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#Grasping and manipulation
Nov 5Thu
  1. Reddit r/robotics22

    SmartGrid Mesh Flow feature: How to use vector fields to generate adaptive spray trajectories on 3D mesh surfaces

    A developer is seeking mathematical algorithm advice for the Mesh Flow feature of SmartGrid, an offline robotic spray trajectory planner, with the goal of generating and maintaining consistent trajectory patterns across different 3D mesh geometries. Their concerns include using vector fields to determine travel direction, controlling path spacing with geodesic distance fields or surface offsets, and letting local normals, tangent vectors, and curvature influence trajectories while suppressing jitter caused by mesh irregularities. The current focus is on deriving a general geometric algorithm that can reproduce reference patterns, rather than on programming implementation.

  2. 爱范儿34

    Interview with Stardust Intelligence's Lai Jie: the rope-driven robot, AOS operating system, and Lumo-2 embodied model's "iPhone 4 moment" has not yet arrived

    In an interview, Lai Jie, founder and CEO of Stardust Intelligence, said the company's architecture combines a rope-driven robot body, the embodied operating system AOS, and an embodied model. AOS packages perception, movement, manipulation, and interaction into skill modules, can adapt to large models and Agents such as GPT 6, and lets developers build applications using natural language. Its Lumo-2 embodied model has implicit world dynamics prediction capability and, combined with reinforcement learning, builds a trial-and-error and error-correction loop. Lai Jie believes the robotics industry's "iPhone 4 moment" has not yet arrived, but may be closer than imagined.

  3. arXiv cs.RO58

    Warp: Structured Output-Space Adaptation for Frozen Robot Policies

    The authors propose Warp, a context-conditioned monotone bijective spline adapter that nonlinearly reshapes the sampled outputs of a frozen base policy while preserving per-dimension ordering. Across four ManiSkill tasks, Warp achieves the strongest recovery in a matched evolution-strategy adapter study; after switching to PPO, the ordering relationships vary by task. In a physical SO101 peg insertion task, Warp's success rate is comparable to a strong stochastic residual, with median time per successful cycle reduced by about 30%.

  4. arXiv cs.RO71

    RoboTryOn: Synthesizing Robot Dexterous Manipulation Demonstrations from Human Videos via Virtual Try-On

    RoboTryOn proposes treating human-to-robot translation as virtual try-on: it preserves the demonstrated motion, objects, and background, and only replaces the human with the target robot specified by a reference image. The method autoregressively converts keyframes and uses them to guide video synthesis, with motion conditioned on the human hand skeleton; it then uses an inverse dynamics model (IDM) with depth features to infer action labels from the synthesized videos for policy learning. Experiments show that policies trained only on synthetic data improve average success rate from 2% to 60% on new tasks, including dual-arm manipulation, and reach the level of policies taught by pure teleoperation at about one-third of the collection cost.

  5. arXiv cs.RO60

    Study: How inertia mismatch affects robot policy learning when robots are heavier than objects

    This paper investigates how inertia mismatch between a robotic arm and the objects it manipulates affects robot policy learning. Through controlled simulation experiments that vary robot and object masses, it finds that reinforcement learning succeeds across a broad range of mass combinations, while behavior cloning learns more reliable policies when a lighter robot manipulates a heavier object.

  6. arXiv cs.RO67

    Tactus: A tactile and pose-sensing glove for natural dexterous manipulation data collection

    The authors propose Tactus, a textile-integrated sensing glove that provides both capacitive tactile force sensing and IMU-fused pose tracking with a 1.5 mm fingertip stackup thickness, for collecting natural dexterous manipulation demonstration data. At 95% confidence, the glove's minimum detectable force is 0.59 N, 1.6–2.7 times lower than two commercial tactile glove baselines, with cross-subject consistency of 0.87; under the most severe manipulation occlusion, the relative error of fingertip keypoints is 11.0 mm.

  7. arXiv cs.RO47

    Low-cost 4-DoF arm RoArm-M2-S achieves vision-guided writing tool reorientation and color sorting

    The roughly $200 Waveshare RoArm-M2-S four-degree-of-freedom robotic arm, under a fixed overhead camera, uses a YOLO11n-OBB detector, an ArUco reference pose, and a color classifier to perform color sorting of writing utensils without a wrist rotation joint. For utensils deviating more than 45 degrees from the fixed approach direction, corrective sweeps bring them into a graspable pose within at most 4 attempts. In 196 single-utensil trials, 176 (89.8%) landed in the correct color bin; of the 20 failures, 19 were grasp slips or missed grasps and 1 was a color mislabel.

  8. arXiv cs.RO67

    Reverse to Advance: Learning Hard Manipulation Policies with Reversed Easy-Task Data to Cut Teleoperation Costs

    This paper proposes a teleoperation-cost-effective framework that uses temporally reversed trajectories from easy tasks as supervision signals for hard manipulation tasks, reducing manual demonstration collection. The framework has three parts: a closed-loop data collection pipeline, hierarchical data refinement based on kinematic priors and critic-guided advantage filtering, and iterative policy learning. In two simulation benchmarks and real-robot experiments, the method improved success rates on hard tasks compared with reversal-based and reinforcement-learning baselines, with higher data efficiency and more stable training.

  9. arXiv cs.RO60

    SDN: Improving VLA policy robustness at test time via selected diffusion noise

    The authors propose Selected Diffusion Noise (SDN), a training-free method that treats diffusion noise as a controllable degree of freedom to improve the test-time reliability of VLA policies. SDN samples multiple candidate action chunks and performs two-stage selection: it first filters by task-relevant visual grounding using a counterfactual negative set, then selects the kinematically most stable action chunk based on JerkRMS. In simulation and real-world experiments with three VLA policies, it achieved improvements of 8-10% and 18.33%, respectively.

  10. arXiv cs.RO65

    InDex: Adapting VLA models to dexterous manipulation via intent-conditioned fine-tuning

    The study proposes the InDex framework, which uses a normalized aperture signal derived from retargeted demonstrations to supervise a scalar grasp intent, adapting parallel-gripper VLA models to multi-fingered dexterous hands in two stages. Across four simulated tasks, InDex with π0.5 achieved 85.8% mean task success, a 35.5 percentage point improvement over the native policy, and reached 69.0% on new objects with similar geometry without fine-tuning. It was also effective across three VLA backbones and on a separately trained real-world policy, whose mean success rate rose from 71.0% to 83.0%.

  11. arXiv cs.RO58

    Code as Policy Agents' Self-Repair: What Drives Retry Success

    This study compared retry mechanisms of code-as-policy agents on simulated robot manipulation tasks: on the same 252 initial failure cases, Qwen2.5-Coder-7B, using feedback templates selected by failure labels, recovered 85.3% within five retries, versus 68.3% for resampling with the same retry cap, and the advantage persisted under a fixed total token budget.

  12. arXiv cs.RO62

    Dynamic Shape Control of Soft Trunk Robots Based on aSSM Model and MPC

    The research team learned an adiabatic spectral submanifold (aSSM) model of the full body of a trimmed-helicoid soft trunk robot from data and used it in model predictive control (MPC), completing a range of shape control tasks on hardware. In closed-loop dynamic shape regulation, the method outperformed existing linear baselines by a factor of 10; in human-in-the-loop interactive demos, it completed section-by-section shaping and balanced a tennis ball for more than 25 seconds, whereas the linear baselines failed to complete the task. A model-free reinforcement learning policy trained on hardware for 12 hours failed on the same balancing task.

  13. arXiv cs.RO72

    soccerbot: Learning Humanoid Soccer Skills with Latent Motion Priors

    The authors propose soccerbot, a hierarchical reinforcement learning framework that distills a structured latent motion prior from large-scale human soccer motion capture data, constraining policy learning within a dynamically feasible motion manifold. A single skill-conditioned policy thereby learns moving, dribbling, trapping, and kicking in a unified way, while maintaining human-like motion and fluid transitions during skill switching. After robustness-oriented simulation design, the framework transfers zero-shot to the Unitree G1 humanoid robot, completing the full soccer skill set in real-world environments.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Guyuehome58

    Dexterous Hand Practice 13: Using Polynomial Trajectory Planning to Eliminate Position Jumps and Achieve Smooth Motion

    The 13th post in Gu Yue Ju's "Dexterous Hand Practice" series explains how to use polynomial trajectory planning to turn position jumps in a dexterous hand into smooth motion. The material provides only the title and summary; it does not give specific polynomial orders, parameters, or experimental data.

  19. 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.

  21. 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.

  22. 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%.

  23. 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.

  24. 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. New Strategy Robotics60

    Youibot Semiconductor Mobile Manipulator Robot Project Wins First Prize of Guangdong Provincial Technology Invention Award

    The project "Key Technologies and Systems for Highly Stable Mobile Manipulator Robots for Semiconductor Manufacturing," led by Youibot, won the first prize of the Technology Invention Award at the 2025 Guangdong Provincial Science and Technology Awards. The project claims breakthroughs in three areas: material vibration control, wide-range navigation and positioning with end-effector grasping precision, and integrated "hand-eye-foot" safety control. The OW series and ATS series mobile manipulator robots have entered more than ten wafer fabs in China and abroad, integrating into 8-inch and 12-inch wafer and 28nm chip production processes and operating 24/7.

  3. 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.

  4. 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.

  5. Guyuehome38

    Dexterous Hand Practice 12: Where the Fingertip Should Go and How to Solve Joint Angles via Inverse Kinematics

    The 12th installment of the Dexterous Hand Practice series focuses on solving inverse kinematics: first determine the target position of the fingertip, then solve backward for each joint angle. The content is tutorial in nature, centered on the correspondence between fingertip targets and joint angles, and does not involve specific robot models or experimental data.

  6. 未来不远 · 微信公众号76

    Future Not Far releases Self-Evolving WAM self-evolving world-action model

    Future Not Far released the Self-Evolving WAM self-evolving world-action model, integrating joint world-action modeling, interactive experience learning, and training-effect feedback into a single system. In update attempts from round 0 to round 30, all four groups of household tasks showed gains in autonomous full-completion success rates: folding clothes rose from 54.6% to 96.8%, hanging clothes from 46.2% to 91.2%, making pasta from 42.7% to 89.5%, and stir-fried pork with chili peppers from 38.2% to 68.9%.

    Why it matters: The original text gives the success-rate changes for four groups of household tasks from round 0 to round 30 updates, showing the practical benefits of the self-evolving training loop.

  7. 月泉仿生 · 微信公众号58

    Yuequan Bionics' YingShou Y-Hand M2 debuts at Hong Kong Fintech Week, demonstrating human-like manipulation with 38 degrees of freedom

    Yuequan Bionics exhibited its YingShou Y-Hand M2 bionic dexterous hand at Hong Kong Fintech Week, stating that it has 38 degrees of freedom, a whole-hand grip strength of 330N, a five-finger closing time of 0.2s, and a single-fingertip repeat positioning accuracy of 0.04mm. The hand can perform all 33 categories of human-like dexterous manipulation with a 96% success rate, and when mounted on a humanoid robot platform it can adaptively grasp square, spherical, irregularly shaped, and thin, brittle, or soft objects. The company also introduced its integrated software-hardware in-house development system, which is built on core components such as motors and artificial muscle modules and extends to dexterous hand manipulation units and bionic full-robot platforms.

  8. Reddit r/robotics18

    SoftSync FlexHand bionic tendon-driven hand featured on CCTV-4's Youth Innovation+

    The SoftSync FlexHand bionic tendon-driven hand was featured on CCTV-4's national science entrepreneurship program Youth Innovation+. The hand is designed with a 1:1 human hand appearance and offers softness, safety, waterproofing, and shock absorption; under the same pose and motor settings, it relies on compliance to adapt to objects of different sizes, enabling pinching and three-finger grasping.

  9. 量子位42

    Kinetix AI Showcases Full-Stack Matrix Including SMASH Ping-Pong Robot and 117-DOF Humanoid KAIBot at Hong Kong FinTech Week 2026

    Kinetix AI presented its full-stack embodied intelligence matrix at Hong Kong FinTech Week 2026, including the high-dynamic humanoid ping-pong system SMASH, the 117-DOF humanoid robot body KAIBot, the 37-DOF dexterous hand KAI Hand, and the first-person data collection headband KAI Halo.

  10. 锐曼REEMAN38

    Planning a multi-floor autonomous forklift project? REEMAN Ironhide elevator integration checklist

    From a supplier perspective, REEMAN provides a five-item checklist of mechanical prerequisites, communication architecture, and responsibility division for autonomous forklift and elevator integration. For a single elevator, a local LoRa direct connection to the elevator control module is used; for multiple elevators, MQTT is used with the fleet system queuing and dispatching. The elevator control module only coordinates calls and status and does not replace the elevator's native safety controls. In the Maoming factory case, Ironhide transports about 1,000 kg of wire rod per trip over a one-way distance of about 100 m, completing 65–85 cross-floor elevator tasks per day.

  11. 仙工智能42

    Seer Robotics Showcases Robot Brain at CeMAT ASIA 2026: A Unified Platform for Multi-Form Robots

    At CeMAT ASIA 2026, Seer Robotics exhibited its Robot Brain product line and several robots equipped with the platform, including the X1 PRO humanoid robot autonomously completing a water-fetching task, the P300 and P1500 AI delivery robots collaboratively avoiding obstacles, and an omnidirectional stacker forklift autonomously identifying and picking up various pallets. Robot Brain consists of three parts: Model, Cerebellum, and Neural pathways. It has been adapted to more than 400 types of core components and supports more than 2000 robot SKUs.

  12. Robotics & Automation News71

    LiberAI launches Liber-0 world action model, pretrained on 2.2 million hours of human experience

    LiberAI has released Liber-0, a world action model pretrained on more than 2.2 million hours of human experience, combining large-scale human video, hand motions recorded with data gloves, and robot demonstrations, with the goal of giving robots generalizable manipulation capabilities. In an eight-task evaluation, a policy trained with 25% robot demonstrations plus 100% glove demonstrations scored 75.6 on progress, higher than the 72.8 scored using only all robot demonstrations. In a 60-task zero-shot evaluation, Liber-0 achieved a 36% success rate.