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#Data and simulation
Nov 3Tue
  1. Guyuehome47

    [Flexible Simulation] Dynamic unfolding control based on MuJoCo cloth dynamics

    Based on MuJoCo cloth dynamics, twelve controlled experiments were completed using realistic shell-element simulation to study differences in cloth unfolding under the same gripping trajectory. Flicking unfolds the cloth through inertia; the slow setting spreads it most fully, while too fast a motion flings it off the table edge. Unfolding performance is insensitive to stiffness, and overdamping instead produces creep.

  3. Guyuehome57

    WAM: Training a world model with quadruped robot locomotion data for navigation planning

    WAM trains a forward-dynamics world model on quadruped robot locomotion data collected under convex model predictive control, replaces hand-crafted steering laws with receding-horizon sampling planning, and combines A* global paths with dynamic obstacle extrapolation to achieve navigation and obstacle avoidance. The material states that all numerical values come from local reproduction experiments.

  4. Guyuehome58

    dex-retargeting: Vector retargeting optimization from human hands to dexterous hands

    dex-retargeting analyzes motion retargeting from human hands to robotic dexterous hands, using a Huber loss on wrist-to-fingertip bone vectors as the objective for per-frame numerical optimization. DexPilot builds on this method by adding grasp detection, projected targets, and temporal low-pass filtering, with the Allegro and Shadow hands converging per frame at millisecond-level speed.

  5. OFweek Robotics65

    Jianzhi Robotics completes $100 million Series A+ funding, positioning itself as embodied intelligence data and evaluation infrastructure

    Jianzhi Xinchuang (Beijing) Robotics Technology Co., Ltd. announced the completion of a $100 million Series A+ funding round in Q3, with investors including SAIC Motor, Baidu Group, Shoucheng Holdings, and other industrial parties as well as multiple financial institutions. Founded on May 29, 2025, the company positions itself as a data and evaluation infrastructure service provider for embodied intelligence and world models. Founder and CEO Chen Jianxing previously served as a senior algorithm director at Momenta.

  6. OFweek Robotics62

    Jianzhi and GenRobot each raise $100M Series A+ on the same day; RoboScience secures Series A2 in the hundreds of millions of yuan

    On November 2, Jianzhi Robotics and GenRobot AI each completed a $100 million Series A+ round on the same day, and RoboScience secured a Series A2 round in the hundreds of millions of yuan. All three investments went to embodied data companies that do not build their own robot bodies. Jianzhi says it has raised over $300 million cumulatively, with effective data deliveries rising from 100,000 hours per quarter to one million hours; RoboScience says it has cut the cost per training data item to 0.05 yuan. The author argues that body, large-model, and data companies are being priced in separate tiers, but the conversion rate of one million hours of effective data and continued customer purchasing remain unclear.

  7. 锐曼REEMAN27

    Forklift Automation: A Step-by-Step Guide to Integrating REEMAN AMRs into Existing Warehouse Management Systems

    REEMAN autonomous forklifts use laser SLAM navigation and can connect to existing WMS, MES, and ERP systems without magnetic strips or QR codes; the integration focus is on location mapping and open API connectivity rather than rebuilding navigation infrastructure. The guide covers site surveys, map creation, task triggering, and multi-vehicle fleet scaling. Each model is rated for 6 hours of continuous operation (fully loaded)/8 hours (empty), supports automatic return to charging, and some models allow manual battery swapping.

  8. HuggingFace Daily Papers67

    H2RB co-trains with human demos and robot data to adapt VLA policies

    The study proposes H2RB, a human-robot co-training framework that adapts pretrained VLA policies on unpaired human and robot demonstrations through geometry-anchored alignment and a decoupled-decoder tokenizer. In real-world tasks, object transfer performance improved from 8.0 to 51.8, and the skill transfer score reached 62.7, while the co-training baseline completely failed to transfer human demonstration skills. Out-of-distribution transfer under object placement changes also improved from 6.0 to 45.3.

  11. 机器人前瞻 · 微信公众号72

    Jianzhi Robotics completes $100 million funding round with SAIC and Baidu participating

    Jianzhi Robotics announced the completion of a new $100 million funding round (approximately RMB 665 million), with industry participants including SAIC Motor, Malaysia's Eastworlds, Baidu Group, Shoucheng Holdings, and GALBOT, and financial participants including Guanghe Ventures, SUNHELM, CITIC Jinshi Investment, Zhongzheng Investment, CCB International, and Huatai Zijin.

  12. 甲子光年 · 微信公众号38

    In conversation with Zhipu, Galbot, Guanglun Intelligence, and Deepexi: Will large language models eventually eat embodied models?

    At the "Qinghe Night Talk" roundtable, Zhipu CEO Zhang Peng, Galbot CFO Sun Dengke, Guanglun Intelligence's Yang Haibo, and Deepexi Technology's Zhao Jiehui discussed whether large language models will eat embodied models. Sun Dengke drew an analogy to fast and slow systems, arguing that embodied models handle S1/S0-level manipulation, planning, and environmental understanding, and may require a smaller S2 parameter scale than language models; Zhang Peng argued that intelligence will ultimately converge. Guanglun Intelligence positions itself as simulation-based physical AI data and evaluation infrastructure, while Deepexi Technology has launched DeepWorks, which can be tried for free.

Nov 2Mon
  1. Reddit r/robotics58

    Robocensus launches directory of 74 walking robots, opens CC BY dataset

    Nick created the Robocensus directory, cataloging 74 walking robots across humanoid, quadruped, and biped types, recording specs, prices, and availability status, and releasing machine-readable JSON data under a CC BY license. The directory annotates context for each spec, such as distinguishing walking, running, and rolling for speed, marking interval values as maximum or minimum, and attaching date and currency to prices; fields not disclosed by manufacturers are left blank rather than estimated. Updates are handled by a scheduled agent that scrapes each robot's official page, compares snapshots, and submits pull requests, which go live only after author review and merging.

  2. 机器之心 · 微信公众号74

    Zhejiang University and TeleAI propose GR-BC, using graph search to select effective actions from robot trial and error

    The team led by Chunhua Shen at Zhejiang University, together with the China Telecom Artificial Intelligence Research Institute (TeleAI) and Chenjia Bai's team at Gamma Intelligence, proposed GR-BC, which builds multiple attempts at the same task into a graph, uses reverse Dijkstra search to estimate the remaining steps from each state to success, and then weights action chunks accordingly to train a vision-language-action model.

  4. 机器人产业应用 · 微信公众号58

    LLMs Enter Embodied AI: Competing for Physical Data or the Next-Generation Intelligent Gateway

    The article analyzes the different paths taken by large-model companies such as OpenAI, Google, Alibaba, and DeepSeek in entering embodied AI, arguing that their primary driver is their own growth and narrative needs, the second is defensive positioning, and that data and gateway are long-term goals not yet realized.

  6. CVer · 微信公众号60

    NTU and other teams present MemSkill at NeurIPS 2026, letting agents learn and evolve memory skills

    A research team from Nanyang Technological University and other institutions presented MemSkill at NeurIPS 2026, packaging memory operations into learnable, reusable, and evolvable Memory Skills, optimizing skill selection through task feedback, and revising and expanding the skill library based on failure cases.

  7. 人形大讲堂 · 微信公众号62

    AgiBot proposes Worken robot energy-efficiency unit, discloses CEL collective experience learning validation results

    At its 2026 Innovation Day, AgiBot's Peng Zhihui proposed the robot energy-efficiency unit Worken, defined as the number of active robots × autonomous working hours × output efficiency ratio, to measure how much work robots actually do. At the Longqi factory, 8 robots worked a cumulative 528 hours with an output efficiency ratio of 0.7, for a total of 2,956.8 Worken; at Guangzhou Metro, 120 robots worked 240 hours with an efficiency ratio of about 0.5, for a total of 14,400 Worken.

  8. 深蓝AI · 微信公众号57

    DeepBlue Embodied Intelligence reviews six embodied AI approaches: modular planning, skill orchestration, imitation learning, reinforcement learning, VLA, and world models

    DeepBlue Embodied Intelligence examines the debate over embodied intelligence approaches along six main technical lines: modular planning, skill orchestration, imitation learning, reinforcement learning, VLA, and world models, drawing on 12 highly cited surveys from the past decade. The article notes that each approach answers the question of what robots should learn from, that the classic problems have not disappeared, and that what has changed is that the technical units for solving them are growing larger. It also mentions an industry estimate that general-purpose robots would need 100 million hours of data to reach a 70%–80% baseline success rate, and that more than 100 open-source embodied foundation models appeared worldwide in the past 18 months.

  9. Reddit r/robotics18

    Indian optical mocap studio seeks input: what human motion data is still missing for humanoid robot training?

    An Indian optical mocap studio offering body and finger capture and multi-person simultaneous capture is beginning to collect human motion data for humanoid robots, including ground transitions, stairs, and household chores. Before deciding on collection directions, the studio asked the community about gaps in public AMASS-type datasets regarding finger detail, object interaction, annotation, and capture scope.

  10. 北京理工大学 · 微信公众号36

    CCTV features BIT swarm intelligence research group: "BIT Zhixingzhe" humanoid robot and quadruped robot dog unveiled

    The swarm control research group at the School of Mechatronical Engineering, Beijing Institute of Technology, presented platforms including the "BIT Zhixingzhe" humanoid robot and a quadruped robot dog on CCTV's program "Amazing, We Are 'Science' People." At the 2026 Beijing Yizhuang Humanoid Robot Half Marathon, "Zhixingzhe" completed the full 21-kilometer course, achieving zero falls and zero interruptions across more than ten types of complex terrain including urban roads, wetland parks, steep slopes, and near-90-degree sharp turns, and won second place for "Best Gait." The research group also independently developed Sigma, a simulation system for single-drone and swarm algorithms, which supports multi-scenario, multi-weather simulation training.

  11. Xbotics具身智能实验室 · 微信公众号20

    Xbotics Embodied Intelligence Community October Event Recap: OmniHand O10 Dexterous Hand Experience, Qinglong Nano Motion Control Training, and MiniDuck Replication

    In October, the Xbotics Embodied Intelligence Community held three offline events: at the Shenzhen session, 48 developers used the OmniHand O10 dexterous hand to complete calibration and a crawling competition; at the Shanghai session, 38 developers completed the transition from MotrixLab simulation training to real-machine deployment of the Qinglong Nano within two days; and at the Hangzhou session, 22 people learned to replicate the MiniDuck little yellow duck.

  12. Xbotics具身智能实验室 · 微信公众号62

    Xbotics Lecture 7: A Robot Manipulation Loop from Target Pose to Grasping, Placement, and Failure Recovery

    In Lecture 7 of the "Twenty-One Lectures" series, the Xbotics Embodied Intelligence Lab converts the target object pose obtained in Lecture 6 into gripper actions, linking the grasp-and-place loop with five poses—pre-grasp, grasp, lift, place, and retreat—and a state machine.

  13. 智东西60

    InSpatio opens embodied simulation data collection center in Hangzhou, plans 1 million hours of simulated data per month

    Spatial intelligence company InSpatio opened an embodied simulation data collection center in Hangzhou on October 30, planning to produce 1 million hours of simulated training data per month, with a comprehensive cost per data item of about 1/10 that of traditional real-machine teleoperation collection. Relying on its self-developed Topos spatial intelligence base, the center scans real sites and then automatically decomposes objects, fills in blind spots, and calibrates physical attributes, before generating embodied Tokens containing depth, pose, contact force, and action semantics in parallel through multiple channels in the cloud.

  14. 古月居 · 微信公众号15

    Beginner CoppeliaSim 4.9/4.10 Robot Simulation Development Course Launched

    Tianyi'an has launched a beginner CoppeliaSim robot simulation development course based on CoppeliaSim 4.9/4.10, teaching the software's basic operations through five projects: a six-link robot, a four-legged linkage robot, a line-tracking and collision smart car, a Mecanum-wheel car, and an E-puck smart car. The course is originally priced at 79.8 yuan, with a 2-person group rate of 59.8 yuan and a 3-person group rate of 39.8 yuan; after purchase, it can be viewed free for 2 years. Future updates are planned to cover Simulink/Python co-simulation, robotic arm control, and trajectory planning.

  15. Guyuehome19

    SoftCon: Octopus swimming control based on bionic muscle actuation and soft-body dynamics

    The SoftCon framework builds an octopus swimming control pipeline from tetrahedral soft-body dynamics, muscle constraints that contract along fibers, and traveling activation waves generated by keyframe matrices and propagation filtering, using PPO to learn coordination rather than reconstruction. The framework originates from SIGGRAPH Asia 2019.

  16. Guyuehome63

    InsertAnything Evaluated in Isaac Lab for Precision Peg-in-Hole Insertion Policies with Fingertip Force Feedback

    Official weights of InsertAnything's force-conditioned insertion policy were evaluated in Isaac Lab: at 0.1 mm clearance, baseline success rates for two hole types were 96–100%. With a 5 mm initial hand offset, success rates were unaffected, but with a 3 mm hole-position observation deviation, the hexagonal hole success rate dropped to 60%.

  17. Guyuehome57

    G1 Dribble: End-to-End Reinforcement Learning for Humanoid Robot Soccer Dribbling Control

    G1 Dribble uses end-to-end reinforcement learning to achieve humanoid robot soccer dribbling; the walking task's weights are not adjusted, and velocity commands drive both the body and the ball. The author tested it locally in MuJoCo-Warp and recorded eighteen cloud sessions, reporting touch rhythm, the distance band for re-touching the ball, and failure modes, including that a centered start steps over the ball without touching it, a first touch occurs only when offset to the side, and at 1.5 meters per second it knocks the ball away directly.

  19. Guyuehome57

    OpenCR-MuJoCo: Discretized MuJoCo simulation of tendon-driven continuum robots

    OpenCR-MuJoCo presents a discretized simulation method for tendon-driven continuum robots in MuJoCo, modeling the continuous backbone as a rigid chain with elastic joints, where bending is produced by tendon length differences. The work uses SoRoSim reference data and the official demo to validate static accuracy, release oscillation, and closed-loop tracking.

  20. Guyuehome58

    PPO Enables Autonomous Balance and Navigation Control for a Bicycle Robot in PyBullet

    This study takes a bicycle robot with a reaction pendulum as its subject, derives the roll dynamics and the speed-dependent instability mechanism, and uses proximal policy optimization (PPO) to train coordinated control of the steering, rear wheel, and reaction pendulum. Experiments are carried out in PyBullet: natural fall, training, balancing, navigation, and disturbance recovery, concluding with obstacle-avoidance training.

  21. Guyuehome58

    do-mpc: Robust Model Predictive Control with Multi-Stage Scenario Trees

    Using bridge crane anti-sway as the object, the article derives the composite pendulum dynamics formed by the trolley, a homogeneous suspension rod, and the payload, and presents the construction of a multi-stage scenario tree, non-anticipative input sharing, and an equally weighted expected objective. The authors conducted measurement-intermittent comparative experiments between nominal and robust controllers in the MuJoCo physics engine.

  22. Guyuehome58

    Neural-Fly: Online Adaptive Control for Quadrotors in Strong Winds

    Neural-Fly proposes a residual aerodynamic force adaptation law that combines wind-invariant representations with online least squares for online adaptive control of quadrotors in strong winds. The method trains a network on public flight data and conducts comparative figure-8 trajectory tracking experiments with a PyBullet quadrotor in a steady wind field, demonstrating rapid adaptation enabled by domain-shift separation.

  23. Robotics & Automation News50

    Interview with TorqueAGI founder: Most robot AI is just a language or vision model with robot data added

    TorqueAGI is providing a "world reasoning model" intelligence layer for industrial robots and heavy machinery rather than developing its own hardware. The model represents objects, machines, and workflows as spatiotemporal graphs and incorporates physics and geometry into its reasoning. The company says new tasks typically require only dozens of examples rather than tens of thousands, and must be validated in simulation and in supervised operation at customer sites before unattended operation is possible. The same model has already run on single-arm and dual-arm robots, mobile platforms, scissor lifts, humanoid robots, and tractors, while details specific to each machine body still need to be described per machine.

  24. OFweek Robotics62

    CAS Institute of Automation and PaXini collaborative research: tactile reinforcement learning achieves sub-millimeter precision assembly

    A team led by Cheng Long at the Institute of Automation, Chinese Academy of Sciences, published research at IROS 2026 using tactile-enhanced reinforcement learning to complete sub-millimeter precision insertion, raising the success rate from 52% for a tactile-free approach to 67% under a 0.05 mm clearance.

  25. OFweek Robotics70

    Critical Point rolls out its 15,000th dexterous hand, launches OmniGlove Pro data-collection glove

    At its AI DAY on October 29, Critical Point announced that cumulative production of its dexterous hands has surpassed 15,000 units, and launched its self-developed data-collection glove, OmniGlove Pro. The glove uses a fusion of EMF magnetic field positioning and IMU, achieves fingertip positioning accuracy of 0.1 mm, is equipped with 746 tactile sensing points, and has a tactile spatial resolution of 3 mm. The company also introduced the "real-machine Ready" concept, spanning from data collection to real-machine replay.

  26. arXiv cs.RO62

    GenSight: A Unified Embodied World Model for Video Generation and 4D Perception

    GenSight is a unified embodied world model that integrates future video generation and dense perception into a single pretrained video diffusion Transformer. It uses a unified spatiotemporal representation covering video, depth, embodiment segmentation, and 2D optical flow relative to the first frame; built on a shared DiT backbone, it predicts each modality within the same architecture via lightweight task modulation, and jointly optimizes them in a unified velocity prediction space. At inference, it supports multi-step future video generation with aligned dense prediction, as well as single-step dense perception from an observed video. Training data covers a variety of robot embodiments and human hand interaction.

  27. arXiv cs.RO62

    MUSBRIC: LEGO-Compatible Pneumatic Muscle Modules for Reconfigurable Musculoskeletal Robots

    The authors present MUSBRIC, a modular hardware platform and accompanying MuJoCo simulator for reconfigurable musculoskeletal robots, which uses LEGO-compatible endcaps to turn fiber-reinforced extensile pneumatic artificial muscles into reusable, plug-and-play muscle modules. An elbow-joint prototype validated load-dependent flexion and extension, with pulley-guided routing enabling reversible motion and a mechanical stop stabilizing the straight posture; a robotic hand demonstrated compliant grasping and multi-joint construction, 15 leg configurations completed matched sweeps in hardware and simulation, and a quadruped assembled from four legs achieved walking. The hardware design and simulation code will be open-sourced.

  28. arXiv cs.RO72

    OpenFlyV2: An onboard vision-language-action model for aerial perception and navigation

    OpenFlyV2 is a unified onboard vision-language-action architecture that uses a shared vision-language representation to support semantic understanding, language-grounded perception, spatial reasoning, and continuous flight control within a single model. The authors built a simulation dataset of approximately 70,000 navigation trajectories and 60,000 vision-language instruction-response pairs. On an NVIDIA Jetson Orin NX, the model runs fully onboard and generates action chunks of 12 waypoints each at 10 Hz.