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#Motion control
Oct 21Wed
  1. 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.

  2. LeadeRobot40

    Honor humanoid robot Lightning races marathon sub-2 runner Sabastian Sawe over 20 meters

    Honor's self-developed humanoid robot Lightning (HONOR Robotics D1) held multiple 20-meter sprint interactions on October 20 with marathon sub-2 runner Sabastian Sawe and trail-running champion Zhao Jiaju. Lightning stands 169 cm tall with an effective leg length of 1.05 m; its 100-meter sprint relies on pure visual recognition through hip cameras, and it previously won the Beijing Yizhuang humanoid robot half-marathon in 50 minutes 26 seconds. Honor's move is brand preheating for its consumer-grade humanoid robot focused on three scenarios: shopping malls, factories, and homes.

  5. arXiv cs.RO60

    FIBER: An Action-Aligned Latent Planning Framework for Humanoid Loco-Manipulation

    The paper proposes the FIBER framework, which uses the executable latent space of a frozen behavioral foundation model (BFM) as a shared planning representation for heterogeneous interaction skills. Its latent policy projection (LPP) maps independently trained task policies into this representation, enabling low-frequency planning while retaining the BFM's high-frequency state feedback; a single perceptive flow planner is then trained entirely offline. The paper also proposes an adaptive-horizon BFM (AH-BFM) trained with multi-gamma forward-backward learning.

  6. arXiv cs.RO56

    arXiv paper explores using nonlinear body oscillations to generate natural quadruped gaits

    The paper proposes a proof-of-concept framework that incorporates a robot's nonlinear dynamics into the design process, enabling multi-gait locomotion to emerge from nonlinear resonances shaped by gravity, inertia, and elasticity. The authors introduce the highly compliant quadruped robot eBert, use new theoretical tools to identify six nonlinear normal modes (NNMs), and validate their existence in simulation and hardware. Using black-box optimization to determine step length, simulations show that each NNM naturally develops into a distinct gait with different speeds, and most of these largely transfer to the physical hardware.

  7. arXiv cs.RO50

    Readiness Barrier Functions: Forward-Invariant Control Authority for Overactuated Multirotor Allocation

    This paper proposes enforcing a control barrier function at the torque level via a quadratic program in the allocation null space, keeping the control authority of an overactuated multirotor forward-invariant. In simulations of a hexarotor and a fully actuated octorotor, across 300 randomized tasks, the greedy maximization scheme violated the certified lower bound in up to 51% of authority-scarce tasks, whereas the proposed filter never left the certified set, and in the hardest torque-reversal scenario the torque error was 79 times smaller; the robust variant maintained the lower bound on all 150 perturbed objects.

  8. arXiv cs.RO55

    PAKE: Whole-body loco-manipulation via partial motion generation and low-level imitation control

    PAKE proposes a whole-body loco-manipulation control framework that decomposes the problem into partial reference motion generation and low-level imitation control. It introduces a Kinematic Normalizing Flow (KNF) model trained on a large-scale motion generation dataset to generate diverse partial motion references; a high-level controller navigates the KNF latent space to exploit redundant solutions, while a low-level controller learns dynamically consistent motion execution.

  10. arXiv cs.RO45

    Learning Action and Style Priors for Joint-Torque Control in Legged Robots

    A study on deep reinforcement learning for legged robots proposes using latent actions learned from a small set of expert demonstrations as an inductive bias to help joint-level torque control policies explore more efficiently. This prior allows the agent to not be limited by the demonstration reward level and significantly improves performance on transfer tasks; when combined with style rewards for imitation, it can more closely match expert behavior. Related videos have been made public.

  11. arXiv cs.RO38

    Distributed Platoon Control for Safe and Smooth Crossing at an Unsignalized Intersection (Part I): Stability Analysis

    A study proposes a distributed platoon controller that analyzes the asymptotic behavior of multi-vehicle traffic at an unsignalized intersection through closed-loop path modeling. It shows that under mild conditions on the controller gains, safe crossing is guaranteed during the transient phase, and in steady state all vehicles cross smoothly at a common constant speed while avoiding collisions. Unlike traditional bi-level decoupled designs on open paths, the method couples intersection control with platoon convergence analysis. Its effectiveness was validated in a high-fidelity CARLA simulator using multiple commercial vehicle models.

  12. arXiv cs.RO78

    SuperMimic: Scaling Humanoid Whole-Body Control with One Differentiable World Model

    SuperMimic uses a differentiable world model of shared dynamics to train the world model and the tracking policy together in a single training loop, giving the policy analytic gradients through backpropagation-through-time over short imagined rollouts.

    Why it matters: The paper lays out a concrete path for replacing large-scale reinforcement learning training with a single differentiable world model and provides compute comparison data, making it possible to assess its impact on scaling humanoid whole-body control.

  13. arXiv cs.RO46

    RILC: Reinforced Iterative Learning Control for Elastic-Joint Robotic Arms, Achieving Cross-Trajectory Generalization with Convergence Guarantees

    This study proposes Reinforced Iterative Learning Control (RILC), which couples Iterative Learning Control (ILC) with Reinforcement Learning (RL) for trajectory tracking of elastic-joint robots, addressing the problems that traditional ILC must learn each new trajectory separately and that RL has high sample complexity and limited tracking accuracy.

  14. arXiv cs.RO62

    DirectWAM: Distilling Cascaded World-Action Models into Single-Step Action Prediction

    The paper proposes DirectWAM, which converts pretrained video and action experts into single-step predictors that require no stochastic sampling while retaining the explicit future-to-action cascade structure. After learning a single-step action model, the method trains the future predictor by decoding actions from its own predicted futures and backpropagating demonstration action error.

  15. arXiv cs.RO47

    Hierarchical QP control and authority-guided recovery planning achieve high-precision RCM motion for ophthalmic robots

    A study proposes a high-precision remote center of motion (RCM) servo architecture for ophthalmic robots, using a hierarchical QP controller and authority-guided recovery planning to address servo oscillation caused by conflicts between RCM and tip-tracking objectives. In a 200-scenario kinematic benchmark, the approach achieved a 100% reach rate for 128 near-singular targets and a 99% reach rate for 72 near-joint-limit targets, with software RCM residual below 0.142 mm.

  16. arXiv cs.RO57

    Research on Enumeration-Guided Training of Neural Hamilton-Jacobi Safety Certificates

    This research proposes a verification-guided training framework that uses probabilistic enumeration of safety constraints to detect forward-invariance violations, generating counterexample sets that are more compact and closer to the failure boundary than those from standard adversarial attack methods. Fine-tuning a pretrained HJ value function with these counterexamples expands the certifiable region and shortens certification time across all tested benchmarks; the authors also introduce a deployment-time shielding mechanism based on the refined certificates, achieving zero safety constraint violations in Isaac Sim physics simulation and real-world autonomous racing experiments compared with a nominal controller and an LQR-based backup strategy.

  18. arXiv cs.RO47

    Orthogonal Residual Learning for Adaptive Sideslip Angle and Cornering Stiffness Estimation

    An orthogonal residual learning method constrains a model corrector via null-space parameterization to be orthogonal to cornering stiffness sensitivity, preventing the corrector from absorbing stiffness-change information and suppressing online adaptation. In full-vehicle simulation, the method reduces sideslip angle error by 12.7% to 22.0% and maintains the same adaptation speed as an uncorrected filter under step changes in stiffness. On public real-vehicle data, a 430-parameter orthogonal corrector retained more response to nominal stiffness offsets than an unconstrained corrector of the same size.

  19. arXiv cs.RO46

    ThERMO Thermal Imaging Speed Optimization Algorithm Improves UR10e Robot Autonomous Tumor Resection Accuracy

    The ThERMO closed-loop thermal feedback algorithm completed autonomous electrosurgical tests on ex vivo porcine tissue using the UR10e-based ASTR system, reducing thermal injury by up to 52% and improving margin accuracy by up to 59%, without requiring data-driven training or iterative tuning. The algorithm adjusts cutting speed via real-time thermal imaging; histological analysis and 3D photogrammetry verified thermal injury extent and margin accuracy, outperforming two fixed-speed control methods.

  20. arXiv cs.RO47

    Real-Time Disturbance-Aware Koopman Model Predictive Control for Quadrotors in Spatially Varying Winds

    The study proposes a disturbance-aware Koopman control family (DA-KCF) framework that preserves an offline-identified nominal model, learns only a disturbance correction online, and embeds it into model predictive control (MPC) to optimize position and yaw references. The reference-dependent KCF is first learned from simulation data and then calibrated with limited hardware data; during deployment the nominal model remains fixed, and a lightweight residual ensemble learns a low-dimensional disturbance correction from causal state-command-state transitions.

  21. arXiv cs.RO38

    Determination of the Human-Robot Interaction Workspace for Head Stabilization Based on a 6-DoF Stewart Platform Robot

    A study conducted human volunteer experiments using a 6-degree-of-freedom (6DoF) Stewart platform robot to determine the biokinematic workspace of the head-neck system, establishing foundational data for human-robot interaction in precision head stabilization. The system integrates real-time optical motion tracking and can dynamically compensate for head movement without physical restraint, for use in precision-demanding medical scenarios such as neuroimaging and intracranial radiotherapy.

  22. arXiv cs.RO46

    Design and Control of a Multimodal Legged-Aerial Robot via Separate Control Contraction Metrics

    A paper proposes a design and control method for a multimodal quadrupedal aerial-legged robot, using separate Control Contraction Metrics (CCM) to search offline for contraction metrics for walking and flight respectively, unified by an online energy-based Control Lyapunov Function Quadratic Program (CLF QP) to achieve optimal cross-domain control allocation.

  23. arXiv cs.RO58

    XoNav: Using a Hip Exoskeleton as a Personalized Haptic Interface for Assisted Navigation

    XoNav is a four-actuator hip exoskeleton that renders navigation commands directly as haptic cues at the hip joints, using sagittal-plane actuators to render an Advance cue and two-level Braking cues, and frontal-plane actuators to render two-level Steering cues. A bilateral thigh-mounted IMU setup combined with an LSTM gait-state classifier synchronizes cues with the wearer's gait and suppresses them while standing; the classifier achieved 95.2-97.9% recall on unseen users, with mean detection delays of 38.6 ms and 63.1 ms for stance-to-swing and swing-to-stance transitions, respectively.

  24. Reddit r/robotics38

    Cubic Doggo reinforcement learning standing stability training update

    Cubic Doggo used Gymnasium and SB3's PPO in MuJoCo to train standing while minimizing pitch and roll, and deployed the ONNX file back to MuJoCo, raising resistance to horizontal push from 3.5N to 4N. The second phase added "push recovery training," using horizontal force as domain randomization under a curriculum schedule, but after two weeks of training the improvement was limited. Next steps are balancing on fixed and varying slopes, then deployment to Gazebo and a physical robot.

  26. NASA44

    NASA's new Mars helicopter rotor blade roamx-0201 could extend single-flight distance to 18.7 km

    NASA researchers, working with Imperial College London and Nagoya University, have designed a new Mars helicopter rotor blade airfoil called roamx-0201. Through high-fidelity simulations and wind tunnel tests, it could increase the maximum single uninterrupted flight distance from Ingenuity's 0.7 km to up to 18.7 km while carrying scientific instruments.

  27. Fudan Multimodal Embodied Intelligence38

    Fudan University's Qi Lizhe team wins innovation gold award at 26th CIIF university exhibition area for "space-air-ground integrated desert planting robot"

    The "space-air-ground integrated desert planting robot" project from Fudan University's Qi Lizhe team won the innovation gold award in the university exhibition area at the 26th China International Industry Fair. The robot integrates autonomous perception, precise navigation, and integrated planting functions, combining BeiDou, inertial navigation, LiDAR, and stereo vision positioning, and has completed field operation verification in the desert of Dalad Banner, Ordos, Inner Mongolia. The team has filed 7 core invention patents, and the single-vehicle platform will later integrate four planting mechanisms, with operating efficiency reaching more than 10 times that of traditional manual work.

Oct 20Tue
Oct 19Mon
  2. 高工咨询 · 微信公众号66

    Fa'ao Robotics completes nearly 1 billion yuan in C+ and D rounds, showcases force-controlled humanoid arm and intelligent workstation at CIIF

    Fa'ao Robotics announced at the 2026 China International Industry Fair that it has completed its C+ and D funding rounds, with a cumulative scale of nearly 1 billion yuan. Investors include Meituan Dragon Ball, Yongxin Capital, Shishan Phase II, and Suzhou Venture Capital, among others. The funds will be invested in expanding production capacity for core components and in scaling up R&D for industrial embodied intelligence.

  3. 深蓝AI · 微信公众号62

    Shenlan College livestream preview: Robot motion control from two-stage to one-stage

    Shenlan College will hold a livestream on October 21 titled "Robot Motion Control from Two-Stage to One-Stage," featuring Ye Lei, a PhD student jointly trained by Harbin Institute of Technology and Shanghai Innovation Institute. Centered on three works—KCFRC, PegasusFlow, and PredActor—the session will explain how robot motion control moves from a two-stage approach of planning first and then tracking to a one-stage approach that jointly generates states and actions.

  5. arXiv cs.RO45

    Reinforcement Learning-Based Tendon-Driven Bionic Jellyfish Robot Achieves Closed-Loop Depth Control

    A tendon-driven bionic jellyfish robot achieves 150° bending through soft actuators that combine a flexible substrate with discrete constraints, with tendon displacement and bending angle approximately linear. Eight actuators driven by four servos enable stable swimming, attitude adjustment, and self-righting. A reinforcement learning controller trained on this linear actuation characteristic achieved closed-loop depth regulation in both simulation and physical experiments.

  6. arXiv cs.RO60

    Improving legged locomotion policy health via gait-phase-conditioned effective rank of the network Jacobian

    This study analyzes reinforcement learning policies for legged locomotion using the effective rank of the network Jacobian, finding that conditioning on gait phase exposes representation structures invisible from the global rank. Architectures with observation normalization and residual connections allocate more than one additional effective-rank dimension to swing-dominant states than vanilla MLPs do. Based on this, the proposed diagnostic method reduces joint jitter by up to about 2x in simulation and on a physical Spot, and the paper has been accepted to CoRL 2026.

  7. arXiv cs.RO68

    PRISM: Improving Robot Motor Control Policy Learning with Polynomial Representations

    PRISM (Polynomial Representations for Interaction Structured Motor Control) is a physics-inspired policy representation method that learns multiplicative interactions among variables by recursively adding scaled bilinear features. It only requires replacing the first affine layer of a multilayer perceptron (MLP) and can be used for reinforcement learning and imitation learning without changing training objectives, sensing, or controllers.

  8. arXiv cs.RO55

    Research on Asynchronous Upper-Body Task-Space Trajectory Tracking Strategies for Humanoid Robots

    The paper proposes an asynchronous upper-body task-space trajectory tracking framework for humanoid robots, addressing the temporal asynchrony between sparse, low-frequency trajectories output by high-level planners and the high-frequency operation of whole-body controllers. The student policy is initialized through teacher-student distillation, conditioned on the fully cached future trajectory and an execution-time index, and trained with a sliding-window global reward to reduce frame drift; in the task post-training stage, an MPC module completes sparse references into floating-base and upper-body guidance.

  9. arXiv cs.RO55

    Perceptive BFM: A Perception Control Framework for Adapting Human Motion Priors to Robot Terrain

    The paper proposes Perceptive Behavior Foundation Model (Perceptive BFM), a terrain-aware whole-body control framework for humanoid robots that uses local terrain observations from the robot's perspective to adapt contact, posture, and timing while retaining the original kinematic reference as the behavioral interface.

  10. arXiv cs.RO46

    Learning Cross-Coupled and Regime-Dependent Dynamics for Aerial Manipulation

    A study proposes a structured encoder-decoder framework in which a nonlinear history encoder captures the temporal and cross-coupled residual dynamics of an aerial manipulation platform and its robotic arm, while a linear latent-space decoder supports rapid adaptation at deployment time. The framework's linear-in-parameters structure enables closed-form Bayesian updates and integration with real-time model predictive control (MPC). Experiments on a real aerial manipulation platform showed improved residual prediction accuracy, rapid adaptation after dynamics changes, and reduced MPC tracking error under changing payload conditions.

  11. arXiv cs.RO44

    Gaussian Process-Based Robot Skill Learning and Adaptation Framework

    A study published in IEEE RA-L 2026 and presented at IROS 2026 proposes a robot skill adaptation framework based on Gaussian processes (GPs) and sparse via-points, which preserves the kinematic characteristics of skills by using first- and second-order analytical derivatives of trajectory poses. The framework includes three adaptation methods: an optimization agent, a behavior cloning agent, and a reinforcement learning (RL) agent. In simulation and real-world evaluations on three tasks—opening a drawer, pushing a cube, and manipulating a horizontal bar—it outperformed all baseline methods in success rate and demonstrated high cosine similarity and low velocity magnitude error.

  12. arXiv cs.RO50

    TRAIN: A reinforcement learning framework for terrain-aware interactive navigation for quadruped robots

    TRAIN proposes a two-stage deep reinforcement learning framework that, through the TRAIN-Loco and TRAIN-Nav pipelines, enables quadruped robots to achieve terrain-aware navigation in unstructured terrain and human-populated environments. Benchmarks compared quadruped locomotion and robot navigation baselines, and hardware experiments verified its sim-to-real transfer potential.