human-humanoid interaction motions. The method first reconstructs 3D human motion with foot-ground contact, then transfers one participant to the target humanoid embodiment through interaction modeling, and uses post-retargeting optimization to ensure balance, contact, and collision avoidance.

  13. arXiv cs.RO58

    QuadPose estimates full-body 18-DoF pose of quadruped robots from monocular video sequences

    The authors propose QuadPose, which estimates the full-body 18-DoF pose of a quadruped robot from monocular video sequences, including the 6-DoF base pose and 12-DoF joint angles, and release QuadSyn, the first camera-to-quadruped robot pose benchmark. On QuadSyn, QuadPose achieves 92.18% PCK@2.5px and 85.94% ADD@20cm, with a mean joint-angle error of 3.65 degrees and a runtime of about 33 FPS.

  14. arXiv cs.RO71

    OpenJaws: An open-source hardware UMI framework for generalist manipulation, using stereo localization to improve demonstration accuracy

    OpenJaws is a fully open-source sensorized UMI hardware and software framework that pairs a modular 3D-printed gripper with a low-cost stereo camera to recover metric camera trajectories, improving action-label localization accuracy in handheld demonstrations. The authors also propose UMI-Loc-Bench-v1, a benchmark for quantifying the real-world localization accuracy of handheld data-collection devices, and show that stereo localization clearly outperforms monocular alternatives. Policies trained on data collected with OpenJaws have been deployed on real robots across multiple tasks, and the hardware, software, and data are released under a commercially friendly license.

  15. arXiv cs.RO71

    REACH: An RL-based hierarchical humanoid crawling framework for confined-space traversal

    REACH is an RL-based hierarchical framework that enables a Unitree G1 humanoid robot to traverse height-constrained environments through variable-height bipedal walking and crawling. Its policies regulate body height over complementary command ranges: 1.27–0.50m for bipedal walking and 0.50–0.20m for crawling, and a bidirectional transition policy connects crouched walking and crawling. The framework also combines a LiDAR planner that converts point clouds into collision-free three-dimensional paths and locomotion commands. Real-world experiments validated height and speed tracking, terrain traversal, mode switching, and confined-space traversal; the code and videos have been released.

  16. arXiv cs.RO64

    CARD: A Contact-Anchored Retargeting Framework for Dexterous Manipulation

    CARD is a simulator- and object-model-free framework that retargets human demonstrations into robot actions by explicitly preserving contact force targets, and it handles video, motion capture, and tactile glove data in a unified way. On video, glove, and motion-capture data and the GRAB dataset, CARD maintains contact on 43%–78% of contact frames, whereas purely kinematic and object-aware retargeting methods stay below 10% and 22.5%, respectively.

  18. arXiv cs.RO58

    MyoAcross: A Generalist Muscle-Driven Control Policy for Musculoskeletal Models Across Individuals

    The study proposes the MyoAcross framework, which combines individual musculoskeletal model construction, morphology-conditioned policy learning, and virtual model augmentation to achieve muscle-driven reference-motion tracking without per-individual fine-tuning. Across 31 held-out individual models, it achieved a 99.24% completion rate and reduced tracking error by 22.60% relative to an unconditioned multilayer perceptron. Policy reuse also extended to 22 held-out individual models constructed from single-image shape estimates, height, and body mass, as well as to a model with unilateral lower-limb shortening after asymmetry augmentation.

  19. arXiv cs.RO69

    RoboAuto: Autonomous Robot Program Search through Multi-Level Physical Understanding

    RoboAuto proposes a robot program search framework that organizes physical understanding at three levels—object, scene, and interaction—to guide LLM agents in generating and revising manipulation programs. The method achieves better performance across five benchmark suites, reaching an 89.1% success rate on short-horizon LIBERO-PRO tasks and averaging more than twice the success rate of ASPIRE on LIBERO-PRO Long and LIBERO-Plus.

  20. arXiv cs.RO62

    GRAFT: Geometry-Aware Residual Adaptation for Frozen Humanoid Behavior Trackers

    GRAFT proposes a geometry-aware residual adaptation framework that, without changing the parameters of a frozen humanoid behavior foundation model (BFM) or its motion-reference interface, uses a lightweight adapter to apply terrain-conditioned corrections only to lower-body execution targets. The adapter is trained with task and foot-geometry rewards, is regularized so it produces no unnecessary corrections on flat ground, and is distilled from privileged terrain observations to onboard depth.

  21. arXiv cs.RO67

    SVIn+: An Underwater State Estimation Framework Integrating 3D Sonar, Visual-Inertial, Water Pressure, and DVL

    SVIn+ is a multi-sensor state estimation framework for autonomous underwater vehicles (AUVs). Building on the open-source visual-inertial SLAM system SVIn, it integrates water-pressure and DVL measurements into tightly coupled nonlinear optimization and uses a KISS-ICP decoupled front end to generate relative pose factors, incorporating 3D sonar structural information into the factor graph. The authors conducted field deployments in shipwreck and coral reef scenarios, validating drift suppression and global consistency. The paper was accepted to IROS 2026, and the code and datasets have been released on the project website.

  22. arXiv cs.RO71

    Whole-Body Humanoid Control via Stochastic Motion Tokenization

    This study proposes using a stochastic motion tokenizer to generate concise discrete motion representations that can be fed directly into a whole-body tracking policy to execute dance, fighting, and walking motions on the Unitree G1. At matched capacity, the tokenizer reconstructs Unitree G1 motions better than FSQ and VQ-VAE. In real-robot demonstrations, each generated motion lasted 16.8 seconds, and a 35-second fighting-to-dance sequence was completed.

  23. arXiv cs.RO49

    GSD-Constrained k-Coverage Viewpoint Planning via Set Multi-Cover Formulation

    For multiple observations under image-quality constraints in drone-based 3D reconstruction and inspection, where overlap-based planning heuristics cannot guarantee such observations, this work models GSD-constrained k-coverage viewpoint selection as a set multi-cover problem. Candidate viewpoints are generated along outward surface normals, screened by distance, viewing angle, field of view, and occlusion, and then a dynamically weighted greedy heuristic prioritizes coverage gaps and scarce viewing angles. Across two structures, four voxel resolutions, and three coverage requirements, the method achieves full or near-full k-coverage, using only 3.00%–16.39% more viewpoints than ILP.

  24. arXiv cs.RO62

    HIDRA: A Hierarchical Dual-Routing Attention Framework for Replay-Free Lifelong Imitation Learning

    The authors propose HIDRA, a hierarchical dual-attention routing framework for replay-free, single-pass lifelong imitation learning that decouples instruction-level expert retrieval from context-dependent refinement. The method introduces key-level geometric regularization that constrains within-task alignment and between-task separation to mitigate routing interference under representation drift. On several LIBERO benchmark suites, including variants with heterogeneous task sequences and paraphrased language instructions, HIDRA outperforms replay-free and replay baselines on AUC and forward transfer while maintaining low forgetting, and the code is publicly available.

  25. arXiv cs.RO58

    SG^2SLAM: Spatial Geometry-Constrained 3D Gaussian Splatting SLAM for Monocular Outdoor Navigation

    The authors propose SG^2SLAM, a 3D Gaussian Splatting SLAM method for monocular outdoor navigation that replaces per-frame cross-dimensional correspondence with a chunk-level metric prior, so that tracking and mapping share the same geometric source. The method also introduces a ray-conditioned 3DGS kernel that extends each primitive with a viewing-direction subspace, and jointly optimizes spatial geometry and poses. On the KITTI and Waymo Open datasets, SG^2SLAM outperforms per-frame association baselines in the monocular outdoor navigation task. Code will be made publicly available upon acceptance.

  26. arXiv cs.RO42

    ASPEN: Adaptive Semantic Reasoning via Command Persistence Prediction to Improve Navigation VLA Efficiency

    ASPEN is an event-triggered scheduler based on command persistence prediction, designed to reduce repeated semantic reasoning calls in navigation VLA policies. On frozen NaVILA in the cross-dataset RxR-CE val-unseen evaluation, ASPEN reduces VLA calls by 9.5% and estimated inference time by 8.1% (including scheduling overhead), while preserving action sequences identical to the baseline in 92.1% of episodes.

  27. arXiv cs.RO65

    Learning Multi-Phase Robot Loco-Manipulation via Phase-Conditioned Policy Optimization

    The study proposes extending PPO into phase-conditioned policy optimization, using a phase-conditioned critic, phase-gated rewards, and a multi-phase reset curriculum to train a single policy for long-horizon loco-manipulation tasks. The method adopts a decoupled hierarchical control architecture that separates low-level balancing dynamics from high-level phase execution, and validates multi-stage skills on real hardware on the Unitree G1 and a Boston Dynamics Spot equipped with a manipulator arm, while testing design choices through ablation experiments.

  28. arXiv cs.RO67

    cuArms: A GPU-Accelerated Conflict-Based Planner for Multi-Arm Manipulation

    cuArms is a conflict-based planner co-designed with GPU parallelism. It decomposes the search space using reusable multi-modal roadmaps and resolves conflicts and deadlocks through lazy search. It integrates with a high-level scheduler to jointly optimize task assignment, scheduling, motion, and manipulation skills. Experiments report real-time initial solutions and anytime improvements in motion quality and scheduling, without domain training for learning-based methods. The authors say it provides demo videos on both simulated and real industrial tasks.

  29. arXiv cs.RO55

    Program-Level Adversarial Co-Adaptive Self-Play for Robot Security

    The paper proposes a program-level adversarial co-adaptive self-play framework that lets coding agents generate attacks and defenses based on opponent code and task feedback, without model fine-tuning. The framework provides external program interfaces for autonomous driving and SDK-level defense interfaces for humanoid robot locomotion; two eight-generation case studies show that the driving defender introduced direct-actuation recovery, while the humanoid defender revised incoming-state validation. These responses provided only local task recovery and did not establish sustained robustness.

  30. arXiv cs.RO72

    Rollback: Using World-Model Experience Backtracking to Improve Robot Policies

    Rollback proposes a policy-improvement paradigm in which a world model rewinds to critical decision points, branches to try different actions, and compares the outcomes, distilling counterfactual results into action-level supervision without a separately learned value model. On three real-world tasks—block extraction, ball throwing, and tabletop curling—success rates improved by 10.67%, 11.11%, and 11.85% in absolute terms over the strongest baseline, and ball throwing and tabletop curling surpassed the average human player success rate within 8 hours of training.

  32. HuggingFace Daily Papers65

    JAM: A Unified Action Model Framework Decoupling Backbone, Objective, and Inference Mechanism

    The study proposes the Just Action Model (JAM) framework, which breaks down the differences between VLA and WAM into three separable choices: backbone initialization, the future-observation objective, and whether that loss mechanism is retained at inference. It uses a pretrained visual backbone plus an action expert, trained with an action loss and a foresight loss.

Oct 27Tue
  2. 机器之心 · 微信公众号58

    UIUC, Google, Stanford and others jointly review how Proactive AI can move toward superintelligent agents

    Researchers from UIUC, Google, Stanford, UC Berkeley and CMU jointly published the review "Proactive AI for Superintelligent Agents," systematically mapping the research landscape of proactive AI and proposing a 6A framework composed of six capabilities: Awareness, Anticipation, Agenda, Arbitration, Action and Adaptation.

  3. 机器之心 · 微信公众号76

    Ropedia Releases Six Research Results, Exploring How First-Person Human Data Can Be Turned into Robot Capabilities

    Ropedia released six studies at the first Ropedia Super Week, held October 19 to 23, centered on how first-person human video can be turned into training and execution information usable by robots.

    Why it matters: The six studies collectively address how first-person human data can be converted into training and execution information usable by robots, and present cross-embodiment and real-robot comparison results.

  4. CVer · 微信公众号72

    FastHarness: A Robot Execution Framework That Schedules LLM Review Using Task Experience

    FastHarness is an experience-driven robot execution framework that uses π0.5 to generate actions and GPT-6 Astra to review and correct them, adjusting the review interval according to task stage and past review results. Across four simulated manipulation tasks in RoboDojo, it achieved an average success rate of 97.19%, higher than the 80% reported for π0.5 + GPT-6 Astra in the GPT-as-Policy report.

  5. CVer · 微信公众号77

    Tsinghua AIR proposes OxyGen: unified KV Cache management lets robots talk while acting

    The Institute for AI Industry Research (AIR) at Tsinghua University, together with Tsinghua's Department of Electronic Engineering and the University of Science and Technology of China, has proposed OxyGen, which uses unified KV Cache management to let an embodied model's action and language reasoning share prefix computation. The paper has been accepted to NeurIPS 2026.

    Why it matters: The paper presents a unified KV Cache management scheme and measured data across multiple platforms, offering a reference for on-device embodied inference deployment.