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#Robot models and learning
Nov 5Thu
  1. arXiv cs.RO57

    H^2MARL: Hierarchical Heterogeneous Multi-Agent Reinforcement Learning Framework for Humanoid Whole-Body Control

    The authors propose H^2MARL, a hierarchical heterogeneous multi-agent reinforcement learning framework for humanoid whole-body control. The method performs policy factorization with physical heterogeneity awareness at the joint degree of freedom (JDoF) level, conditioning each torque-actuated joint on descriptors of its own actuation, kinematic, and dynamic properties, and uses closed-loop policy-executor co-optimization to jointly adapt the high-level pose planner and low-level joint executor with shared interaction trajectories.

  2. arXiv cs.RO62

    Failure-Boundary Profiles: A New Method for Closed-Loop Evaluation of Driving and Robot Navigation Agents

    The study proposes a failure-boundary profile evaluation method in which each scenario family varies only one measurable factor (such as corridor width or the time gap in oncoming traffic), and each variant is labeled from measurement as one where the agent should proceed or one where it should stop. Along this axis, two curves are estimated for each agent: commitment (the probability of attempting the passage) and competence (the probability of passing without contact). The method locates where each curve crosses 0.5 and gives intervals that account for the simulator's run-to-run variance, while also using certified labels to derive a decision accuracy metric.

  3. arXiv cs.RO64

    TwinBridge: Using digital twins to transfer policies and rewards for real-world reinforcement learning in laboratory robotics

    TwinBridge proposes a digital twin-driven framework that provides policy initialization and dense visual rewards for real-world online reinforcement learning. The method builds digital twins by combining 3D Gaussian splatting with physics simulation; simulator states provide task events and geometric progress, which are used for pretraining with simulated demonstrations and for reinforcement learning, then distilled into a visual reward model. Combined with execution history, this computes dense rewards and supports human-in-the-loop policy optimization.

  4. arXiv cs.RO58

    Riemannian Optimization-Based Whole-Body Obstacle Avoidance Framework for Mobile Continuum Robots

    The study proposes a unified planning and control framework for mobile continuum robots, combining a global layer consisting of A* search and minimum-jerk planning with a local layer consisting of a geometry-consistent inverse kinematics solver and Riemannian optimization, to coordinate the mobile base and continuum deformation for obstacle avoidance. The authors compare it with multiple baseline planners in simulated pipe-traversal tasks and conduct real-world experiments on navigation among dense obstacles and manipulation in confined spaces. Results show improvements in motion consistency, feasibility, and adaptability.

  5. arXiv cs.RO56

    Learning manipulation actions from demonstrations: online adaptation to latent physical properties

    The authors propose a learning-from-demonstrations method that jointly learns a dynamics model and object-specific latent representations of physical properties, and conditions an imitation policy on online estimates of these representations, enabling the robot to adjust manipulation actions such as regrasping and contact locations based on physical-property information acquired through interaction. During execution, the dynamics model and policy weights remain fixed, and only the latent representation is updated based on the discrepancy between predicted and actual observations. In real-world and simulation experiments with objects having different centers of mass, online adaptation improved task success rates, but in simulation the success rate remained about 30% at center-of-mass positions not covered by the demonstrations, indicating limited adaptation to unseen physical properties.

  6. arXiv cs.RO65

    Learning Object-Centric Motion Priors from Human Data for Robotic Dexterous Manipulation

    The paper proposes learning to holistically predict future hand-object states from human-object interaction (HOI) datasets, using the predicted future object states as a general-purpose reward term for reinforcement learning, reducing reliance on task-specific reward engineering. The authors experiment on three manipulation tasks in simulation and the real world, reporting better success rates and generalization to novel objects than existing SOTA methods, and validate cross-embodiment compatibility by deploying skills to different robot hands. The work has been accepted as an oral paper at AAAI 2026.

  7. arXiv cs.RO58

    Diversity-Aware Experience Replay via Statistical Leverage Scores

    The paper proposes scoring each transition in the replay buffer with a statistical leverage score and drawing mini-batches from a mixture of this score and uniform sampling, to alleviate the under-sampling of rare events in robot offline experience. When actions are stored as one-hot encodings, the method instead uses the feature column with the largest kurtosis, and it drops the importance-weight correction of prioritized replay, directly optimizing a rebalanced objective; for rare cases related to the target, it applies the same tail rule to short trajectory windows.

  9. arXiv cs.RO58

    RACE: Rollout-Free Calibrated Cumulative Scoring for Robot Policy Checkpoint Selection

    The study proposes RACE, a robot policy checkpoint scoring metric that requires no real-world rollout. It uses a fixed set of held-out successful trajectories to calibrate action deviations against state-conditional thresholds, then applies a running product to reduce the weight of later reference predictions after large deviations. In simulation, RACE outperformed baselines across different validation sources, while some baselines degraded noticeably when the validation set changed; on a real robot, its rankings were consistent with deployment success rates across the evaluated policy backbones and tasks of varying difficulty.

  11. arXiv cs.RO57

    Multi-Robot Closed-Loop Adaptive Sampling Framework Based on a Generative Ocean Model

    The authors propose a multi-robot closed-loop adaptive sampling framework in which a single learned generative model performs forecasting, uncertainty estimation, planning, and measurement assimilation simultaneously. The model generates ensemble forecasts in latent space and decodes them into physical ocean states, uses ensemble statistics to construct A- or D-optimal sampling objectives, selects sampling locations within the robot-reachable range constrained by predicted currents, and feeds measurements back into the latent state through observation-conditioned diffusion sampling.

  12. arXiv cs.RO62

    OdorSpaces: A Visual-Olfactory Embodied Navigation Simulation Platform and Benchmark for 3D Environments

    The authors propose OdorSpaces, a physics-simulation-based platform and benchmark for visual-olfactory embodied navigation. It integrates 3D odor transport and dispersion simulated by Fire Dynamics Simulator (FDS) into Habitat-Sim, enabling agents to navigate realistic indoor environments using both visual and olfactory observations.

  13. arXiv cs.RO61

    PHIL: Proactive human-in-the-loop intervention via preference learning

    The paper proposes the PHIL framework, which lets operators preview a policy's predicted future actions through an XR interface, take over to correct before undesirable actions occur, and pair rejected predictions with corrective actions for preference post-training. On four real manipulation tasks, with five correction episodes per condition, mean success rates rose from 44.0% to 70.3% for the diffusion policy and from 57.8% to 76.9% for X-VLA, compared with the HG-DAgger baseline; policies trained with proactive corrections showed fewer unnecessary recovery behaviors in successful episodes.

  15. Sergey Levine29

    New constraint enables offline-to-online reinforcement learning

    When running reinforcement learning with prior data, a behavior constraint is important for trading off exploration against staying close to known good parts of the data. But choosing this constraint is delicate—we neither want to merely match all prior data nor to deviate too far. In our new paper, we propose a new constraint that combines a support constraint with a continuous penalty to achieve excellent offline-to-online reinforcement learning performance.

  16. Guyuehome58

    OpenFleX: Omnidirectional Mobility and Dual-Arm Coordinated Manipulation Control for a Wheeled Humanoid Robot

    The OpenFleX wheeled humanoid robot reproduces omnidirectional chassis kinematics, damped least-squares inverse kinematics, and whole-body redundant coordinated control in MuJoCo. A single end-effector motion can be achieved through various combinations of chassis, lift, and dual-arm degrees of freedom; the experiments compare pseudoinverse and damped least-squares performance near singular configurations.

  17. Guyuehome58

    SkillMimic uses multiplicative rewards and hierarchical skill control to imitate basketball skills

    SkillMimic uses multiplicative Gaussian kernel rewards to turn pose, interaction, and contact into multiplicative joint conditions, learning 19 types of interactive skills from basketball demonstrations. Its hierarchical version uses discrete skill selection to drive a frozen low-level policy and reproduces four types of tasks on a new-generation simulation platform.

  18. HuggingFace Daily Papers78

    PyRUA-DataFarm: Using LLM agents instead of teleoperation to generate real-ready robot data

    PyRUA-DataFarm proposes having a coding agent rebuild tasks in simulation, with a video model rendering them into real-ready data, replacing teleoperation for collecting robot demonstrations. On five real-robot tasks, policies trained only on this data achieved 93/100 successes, versus 84/100 for policies trained on teleoperated data; under an unseen tablecloth and colored lighting, the former kept 87 successes while the latter fell to 46.

    Why it matters: The paper presents a complete pipeline and cost comparison for using a coding agent to replace teleoperation in collecting demonstrations, which can be used to assess the room for replacing this data-collection route.

  19. OFweek Robotics55

    Weishi Robotics raises tens of millions of yuan seed round, exclusively invested by WeiShi Electronics

    Weishi (Suzhou) Robotics Technology Co., Ltd. has completed a seed round of tens of millions of yuan, exclusively invested by A-share listed company WeiShi Electronics, with Yushi Capital serving as exclusive financial advisor. The funds will be used for iteration and mass-production preparation of the industrial embodied robot V2, R&D of embodied large models and data systems, and first-batch industrial scenario delivery. V2 targets light-load precision operations, with repeat positioning accuracy of ±0.05 mm and an operation yield rate of 99%; the first batch of orders will be prioritized for WeiShi Electronics' backlight module production line.

  20. 机器人全球资讯 · 微信公众号70

    Xiaomi Robotics reports progress on 8-hour factory operations and long-horizon household tasks

    During the 2026 World Internet Conference, Xiaomi disclosed two deployment advances in its robotics business: a humanoid robot took on an 8-hour shift for the first time at a center-console side-cover sequencing station, achieving autonomous anomaly correction and system-level coordination with the production line; an Agent system built on the Xiaomi-Robotics-1 pretrained model is in trial operation at Xiaomi Youth Apartments, completing a long-horizon validation of over 30 consecutive minutes, covering 3 rooms and 50+ tasks.

  21. 图灵人工智能 · 微信公众号34

    Building a Mind for Machines: Zhu Songchun, BIGAI's CUV Architecture, and the General-Purpose Intelligent Agent Tongtong

    At the 2026 World Artificial Intelligence Conference, Zhu Songchun reflected on the large-model approach and argued for replacing pure data fitting with BIGAI's CUV cognitive architecture, promoting the idea of "building a mind for machines." His team has released version 3.0 of its general-purpose intelligent agent Tongtong, upgrading its spatial, cognitive, and social intelligence, and it ranked first overall in eight categories of the team's self-developed General Social AI (GSA) test. This approach still faces three questions: evaluation independence, simulation-to-reality transfer, and a closed commercialization loop.

  23. 机器人前瞻 · 微信公众号72

    LiberAI releases world action model Liber-0, pretrained on 2.2 million hours of human data

    Physical world model startup LiberAI has released the world action model Liber-0, pretrained on over 2.2 million hours of human experience data, converting human manipulation experience into robot manipulation capability. In 60 tasks randomly generated by GPT-6 and 600 real-robot tests, Liber-0 achieved a zero-shot overall success rate of 36.0%, reaching 63.5% on pick-and-place tasks; the GPT-6 Astra control achieved an overall success rate of only 5%.

  24. 未来机器人网 · 微信公众号77

    D-Robotics and University of Tokyo release RawVLA: frozen VLA, only imaging front end trained, low-light dual-arm task success rate rises from 0 to 66.5%

    D-Robotics, together with the University of Tokyo and other institutions, released the RawVLA technical approach, which adds a learnable neural ISP module before the VLA policy while keeping the VLA model frozen, using action error to train the imaging front end in reverse. On real robots, four dual-arm tasks were tested with π0.5; the average success rate in low light rose from 0% with the default ISP to 66.5%, versus 75.0% in normal light, where the default ISP achieved 76.5%.

    Why it matters: The approach brings the imaging front end into the learnable pipeline and provides comparative data under low light and normal light, offering a reference for vision pipeline design.

  25. 未来机器人网 · 微信公众号72

    Xiaomi humanoid robot completes 8-hour full-shift work at car factory and begins apartment pilot operation

    At the 2026 World Internet Conference Wuzhen Summit, Xiaomi disclosed progress in the industrialization of its humanoid robot: it has been deployed at the center console side cover panel sequencing station in Xiaomi's automotive final assembly workshop, completed its first 8-hour uninterrupted full-shift operation, can independently grasp flexible workpieces, adjust posture, and place them neatly, and has autonomous anomaly correction capability.

  26. 未来机器人网 · 微信公众号40

    Wang Xingxing at Wuzhen: 2027 likely the tipping point for embodied intelligence; humans' core value is setting goals and scoring for AI

    At the 2026 World Internet Conference Wuzhen Summit, Unitree founder Wang Xingxing judged that next year is likely the tipping point for the robotics industry or robot models, based on the rapid capability gains of foundation models such as GPT-6. He said the biggest current bottleneck is aligning models with real machines in the "last few millimeters" of precision assembly and flexible manipulation. Unitree is advancing a physical AI robot self-evolution system: AI agents autonomously search papers and open-source solutions to generate control code, which is validated in simulation and then deployed for real-machine testing, followed by closed-loop iteration combining automated evaluation and human scoring.

  27. 移动机器人产业联盟 · 微信公众号58

    Geek+ exhibits Gino 1 humanoid robot, Gravity embodied model and full range of warehouse robots at CeMAT

    At CeMAT ASIA, which opened on November 3, Geek+ is exhibiting its general-purpose humanoid robot Gino 1, the Gravity embodied model, the unmanned picking workstation RAPS, and a full range of robots including RoboShuttle Hyper, SkyCube and RoboPallet, providing on-site evidence for its recently announced 24/7 autonomous intelligent warehouse plan.

Nov 4Wed
  2. 人形大讲堂 · 微信公众号67

    Shunheng Intelligence's SynDexy C1 dual-arm wheeled humanoid robot completes first small-batch delivery

    On October 30, Shunheng Intelligence announced that its self-developed dual-arm wheeled humanoid robot, SynDexy C1, has completed its first small-batch delivery. The customers are in the consumer electronics and electronic components sectors, and the robot is used for precision assembly processes on production lines. The robot has 22 degrees of freedom across its body, with motion control latency reduced to the 1-millisecond level. It is equipped with self-developed visuotactile sensors and the SynDexy M1 visuo-tactile-force tri-state fusion manipulation model. The company disclosed that the success rate for key tasks exceeds 98%. The project was initiated in April this year, the first-generation prototype was completed in July, and delivery took place in October. The company was registered in June 2025, and its chief scientist is Fang Bin of Beijing University of Posts and Telecommunications.

  3. Robohub22

    AIhub coffee corner: Are humanoid robots the future of embodied intelligence?

    The AIhub coffee corner roundtable discussed whether humanoid robots represent the future of embodied intelligence. Participants noted that recent advances in hardware, algorithms, and training methods have driven leaps in capability, and that modern humanoid robots mostly use learning-based controllers and achieve walking through sim-to-real transfer.

  4. Reddit r/humanoidrobotics22

    Neura Robotics launches perception-oriented foundation model neuron ai, discusses role of perception in foundation models

    Neura Robotics has released neuron ai, a perception-oriented foundation model that is said to incorporate more sensory inputs in addition to video and simulation data. The poster believes it will still take a long time to collect enough new data for robots to achieve true multi-task autonomy, but the direction is worth watching.

  5. 36Kr Embodied Intelligence38

    After TypeSafeAI's decision model Jev goes viral, why does the physical world need on-device streaming multimodal decision-making?

    TypeSafeAI released the decision model Jev, which outputs only yes/no, option numbers, or probability scores. Within weeks of launch, users exceeded one million, about one-third of Fortune 500 companies began using it, and its valuation rose from about $200 million to $7.5 billion in 24 days.

  7. Guyuehome29

    Flex-π explained: from multi-stream world prediction to switchable robot policies

    Flex-π targets dual-arm robot manipulation, using a single approximately 6-billion-parameter model to jointly learn actions, future appearance, 3D geometry, and object semantics, then selecting at deployment which content to generate. It addresses the tension that predicting the future can provide more information, yet not all tasks require all prediction content.

  8. Guyuehome58

    BSC-Nav paper analysis: a retrievable spatial cognition approach for embodied navigation

    BSC-Nav is an arXiv paper (2610.02784) aimed at indoor object navigation, long-instruction following, active embodied question answering, and mobile manipulation, addressing the problem that multimodal large models can understand the current view but struggle to support spatial cognition. This analysis article from Guyuehome says it moves embodied intelligence from reacting to a single glance toward retrievable spatial cognition, but currently only the abstract is provided; method details and experimental results are not elaborated.

  9. Guyuehome55

    Review: Three paths for embedding physical priors into robot learning, from classical mechanics to VLA and WAM

    The article reviews three paths for embedding physical priors into robot learning: data and representation, model architecture, and training loss, and emphasizes that where a prior is placed determines its constraining effect. Using grasping a soft object as an example, it explains the layered roles of kinematics, dynamics, contact, and material properties in the task, while noting that physical priors are not a panacea and need to match the task.

  10. Guyuehome58

    SSTG-Nav proposes reusable object navigation framework

    SSTG-Nav proposes a reusable object navigation framework for long-term deployment scenarios, focused on addressing the mismatch between observation poses and docking poses. The framework uses a single task-agnostic RGB-D mapping pass, employing a vision-language model (VLM) for detection and depth back-projection to generate reachable targets. The material provides only an abstract and does not give full experiments or results.

  11. Guyuehome60

    Reproducing PPO Reinforcement Learning-Based Quadrotor Racing Flight Control

    The author loads an open-source pretrained policy in Isaac Sim to reproduce quadrotor racing on a figure-8 track, matching the official result with 25 gates and 3.12 laps. The content breaks down the three attitude phases of high-speed turns and uses a three-seed controlled experiment to test the real benefits of domain randomization.