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#Motion control
Oct 26Mon
  1. New Strategy Robotics22

    Geek+ general-purpose humanoid robot Gino 1 to appear at CeMAT Asia 2026

    Geek+'s general-purpose humanoid robot Gino 1 will appear at CeMAT Asia 2026 from November 3 to 6, 2026, at Booth A3-2, Hall N1, Shanghai New International Expo Centre. The on-site demonstration will showcase multi-category picking and cross-task transitions, combining multimodal perception and force-control feedback to determine grasp positions and strategies in real time, and it is equipped with the self-developed Gravity embodied intelligence model; under full-body parallel control, peak throughput can reach 300UPH.

  2. 拓斯达50

    In optical workshops, how does Topstar's embodied intelligence cross the "deployment" hurdle?

    Topstar's humanoid dual-arm robot AOI glass wiping workstation has completed validation in the workshops of a leading 3C customer and an optical customer, integrating the functions of two stations—inline AOI inspection and wiping—into a single machine. The workstation has a repeat positioning accuracy of ±0.05mm; it locks onto stain positions via vision and performs wiping with flexible force control, relying on a high-resolution industrial camera and an edge AI computing unit to complete the full chain from image acquisition to defect classification and localization. Ordinary production line workers can quickly get up to speed, reducing training costs and human error and helping improve overall customer workshop yield.

Oct 25Sun
  1. Reddit r/robotics22

    DIY bipedal robot CBR-II V3 update: smoother gait, attempts one-leg stand

    The DIY bipedal robot CBR-II has released its third version (V3), walking more confidently with noticeably less jitter, but when "forward" is immediately followed by "stop," the front foot still slams heavily into the ground. In simulation, V3 balances on one leg every time, but on the real machine it is still far from perfect; the author says the hardware is too fragile, with one joint breaking three times in a day and a GT2 belt snapping, and the project ends here for this year.

  2. Reddit r/robotics42

    ETH Zurich's ANYmal quadruped robot solves giant Rubik's Cube without using its hands

    ETH Zurich's ANYmal quadruped robot solved a 10-pound giant Rubik's Cube using only its feet, three times, taking about 8 to 9 minutes each time. The demonstration combines computer vision, a traditional Rubik's Cube solving algorithm, and reinforcement learning to teach the robot to balance, apply force, and correct errors. This "pedipulation" (manipulation with feet) could enable future robots to open doors, turn valves, or move obstacles without hands.

  3. 深蓝AI · 微信公众号81

    TU Delft's MonoRace achieves 100 km/h autonomous drone racing using only a single front-facing camera

    The MonoRace system from TU Delft's MAVLab in the Netherlands, relying only on a single front-facing camera and an IMU, defeated three world-champion-level pilots in a row at the 2025 Abu Dhabi Autonomous Drone Racing competition; the paper was published in Science Robotics on October 14, 2026.

    Why it matters: The Delft team broke down how a monocular camera plus IMU divides perception, state estimation, and reinforcement-learning control in high-speed racing, which can be transferred to real onboard systems with limited compute.

  4. 斯帝尔科技 · 微信公众号42

    STIAL's AI flexible grinding robots and NextBrain™ full-stack product system empower high-end manufacturing

    STIAL has built four product series around complex grinding scenarios: AI flexible grinding robots, AI flexible grinding workstations, embodied core components, and the NextBrain™ AI brain, forming a physical AI technology loop of "perception—analysis—decision—execution."

Oct 24Sat
  1. 机器人前瞻 · 微信公众号58

    AgiBot's 110 Expedition A3 humanoid robots dance together on stage, setting a Guinness World Record

    AgiBot organized 110 Expedition A3 humanoid robots to complete a group dance challenge at the Jinan Yellow River International Convention and Exhibition Center, with a Guinness World Records adjudicator presenting the certification on site. The challenge relied on the self-developed LinkBots group control and scheduling system to uniformly manage motion trajectories, action timing, and formation changes, combined with UWB centimeter-level fusion positioning and 5G/WiFi dual-mode communication to achieve millisecond-level synchronization, and supports fault isolation and empty-slot avoidance. These robots will subsequently enter subway stations, scenic areas, and other settings to take part in information guidance, patrol assistance, and cultural tourism performances.

  2. Tech Xplore Robotics58

    Peking University team proposes motion prediction method for flexible robotic arms, reducing long-range error by more than a quarter

    A team led by Xie Guangming and Zhang Feitian at Peking University proposed a hybrid model that uses an equilibrium anchoring model and a lightweight correction model to suppress the accumulation of step-by-step prediction errors in flexible robotic arms. The work was published at RSS 2026. When predicting 200 steps (6 seconds) on a simulated 3D cable-driven robotic arm, the hybrid model's error was more than a quarter lower than that of the anchoring model alone, and none of 19 tests showed loss of accuracy. On a physical hydraulic soft robotic fish tail, its error was half or less that of the anchoring model and a quarter or less that of the correction model. More stable predictions could let a controller first try out multiple actions in a computer and pick the safest one.

Oct 23Fri
  1. arXiv cs.RO60

    STAMP: Humanoid Motion Transitions with Human Motion Priors

    The paper proposes the STAMP framework, which uses a "robot-to-human-to-robot" process to connect independently captured humanoid motions into continuous sequences. Its core is a learned alignment model: after motions are retargeted to a human skeleton, the model ranks candidate positions, headings, and transition durations for the subsequent motion, and a human motion prior then generates the transition under the selected alignment. The study evaluates the tracking success rate of the generated transitions on the Unitree G1 and ROBOTIS K1, and demonstrates long-horizon motion transitions in real-robot demos.

  2. arXiv cs.RO62

    STR: A Spatiotemporal Preference Motion Retargeting Method for Humanoid Loco-Manipulation

    The authors propose a spatiotemporal retargeting method, STR, which uses a vision-language model to select relevant motion signals and body parts and identifies keyframes accordingly, allocating additional solver iterations to keyframes and higher interaction weights to the selected parts to reduce retargeting computational overhead.

  4. OFweek Robotics60

    Commentary: The stronger Astra gets, the more domestic robots must keep the execution layer in their own hands

    Using a real-world test report on SenseTime's Astra as its starting point, the article notes that Astra is outstanding at the planning layer (100% on LIBERO-Long++, 81.9% on RoboDojo), but real-machine operation exposes an architectural break between cloud inference's second-level latency and millisecond-level force-feedback closed loops; stacking bowls took an average of about 131 minutes and cost about 19 US dollars.

Oct 22Thu
  1. Reddit r/robotics20

    Help sought with actuator jitter on 6-DOF motion-control camera

    A freelance filmmaker's DIY 6-DOF motion-control camera uses Robstride actuators running in MIT mode on an STM32, and now shows noticeable vibration and jitter when the J2/J3 joints move together under load. The author has tried implementing a cogging-torque compensation table and a gravity model but has not resolved the jitter; the arm is usable for stop-motion scenarios, but motion smoothness still falls short of requirements, and the author is asking the community for suggestions.

  2. 机器人前瞻 · 微信公众号73

    TeleAI and others propose NEXUS, a cross-terrain perception and motion control foundation, enabling Unitree G1 to perform full-body actions on stairs and slopes

    The China Telecom Artificial Intelligence Research Institute (TeleAI), Gamma Intelligence, Shanghai Jiao Tong University, the University of Hong Kong, and Zhejiang University jointly proposed NEXUS, a cross-terrain perception and motion control foundation for humanoid robots that lets robots autonomously adjust posture and contact based on terrain while preserving motion intent.

  4. arXiv cs.RO58

    RISE: Test-Time Motion Steering for Perceptive Humanoid Fall Recovery

    RISE is a perceptive humanoid fall-recovery method that uses task objectives at inference time to steer how the robot stands up. It learns a recovery vocabulary that links intended movements to motor actions through sensory feedback; a visual diffusion planner learns from executions of this vocabulary to predict motion intent as future body states, and task costs steer that prediction toward a desired heading, rise speed, or path, after which motion latents are generated accordingly. Simulations demonstrate diverse recoveries, terrain adaptation, and test-time steering without retraining, with a real-robot demo on the Unitree G1.

  5. arXiv cs.RO57

    SP-HLM: Sparse-Pose-Driven Motion Generation and Whole-Body Control Framework for Humanoid Loco-Manipulation

    SP-HLM proposes a sparse-pose-driven interface framework that uses five task-critical body poses plus a learned implicit motion prior to represent spatial intent and whole-body coordination in complementary forms. The SP-VLA vision-language-action model first predicts task-relevant sparse poses and finger joint positions from multimodal observations and language instructions via asynchronous action-chunk inference. The SP-MGM motion generation model, trained through two-stage distillation, reconstructs coordinated full-body motion in real time, and the SP-GMT general motion tracker then combines the explicit sparse targets with the implicit motion prior for whole-body control.

  6. arXiv cs.RO56

    HuMiT: Low-Latency Whole-Body Humanoid Teleoperation via Compact Reference Tracking

    HuMiT is a whole-body humanoid teleoperation system whose unified tracking policy requires only a compact reference target—root height, root velocity, and the SE(3) poses of four sparse keypoints at the current frame—yet achieves tracking accuracy comparable to methods that rely on full joint positions, dense body-part transformations, and even future frames.

  7. arXiv cs.RO47

    Spacetime Splines for Adaptive Robot Motions

    This study proposes a spline-based movement primitive representation that unifies spatial geometry and temporal progression into a common set of continuous variables, jointly estimates a shared Bezier curve, and allows spatial and temporal model complexity to be adapted independently. Simulations validated joint recovery accuracy, refinement from modifications to model complexity and continuity, and improved robustness to variance in the demonstration set. In a robot-assisted patient-turning task, online retiming and synchronization capabilities were evaluated with a physical manikin.

  9. arXiv cs.RO60

    End-to-End Reinforcement Learning for Quadrupedal Jumping in Lunar Gravity

    The study proposes a multistage reinforcement learning method that produces a single end-to-end control policy for commanded point-to-point jumping by a quadruped robot under lunar gravity. The policy architecture is simple and relies only on mission-compatible onboard sensing; in simulation, landing errors are smaller than the robot's body size. The work also analyzes torque and power demands and characterizes performance degradation under extreme outlier events, including control-update freezes of up to 1 second during flight. In hardware transfer tests, the policy successfully completed a vertical jump under lunar-gravity offloading, and measured torque and power profiles matched the simulation and stayed within hardware limits.

  10. arXiv cs.RO51

    Make it Walk: Computational Design Methods for LEGO Walkers

    The paper proposes the first computational approach for designing LEGO walkers that support multiple gaits beyond stable walking. The method has two parts: first, a specially designed gear-linkage mechanism controls the foot trajectory of each leg, incorporating a pair of 3D-printed non-circular gears working with a LEGO Technic mechanism; second, each leg is driven by an independent motor, with a simple yet flexible control algorithm synchronizing the timing of multiple leg motions.

  12. arXiv cs.RO47

    Linear Twisting Sidewinding Gait Motion Planning for Snake Robots on Complex Terrain

    The study proposes a linear twisting function that distributes torsional load across the entire body of a snake robot, replacing the existing rolling vector method's approach of concentrating it in two middle twisting joints. Under matched turning speed and total output power, the new gait reduces the peak output power of a single twisting joint by 42%. Real-robot experiments show it can move on various terrains that the existing rolling vector method cannot traverse, and is up to 49% faster than the traditional sidewinding gait without twisting.

  13. arXiv cs.RO55

    Distance-Field-Based Reinforcement Learning for Reactive and Robust Obstacle Avoidance in Robotic Arms

    This paper proposes a reinforcement learning framework that integrates the Euclidean Distance Field (EDF) directly into the observation space, providing the agent with continuous geometric cues to achieve stable and responsive obstacle avoidance behavior and simplifying reward design; it effectively avoids obstacles both with and without artificial potential field guidance.

  14. arXiv cs.RO56

    A2I-Calib++: A Noise-Robust Active Calibration Framework for Whole-Body Multi-IMU Systems on Legged Robots

    The paper proposes A2I-Calib++, a kinematics-driven, noise-robust active calibration framework for multi-IMU systems mounted on legged robot bodies. It can estimate temporal offsets, extrinsic rotations and translations, and inertial biases without requiring artificial calibration targets or complex parameter tuning.

  15. arXiv cs.RO48

    Robot Safe Navigation Framework Combining Flow Matching and Model Predictive Control

    This framework combines flow-matching-based path planning with model predictive path-following control for mobile robot navigation. The flow matching model captures multimodal path distributions, while the predictive controller satisfies state and input constraints online and avoids obstacles, and event-triggered replanning feeds control solutions back into subsequent path generation. Simulation results show that the framework performs robustly in cluttered environments.

  17. arXiv cs.RO64

    SMR: Learning streaming motion retargeting for humanoids from human semantics

    SMR is a streaming neural motion retargeter learned from semantic-conditioned physics experts. It converts human motion into robot task-space targets, which the physics experts track in simulation, and then uses a strictly causal Transformer to learn their executed trajectories. Each new frame uses only current and past human observations to generate a robot motion reference for a downstream whole-body controller. Experiments show that the semantic-conditioned physics experts outperform direct-optimization retargeting in semantic fidelity and physical validity, and that the resulting streaming retargeter performs better in closed loop under a unified whole-body controller than kinematic and physics-refinement baselines.

  18. arXiv cs.RO50

    State-dependent objective coordination enables end-to-end balance and navigation for two-wheeled robots

    An arXiv paper proposes a lightweight state-dependent objective coordination method for end-to-end balance and navigation of two-wheeled inverted-pendulum robots. The method introduces a pitch-angle-conditioned scalar coefficient β(s)∈[0,1] that dynamically adjusts the relative weight of balance stabilization and navigation objectives during training, without requiring an additional control hierarchy or attitude-stabilization controllers such as PID or LQR.

  20. arXiv cs.RO47

    One Policy, Many Styles: A Comparative Study of Preference Aggregation Locations in Steerable Humanoid Control

    A study compared four preference aggregation methods—reward-level, advantage-level, loss-level, and PCGrad—across two humanoid robot control frameworks, finding that no aggregation level is optimal across all environments and metrics. The experiments used Preference Reward Responsiveness (PRR) combined with coverage and utility metrics; results show that early aggregation produces a wider range of behavioral responses but introduces more unintended objective interference, while later aggregation improves control precision at the cost of some responsiveness.

  21. arXiv cs.RO46

    Sensorless force/torque observer and hybrid pose-force control framework for physical human-robot interaction

    The framework combines a neural-network-enhanced robust wrench observer with a hybrid pose-force controller for physical human-robot interaction (pHRI), enabling end-effector pose and force tracking without force/torque sensors. The neural network compensates for system friction and unmodeled dynamics to improve observer performance, and task-null-space decomposition separates joint velocities and reduces the effect of human disturbance on the observer. Experimental results show that the framework achieves robust and accurate wrench estimation while maintaining reliable pose and force tracking, and can be applied to various robotic arm platforms without force/torque sensors.

  22. arXiv cs.RO44

    Meta-Learning-Based Mode Recognition and Adaptive Control for Human-Robot Interaction

    The framework uses a meta-learning-trained neural network to recognize two human-robot interaction modes, active guidance and passive following, without manually setting switching thresholds. The passive mode recognition rate rose from 90.1% to 99.1%, and the active mode from 79.8% to 97.1%. An adaptive meta-learning controller can compensate for human disturbances and adapt online to varying interaction forces. A multi-trajectory meta-learning algorithm jointly learns an environment basis representation and force-related parameters to handle unmodeled dynamics. Experiments verified its robust control and reliable mode recognition performance.

  23. 腾讯科技 · 微信公众号54

    Unitree releases H2-X humanoid robot demo video, says maximum battery life is about 8 hours

    Unitree Technology released a demo video of the H2-X "Kung Fu Legend" humanoid robot on October 21, showing scenes including leaping over a car, kicking a car door, jumping down from a shipping container, blocking a blade in a villa courtyard, lifting a car to rescue a person, and pushing a person to safety. The company says the H2-X has a maximum battery life of about 8 hours; for reference, the publicly stated battery life of the standard H2 is about 3 hours, but the testing conditions were not specified.

  24. Tech Xplore Robotics57

    University of Illinois proposes CARL method to keep reinforcement learning stable for wheeled, flying, and six-legged robots

    Researchers at the Grainger College of Engineering at the University of Illinois Urbana-Champaign combined reinforcement learning with control theory to propose a contraction-aware reinforcement learning method, CARL, which guarantees robot stability while improving tracking performance by up to 15% compared with existing methods.

  25. Pieter Abbeel64

    SuperMimic cuts GPU hours for humanoid full-body control training by over 1,000x

    SuperMimic trains a humanoid full-body control policy on 129,514 motions, surpassing the previous best result of 128 GPUs over 7 days with 1 GPU in 3 hours, reducing GPU hours by over 1,000x. Pieter Abbeel shared it and called it a game changer, noting that the key lies in using a world model faster than conventional simulation and in collecting far more signal than policy gradients through backpropagation through time. The project page is https://supermimic.github.io, and the demo is at https://supermimic.github.io/demo/.

Oct 21Wed
  1. 人形大讲堂 · 微信公众号62

    Unitree releases humanoid robot H2-X, demonstrating kicking in a car door, lifting a car, and 8-hour battery life

    Unitree Technology released the humanoid robot H2-X on its official Weibo account, with a caption stating a maximum battery life of about 8 hours. The video shows it kicking in a car door, landing a flip from a 4-meter-high platform, and lifting a car with its bare hands, with scenarios covering security and emergency rescue.

  2. 机器人前瞻 · 微信公众号58

    Unitree releases humanoid robot H2-X Kung Fu Legend Guardian with about 8 hours of battery life

    Unitree Technology has released its new humanoid robot H2-X Kung Fu Legend Guardian, and an official video claims a maximum battery life of about 8 hours. In the video demonstrations, the robot can lift the front of a car with its bare hands, jump down from a 4-meter-high platform and roll to a standing position, perform consecutive airborne flying kicks and land steadily, and dent a car door with a single kick. The original article holds that these demonstrations test balance control, leg torque, and mid-air posture correction capabilities, and infers that the robot may target fields such as emergency rescue and security protection.