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#Motion control
Oct 30Fri
  1. 江苏箸境智能科技有限公司18

    Zhujing Debuts H1 Humanoid Robot "Xiao Zhu" at China-Europe Cross-Border Exchange Conference, Pursuing Global Embodied Intelligence

    At the 6th Innovation Fair China-Europe Cross-Border Exchange Conference 2026, Zhujing exhibited its H1 humanoid robot "Xiao Zhu" and multi-scenario deployment solutions, with a live demonstration of writing the Chinese character "Fu," showcasing arm motion control and fine force-controlled manipulation. The company hopes to connect with European robot manufacturers, automation integrators, and research institutions to explore cooperation in industrial production, commercial services, and co-building high-quality datasets, and plans to work with ecosystem partners to build an overseas embodied intelligence ecosystem and drive the coordinated overseas expansion of its products and solutions.

  2. Reddit r/robotics38

    Mino robot takes its first autonomous outdoor stroll: ROS 2-based following app tested

    The Mino robot completed its first outdoor autonomous stroll test. Its ROS 2-based following app runs on a Jetson Orin Nano and has three states: finding people, following people, and companionship. The app uses YOLO-World to detect pedestrians and stereo vision depth estimation to generate 3D bounding boxes, combined with Kalman filter tracking, and continuously recomputes trajectories to control the base and head. The app was completed over a weekend using open-source software modules and a coding agent, and will be released as open source with the robot if the Kickstarter crowdfunding campaign succeeds.

Oct 29Thu
  1. 高工咨询 · 微信公众号42

    Bokente launches hybrid dual-arm collaborative robot HS-PRO and five-axis stereo machining workstation, pivoting to industrial embodied applications

    Bokente has released two new products, the "hybrid dual-arm collaborative robot HS-PRO" and the "five-axis stereo machining workstation," marking its transition from high-speed parallel sorting to industrial embodied applications. HS-PRO uses dual-arm biomimicry plus AI vision coordination to cover the full process of material handling, assembly, fastening, and feeding, with a cycle rate of 130 times per minute; the five-axis workstation supports adaptive alignment for irregular holes and constant-force fastening, adapting to non-planar machining such as inclined surfaces and counterbores. The company says more than 95% of core components are produced in-house, and it has entered automotive brake pad line changeover, new energy battery sorting, and production line assembly processes.

  2. 人形大讲堂 · 微信公众号74

    China Releases First Batch of 6 National Standards for Humanoid Robots, Effective April 1, 2027

    The State Administration for Market Regulation has approved and released China's first batch of 6 national standards for humanoid robots, numbered from GB/T 48316 to GB/T 48317.5, covering general provisions, environmental perception, decision planning, motion control, task operation, and simulation testing platforms. They take effect on April 1, 2027.

  4. 3D视觉工坊 · 微信公众号50

    Yufeng 250-L LiDAR Edition Quadcopter Drone Kit: An Autonomous Navigation Development Platform for Research and Education

    The Yufeng 250-L LiDAR Edition quadcopter drone kit is aimed at research and education. It consists of three core modules: a flight controller, an onboard computer, and LiDAR. It can deploy SLAM, planning, and control algorithms to achieve real-time navigation in unknown environments.

  5. 星海图 · 微信公众号22

    Galaxea Showcases Embodied Intelligence Applications at 2026 World IoT Expo (Wuxi)

    At the 2026 World IoT Expo (Wuxi), Galaxea is presenting three major embodied applications: a healthcare scenario deployment in partnership with Kunshu Xingqi, a robot front warehouse with on-site ordering and multi-robot collaborative autonomous picking and packing, and floor exercise champion Kengo taking on high-difficulty moves. The expo runs from October 30 to November 1 at Wuxi Taihu International Expo Center, Hall A3-03.

  6. Guyuehome58

    JEPLO: Terrain-Adaptive Gait Control for Quadruped Robots Based on JEPA and LiDAR Perception

    JEPLO proposes a terrain-adaptive gait control method for quadruped robots that aggregates single-LiDAR point clouds into depth observations and predicts the next-beat latent variable in latent space, without reconstructing images. The method is supplemented by teacher-student distillation and distribution regularization; pretrained weights are reproduced in simulation, and principal component analysis of the latent variables clusters clearly by terrain.

  7. Guyuehome62

    Analysis of Inverted-Pendulum Dynamic Balance Control for a Wheel-Legged Quadruped Robot

    The article derives the dynamics of a wheel-legged inverted pendulum and explains why the wheels must accelerate in the direction of the fall for a quadruped robot to stand on only its two rear wheels. It also analyzes the privileged-information design of an asymmetric actor-critic and tests the limits of four policies through cross-physics-engine experiments.

  8. Guyuehome60

    OpenDoge uses observation-history implicit state estimation to control quadruped robot locomotion

    OpenDoge proposes a hybrid internal model for quadruped robot locomotion control that, without terrain maps or contact sensors, encodes observation history into a velocity estimate and a 16-dimensional latent state to determine gait phase and external disturbances. The article combines real inference data to examine what information the latent state carries and what it lacks.

  10. Guyuehome58

    FTNCBF: Fault-Tolerant Neural Control Barrier Functions Under Sensor Failures and Attacks

    FTNCBF addresses the failure of barrier-function safety guarantees when sensors are attacked. It runs estimators in parallel for each fault hypothesis, converts estimation uncertainty into first- and second-order correction terms in the barrier condition, and then synthesizes fault-tolerant control via a minimum-norm program. The method was reproduced in spacecraft and UAV scenarios.

  11. Guyuehome57

    WaterSpiderbot: Kinematic Modeling and Reinforcement Learning Gait Control for an Eight-Legged Bionic Spider Robot

    This material analyzes the eight-legged bionic robot WaterSpiderbot, covering forward and inverse kinematics of the three-joint leg chain, MuJoCo contact support mechanics, and the SAC gait policy, and provides a complete reproducible training loop. The article also diagnoses the structural reason why learning never starts under the default configuration and offers a fix.

  12. Guyuehome58

    AME: Terrain-attention encoding controls G1 humanoid robot walking on complex terrain

    The article introduces a terrain-attention encoding method called AME that encodes a 1.6×1.0-meter elevation map for the G1 humanoid robot into 187 terrain tokens, uses the robot's proprioceptive state as a single query for cross-attention to extract terrain features, and the policy uses these to walk on steps, plum blossom piles, and slopes. The article derives the encoder structure, attention mechanism, and reward design; the full body of the text is not provided in the materials.

  13. Guyuehome58

    Lidar-E2E: End-to-End Obstacle Avoidance Control with Onboard LiDAR for Quadrotors

    Lidar-E2E proposes an end-to-end obstacle avoidance control method for quadrotors using onboard LiDAR. It pools point clouds from a 1944-line LiDAR into a 36×6 range image, concatenates this with a 20-dimensional body state, and directly outputs attitude increments and throttle, which are executed by a low-level PX4 cascaded attitude controller. The article also derives a rotor identification model, the sensing pipeline, and reward shaping, and uses Isaac Sim.

  14. Guyuehome60

    Minimum Snap: Differential-Flat Trajectory Generation and Geometric Control for Quadrotors

    An analysis of the minimum-snap quadrotor trajectory pipeline: piecewise ninth-order polynomials (ten coefficients) are solved in closed form with snap as the cost and derivative-continuity constraints, while COBYLA trades off time against smoothness. The differential-flat mapping yields thrust and attitude, and geometric control is validated in closed loop in MuJoCo.

  15. Guyuehome58

    FishSim: Tendon-Driven and Hydrodynamics-Based Propulsion Control for a Biomimetic Robotic Fish

    FishSim uses a single motor, two crossed elastic tendons, and a five-coefficient stateless fluid model to make biomimetic robotic fish propulsion an identifiable pipeline. The paper presents a comparison of simulated and real swimming speeds at eight frequencies; trajectory error fell from 592 mm to 91 mm after identification, and it discusses coefficient sensitivity and the limits of the turning experiments.

  17. Tech Xplore Robotics58

    University of Toronto develops flexible foot pad for one-legged hopping robot Hopcopter, improving performance on sand and ice

    A University of Toronto engineering team has developed a flexible foot pad for the one-legged hopping robot Hopcopter, enabling it to hop on difficult terrain such as sand and ice. By enlarging the contact area, using a spherical joint that tilts with the terrain, a rubber band for return, and short spikes on the bottom for grip, the pad reduces takeoff error by up to 66%, improves efficiency by up to 64%, and cuts cross-terrain performance variation by 84%. The related paper was published in Advanced Intelligent Systems; the team's next step is to develop autonomous navigation, targeting scenarios such as search and rescue, pipeline inspection, and mining area inspection.

Oct 28Wed
  1. 机器人全球资讯 · 微信公众号55

    First CMG World Robot Mount Tai Climbing Competition Qualifying Round Kicks Off in Rain

    On October 26, the qualifying round of the first CMG World Robot Mount Tai Climbing Competition started on the trail from Red Gate to Zhongtian Gate in Mount Tai, with multiple domestic teams fielding legged and wheel-legged composite robots. The course is about 3.8 kilometers long with more than 3,100 steps. Rain during the race made the stone steps slippery, testing the robots' balance control, perception and decision-making, and battery life in a real mountain environment. One robot from Dongtai Technology completed the speed race in about 58 minutes with about 80% battery remaining. The event also includes a loaded-carrying race for legged robots, simulating mountain emergency supply delivery scenarios.

  3. Guyuehome58

    Jumper: Dynamic Jumping Control for a Crab Robot Based on Reference Motion and Residual Reinforcement Learning

    Jumper uses a reference-guided residual reinforcement learning method to control dynamic jumping in a crab robot. The reference motion records only the nominal trajectory, and the policy learns only the corrections to the reference commands. In this method, the takeoff impulse determines the launch velocity, the ballistic trajectory determines the airborne phase, and the official policy is used to complete jumping and boundary tests.

  4. Tech Xplore Robotics60

    UCLA and UC Davis develop 185-milligram insect-inspired flying robot that can resume flight after collisions

    A UCLA and UC Davis team published in Science Robotics a fully polymer micro flying robot weighing 185 milligrams that uses electrostrictive polymer films to directly drive flexible conical wings, with no mechanical transmission. In tests it can generate lift about three times its own weight, take off autonomously at an average speed of 40 centimeters per second, and still resume flight after being hit by a fly swatter or pressed under a load about 11,000 times its own weight. The current prototype is powered through external wires; the team's next steps are to improve steering and landing control, increase payload, and advance onboard power, control electronics, and sensing toward autonomy.

  5. 众擎机器人 · 微信公众号47

    First URKL Qualifier's Top 16 Revealed, Each Receives an EngineAI T800 Robot

    The 32-to-16 qualifying round of the first URKL Global Humanoid Robot Free Combat League has ended, with 16 teams advancing to the group stage and each receiving an EngineAI T800 robot. The qualifiers held six days of offline training at the Dasha River Cultural and Sports Center in Nanshan, Shenzhen. All teams used the T800 as their unified hardware platform, deploying code refined in simulation onto real robots for the first time, and were evaluated on basic combat moves such as getting up after a fall, navigating complex terrain, and punching and kicking. The event features a prize pool of tens of millions, and industry partner Dison Digital will provide the top 16 with motion capture experience benefits.

  6. arXiv cs.RO58

    Compact Force Sensor and Tension-Aware Outer-Loop Control for Dual-UAV Cable-Suspended Payload Transport

    This study proposes a dual-UAV payload transport framework that uses a custom compact force sensor at the cable anchor point to measure interaction forces, with static and dynamic tests evaluating its linearity, hysteresis, repeatability, and crossload. Control uses a cascade structure: inner loops handle vehicle stabilization, while outer loops compensate for the measured forces, validated in simulations and indoor experiments under position uncertainty. Payload-drop and constrained-space tests compared with literature-based distributed methods show improved stability, coordination, and disturbance rejection.

  7. arXiv cs.RO65

    Gradient-Free Neural Hamilton-Jacobi Reachability for Scalable Safety-Critical Control

    The authors propose a discrete-time neural reachability framework that learns backward reachable tubes (BRTs) and backward reach-avoid tubes (BRATs) through Bellman-Isaacs value propagation, and uses gradient-free value probes to construct approximate teacher actions, turning adversarial actor learning into supervised policy learning.

  8. arXiv cs.RO65

    MO-Playground: Massively Parallel Multi-Objective Reinforcement Learning for Robotics

    The authors propose MORLAX, a GPU-native multi-objective reinforcement learning algorithm, and MO-Playground, a pip-installable GPU-accelerated multi-objective environment library. They can approximate Pareto sets within minutes, achieving 26-271x speedups over traditional CPU methods and up to 19x speedups over prior GPU methods, while learning Pareto front hypervolumes that are superior. The team also implemented a custom BRUCE humanoid robot environment, learning Pareto-optimal locomotion policies in simulation across 6 practical objectives such as smoothness, energy efficiency, and arm swinging.

  9. arXiv cs.RO58

    Study Reveals Effect of Policy Standard Deviation on Loss of Plasticity in Online Reinforcement Learning

    The paper analyzes loss of plasticity (LoP) in isotropic Gaussian policies within Proximal Policy Optimization (PPO), noting that the policy standard deviation σ significantly affects the extent of LoP, with theoretical and experimental support. The experiments include LoP benchmark comparisons and real-world policy deployment on a 7-DoF robotic arm; the authors also use these findings to explain why common LoP mitigation methods are effective.

  10. arXiv cs.RO71

    Research team uses differentiable simulation to learn quadrotor dynamic narrow-gap traversal from scratch

    The research proposes a differentiable moving narrow-gap modeling method that gates centering, alignment, and smooth collision terms by signed distance to a time-varying gap plane, turning sparse traversal events into dense pathwise objectives, and integrates this into a visual-feature-to-command policy trained in a single stage from random initialization, without a teacher, planner, or distillation. In simulation, path gradients clearly outperform score-function gradients, reaching a 99.3% success rate after a few minutes of training on a single GPU. The policy transfers directly to a real quadrotor using only onboard vision and inertial measurements, successfully traversing the dynamic narrow gap in 8 of 10 real-world trials.

  11. arXiv cs.RO60

    Reinforcement Learning-Based Autonomous Fixed-Wing Drone Interception

    A team at Delft University of Technology in the Netherlands proposed a fixed-wing interceptor controlled by a neural network that maps filtered monocular vision observations directly to actuator commands; the policy was trained in a custom Python environment using reinforcement learning and proximal policy optimization (PPO). In simulation, the policy outperformed two classical guidance controllers under most conditions, and it has successfully intercepted maneuvering fixed-wing targets in real flight. This work provides validation for the autonomous control of fixed-wing interception drones.

  12. arXiv cs.RO70

    MARL-Exo: Multi-Agent Reinforcement Learning Co-Adapts Hip Exoskeleton Control to Reduce Metabolic Energy Cost

    The study proposes MARL-Exo, a multi-agent reinforcement learning framework in which a full-body musculoskeletal model and a bilateral hip exoskeleton policy are co-trained in simulation in two stages; the exoskeleton observes only delayed hip kinematics and past commands. In simulation, the metabolic policy reduced modeled muscle metabolic power by 13.8% compared with walking without the device, and by 8.8% after adding mechanical-power shaping.

  13. arXiv cs.RO62

    VLA-Stem: Adding a Lightweight Safety Mechanism to Vision-Language-Action Models with Reinforcement Learning

    Researchers propose VLA-Stem, a lightweight reinforcement-learning-based safety mechanism that does not fine-tune the underlying VLA policy; instead, it converts its action chunks into motion references and, at inference time, adaptively corrects execution using proprioception and local obstacle information. Across six constrained simulation settings, VLA-Stem achieved an average collision-free task success rate of 52.8%, versus 23.7% for the strongest safety baseline, while preserving the original policy's performance in obstacle-free environments; real-robot experiments also showed reduced collisions under static and dynamic obstacles.

  14. arXiv cs.RO55

    Spiking Neuromorphic Controller Stabilizes Periodic Orbits in Underactuated Robots: A Greedy Lyapunov-Based Approach

    This paper investigates event-driven spiking controllers implemented as spiking neural networks for trajectory tracking in underactuated robots, with the goal of stabilizing a prescribed periodic orbit. The authors reduce orbital stabilization to stabilizing the trivial solution of a periodic linear time-varying system via transverse linearization, and extend a Lyapunov-based greedy design originally developed for LTI systems to this periodic LTV transverse dynamics, thereby constructing a feedback controller. The method's effectiveness is demonstrated on the trajectory stabilization problem for the butterfly robot.

  16. arXiv cs.RO55

    A Structure-Actuation Co-Design Framework for All-Electric Heavy-Duty Robotic Manipulators

    This paper proposes a hybrid discrete-continuous multi-objective bilevel framework for the structure-actuation co-design of all-electric heavy-duty robotic manipulators: the upper level optimizes geometry and actuation topology, while the lower level searches for feasible configurations of motors, gearboxes, and screws for each joint. A system-level coordinator selects actuator packages based on hardware mass, co-design cost, and loss-aware supply energy, and feeds the resulting actuator masses back into the robot model until mass consistency is reached.

  17. arXiv cs.RO59

    Task-Adaptive Friction Margins for Contact Maintenance in Bimanual Non-Prehensile Manipulation

    The paper proposes using signed friction distance to measure friction margin in force space, and derives a task-adaptive margin from the force correction needed to balance pivot moment deviations during bimanual tilting. The method embeds contact margins into a sequence of second-order cone programs (SOCPs), each yielding a globally optimal contact force solution at a given tilt angle, keeping robot contact forces and pivot contact forces away from the nominal friction boundary. Real-world experiments were executed under variations in object properties and model mismatch, and comparisons against fixed-margin and no-margin settings show the importance of adjusting the margin to task demand.

  18. arXiv cs.RO56

    When Black-Box Robot Policies Fail: Online Sensor Diagnosis and Repair with a Transparent Surrogate

    The study proposes a structured surrogate model that separates periodic gait from sensor-dependent correction terms, diagnosing and repairing corrupted inputs externally around a frozen locomotion policy. On simulated Go2, offline diagnosis raises walking success from 259/640 to 429/640, while online repair reaches 382/640, with no repair triggered in 64 held-out fault-free episodes. The authors note that the evidence remains limited to a single policy and a partly screened simulated fault set, and that robot foundation model and hardware validation have not yet been conducted.

  19. arXiv cs.RO62

    HC2Robot: Multi-Terrain Humanoid Parkour with Predicted Foresight and Hyper-Connection Transformers

    HC2Robot proposes a deployable perceptive humanoid parkour framework composed of a Predicted Foresight Module (PFM) and a Hyper-Connection (HC) Transformer. The PFM predicts, from deployable observations, the timing, location, and support quality of each foot's next touchdown event, converting privileged supervision into a deployable latent representation.

  20. 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.

  21. 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.