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#Motion control
Oct 27Tue
  1. arXiv cs.RO65

    ThinkLocoManip: A Whole-Body Control Agent Framework for Legged Loco-Manipulation

    The authors propose ThinkLocoManip, a whole-body control agent framework for legged loco-manipulation that uses an interleaved reasoning-and-acting paradigm to handle long-horizon tasks in large-scale environments. The framework orchestrates perception, navigation, manipulation, and locomotion as composable tools, and it designs cross-domain coordination skills and a keyframe thinking mechanism to support error recovery. In real-robot experiments, its success rate was 77%, more than double the 37.1% of the strongest plan-then-act baseline.

  2. arXiv cs.RO68

    HumanoidNDM: Closing the training-deployment gap in humanoid motion tracking with a neural dynamics model

    The study proposes HumanoidNDM, an online fine-tuning framework for humanoid motion tracking driven by a neural dynamics model trained on real robots, aimed at closing the dynamics gap between training and deployment. The method includes low-frequency reference perturbation data collection with an adaptive intensity mechanism, training a dynamics model based on proprioceptive history in Euclidean space combined with differentiable forward kinematics, and directly fine-tuning the policy through backpropagated model gradients.

  3. arXiv cs.RO55

    Learning Functional Specialization for Dual-Arm Robot Control

    The study compares, in a simulated musculoskeletal system with dual communication-loop controllers, the effects of differentiated versus shared controller objectives and fixed versus balanced hand-task assignments on dual-arm control using a 2×2 design. Functional specialization was strongest when differentiated objectives were combined with a fixed assignment consistent with contralateral control routing, achieving a dual-arm success rate of 81.33% and the highest control efficiency. This advantage widened as motor noise increased and narrowed under stronger energy penalties. Under differentiated-fixed training, swapping the roles of the two hands reduced the success rate by 80.46 percentage points, whereas under shared-balanced training it fell by only 1.51 percentage points.

  4. arXiv cs.RO52

    Ground Friction Assessment Based on Robot Tire-Ground Interaction

    The study drove a skid-steer robot at set speed profiles over five surfaces with different friction characteristics, identifying surface type by the deviation between inertially estimated velocity and wheel odometry velocity. Response delay measurements showed a consistent surface-dependent trend, with the longest delay on soap and the shortest on carpet; spectral features achieved 97.1% balanced classification accuracy under record-level cross-validation. The authors conclude that both the magnitude of velocity mismatch and frequency-domain features contain tire-ground interaction information, which can be used for surface identification and for slip-aware wheel odometry weighting in state estimation.

  5. arXiv cs.RO36

    Quantum Closed-Loop Inverse Kinematics via Hilbert-Space Encoding of Robot Kinematics

    The paper proposes a structure-preserving quantum representation that maps rigid-body transformations and serial-manipulator kinematics to parameterized quantum circuits, encoding spatial directions in single-qubit states and reconstructing end-effector poses from Pauli expectation values. Using the parameter-shift rule, the manipulator geometric Jacobian can be reconstructed from derivatives of quantum observables, analytically equivalent to the classical result under ideal readout. On this basis, the authors propose a hybrid closed-loop method, Quantum Closed Loop Inverse Kinematics, which uses the quantum-derived pose and Jacobian information to solve inverse kinematics.

  6. arXiv cs.RO53

    SHAPE: An Energy-Based Fault Detection and Isolation Framework for Flat Port-Hamiltonian Mechanical Systems

    The paper proposes an energy-based fault diagnosis framework, SHAPE, which combines port-Hamiltonian variables and differential flatness to generate nominal power references from planned motion, constructing mechanical port-power residuals without a dedicated dynamic observer. In simulations of a six-degree-of-freedom Kinova manipulator under model predictive control, SHAPE-CWT achieved an average accuracy of 84.69% on 3000 held-out tests, compared with 81.67% for torque residuals and 97.17% for a generalized momentum observer. The authors note that severity-excitation profiles and offline velocity perturbations limit the method's diagnostic capability.

  7. arXiv cs.RO62

    HA-DiPO: A Diffusion Policy Optimization Framework for Heterogeneous Robot Cooperative Transport

    The authors propose HA-DiPO, a hierarchical multi-agent reinforcement learning framework operating under centralized training with decentralized execution (CTDE) for heterogeneous legged robots cooperatively transporting an unfixed shared payload. At the high level, a centralized critic jointly trains each robot's independent coordination policy, and diffusion policies represent multimodal coordination responses; at the low level, embodiment-specific locomotion policies track the coordination commands. The policies are learned from scratch through reward-driven simulation interaction, and at execution they use only local observations with no inter-robot communication.

  8. arXiv cs.RO71

    MimicAll: A Structured Embodiment Conditioning Framework for Cross-Humanoid Whole-Body Motion Tracking

    MimicAll is a reinforcement learning framework that encodes global and joint-alignment conditions using structured embodiment descriptors, enabling heterogeneous humanoid robots to share a whole-body motion tracking policy. Training covers 4,096 variants across 16 humanoid robot families, and a benchmark of 464 bodies across five distribution strata was built. With the same amount of training transitions, shared training achieved a 99.4% 30-second success rate on randomized G1 bodies in 100STYLE, versus 42.9% for single-body specialized policies.

  9. arXiv cs.RO56

    SCET-CBF: A Speed-Calibrated Soft Guidance Method for One-Step Dynamic Navigation

    Xiaoyu Han of University College London and Dongkun Han of the Chinese University of Hong Kong proposed SCET-CBF, a speed-calibrated soft guidance law for one-step dynamic navigation. It uses a short lateral escape maneuver to provide an allowable approach speed, and lets a quadratic program weigh the two effects of braking when choosing acceleration and steering.

  11. arXiv cs.RO66

    Active Reachability Learning for Humanoid Recoverability Monitoring

    The paper proposes an active reachability learning framework for learning the stochastic finite-horizon recoverability envelope of a given fallback policy, so that fallback can be triggered before an unrecoverable fall occurs. The method reframes data exploration as a reach-avoid problem, in which an uncertainty-guided explorer discovers unseen states while avoiding confidently unrecoverable regions, and combines temporal-difference and Monte Carlo objectives to improve in-distribution value prediction and uncertainty estimation. The authors evaluate on stochastic benchmarks and a 29-DoF Unitree G1 humanoid robot, reporting clear improvements in monitoring coverage and accuracy, and zero-shot transfer to real hardware.

  12. arXiv cs.RO70

    Neuromorphic Decision Dynamics: Deadlock-free Safe Navigation in Congested Environments

    The authors propose Neuromorphic Decision Dynamics (NDD), a decentralized, event-driven controller for safe navigation in congested environments, with guarantees on safety, liveness, robustness, and scalability. NDD requires no communication, preassigned priorities, or models of neighbors' objectives; by coupling internal decision and attention states to physical states and the relative motion observed locally, it enables agents to self-organize into local conflict groups and form a passage order.

  13. arXiv cs.RO62

    RAIWAY: 20-inch large-wheel bipedal robot uses contact-informed blind locomotion control to traverse rugged terrain

    A Korean team proposed the RAIWAY wheeled bipedal robot and a contact-informed control method. The robot is equipped with 20-inch wheels and can roll across irregular and discontinuous terrain. The method distills multi-contact events into a latent representation in simulation, and at deployment infers it online from proprioception alone, requiring no contact sensors. Real-world tests covered steep slopes, gravel, studded surfaces, and rocky terrain, and demonstrated continuous ascent on outdoor stairs with step heights up to 15 cm, outperforming baselines without the contact-informed mechanism.

Oct 26Mon
  1. Guyuehome49

    NeuroMechFly: MuJoCo simulation of hexapod locomotion and sensorimotor closed loop in Drosophila

    A digital fruit fly reconstructed from micro-CT scans achieves hexapod walking in MuJoCo; the tripod gait self-organizes from phase-oscillator phase-locking, with reflex correction rules and adhesion measurements added. Its compound-eye vision outputs 721 ommatidia per eye, and all conclusions come from complete simulations with fixed seeds.

  2. New Scientist Robots58

    Cortical Labs uses a live-cell biological computer to control a robot dog's movement

    Cortical Labs' biological computer CL1, built from living cells, was trained in a simulated 2D environment and then used to control the movement of a real robot dog. A paper from the company that has not yet been peer-reviewed shows that the neural network improved over time when continuously fed the same data, and Ferrante Neri of the Education University of Hong Kong considers this preliminary evidence of learning from limited experience. Cortical Labs says the biological computer may consume less energy than digital approaches, and its cells come from blood stem cells donated with donor consent.

  3. 机器人全球资讯 · 微信公众号47

    Embodied AI robot mountain-climbing training ground built at foot of Mount Tai, replicating 3.8 km Mount Tai course to prepare for first CMG World Robot Mount Tai Climbing Competition

    Taiying Technology Group has built an embodied AI robot mountain-climbing training ground at the foot of Mount Tai, replicating the 3.8 km, more than 3,100-step terrain of the Mount Tai section from Hongmen to Zhongtianmen, with 11 test tracks for humanoid robots and quadruped robot dogs to test and tune for the first CMG World Robot Mount Tai Climbing Competition. The training ground includes special tracks such as low-recognition steps, steep, slippery, narrow steps, and S-shaped sharp-turn steps, focusing on assessing environmental perception, dynamic balance, and gait-switching capabilities. It has been selected as one of Shandong Province's first batch of key-supported regional hub training grounds.

  4. Reddit r/robotics58

    Research team uses a single control policy to let Unitree G1 climb ladders and overcome obstacles

    Researchers developed a single control policy that lets Unitree's G1 complete ladder climbing, crawling, obstacle traversal, stepping stones, and recovery after falls by deciding the placement of nine different body parts. The robot can use its hands, elbows, knees, feet, and even pelvis for support rather than relying only on walking, achieving a 92.5% success rate on a simulated obstacle course, but how this flexibility performs in unpredictable real-world environments remains a bigger test.

  5. 高工咨询 · 微信公众号58

    Inovance Debuts Wheeled Humanoid Robot InoBot W1 at CIIF, Connected to Automotive Welding Demonstration Line

    At the 26th China International Industry Fair, Inovance unveiled its wheeled humanoid robot InoBot W1 for the first time and connected it to an automotive welding demonstration production cell, where it works with a vision system and an arc welding robot to autonomously complete tasks such as workpiece loading and unloading and resetting.

  6. 高工咨询 · 微信公众号71

    Agibot launches safety-certified heavy-duty embodied robot Genie G2 Max with 20 kg single-arm payload and 40 kg dual-arm payload

    On October 26, Agibot launched the safety-certified heavy-duty embodied robot Genie G2 Max (AGIBOT G2 Max). Official figures are 20 kg per arm and 40 kg for both arms (50 kg peak). Its 7-DOF force-controlled dual arms achieve repeat positioning accuracy of ≤±0.5 mm, with vertical operation covering 0.1 m to 2.5 m.

  7. Reddit r/robotics65

    DUET: Humanoids learn new manipulation skills without retraining the balance controller

    DUET proposes separating a humanoid robot's manipulation policy from its balance controller and combining the two commands only at the robot interface, so learning new manipulation skills no longer requires retraining the gait controller. The authors say this removes the hidden cost of retraining the balance controller every time the manipulation policy changes; the paper, code, and a full summary are in the first comment.

  8. 雷科技71

    Agibot launches safety-standard heavy-duty embodied robot Genie G2 Max with 40 kg dual-arm rated payload

    Agibot launched the safety-standard heavy-duty embodied robot Genie G2 Max on October 26, targeting depalletizing and palletizing, material transfer, and production-line feeding. It has a 20 kg single-arm payload, a 40 kg dual-arm rated payload, a 50 kg peak payload, and repeat positioning accuracy within ±0.5 mm. The robot uses 7-DOF force-controlled dual arms, has a vertical working range of 0.1 to 2.5 meters, features dual-battery hot swapping and autonomous recharging, and is compatible with the Genie Studio 2.0 embodied work platform. Agibot disclosed that in e-commerce warehousing scenarios its overall task success rate exceeds 99%, with 80 boxes depalletized and 75 boxes palletized per hour.

  9. 优宝特 · 微信公众号40

    Why are robots competing in the CMG World Robot Mount Tai Climbing Competition? Yobotics explains the real-world Mount Tai trial

    The first CMG World Robot Mount Tai Climbing Competition is about to begin. The course runs from Hongmen to Zhongtianmen and covers more than 3,100 natural stone steps, with steep slopes and sharp turns that become slippery under the influence of temperature, dew, and precipitation. It is designed to test the autonomous, stable mobility of humanoid and quadruped robots in unstructured mountain terrain. The event features humanoid and legged robot racing, loaded transport and exoskeleton assistance special events, and an all-terrain mobility pathfinding challenge for wheeled, tracked, and wheel-legged composite configurations.

  10. 中国机器人网 · 微信公众号40

    Bionic beetle robot BeetleBot unveiled, insect-level agility could change post-disaster search and rescue

    Researchers have developed a bionic robot called BeetleBot that mimics a beetle's body and limb structure and gait control, allowing it to move stably across complex terrain such as rubble, sand, and narrow crevices. Experiments show it can climb over complex obstacles and quickly recover its movement state after external impacts or being flipped over. The robot is expected to be used in post-disaster search and rescue, exploration of hazardous areas, inspection of industrial facilities, and environmental monitoring.

  12. OFweek Robotics47

    How Topstar's humanoid dual-arm robot AOI glass-wiping workstation is deployed in optical workshops

    Topstar's humanoid dual-arm robot AOI glass-wiping workstation uses ±0.05mm repeat positioning accuracy, combined with visual positioning and flexible force control, to achieve targeted wiping without damaging glass in optical workshops. The workstation integrates inline AOI inspection and wiping in a single unit, replacing the traditional two dedicated machines and two workstations, and relies on a high-resolution industrial camera and an edge AI computing unit to perform defect detection and classification positioning. It has now been validated in the workshops of a leading 3C customer and an optical customer; ordinary production line workers can quickly learn to operate it, helping customers improve overall workshop yield.

  13. arXiv cs.RO60

    CATS: Smooth Control of Deep Reinforcement Learning Policies via Temporal Regularization

    The authors propose the CATS method, which combines a temporal penalty with linear ramp-up to suppress action oscillations in deep reinforcement learning policies. The paper proves that the temporal penalty can bound the expected action differences between current states that share the same successor state, thereby producing a spatial smoothing effect. In simulations and real-world environments, the method is verified to significantly reduce action oscillations without degrading task performance, with very low computational overhead. The work was submitted to ICRA 2027.

  14. arXiv cs.RO60

    Coupled Design and Analysis of a Nonlinear Parallel Mechanism for Dynamic Humanoid Legs

    The paper proposes a series-parallel humanoid leg design that combines a knee parallel mechanism, an ankle parallel mechanism, and a nonlinear knee-ankle coupling, with all actuators placed near the hip. The authors derive coupled nonlinear transmission models and validate them in simulation and on hardware. The 6 kg leg achieves an effective reflected mass of 0.37 kg at the foot end; the coupling passively assists ankle dorsiflexion to lift the foot when the knee flexes, and assists plantarflexion for push-off when the knee extends.

  15. arXiv cs.RO58

    Environmental Constraint Exploitation: Using environmental contact to simplify robot motion planning

    The paper proposes Environmental Constraint Exploitation (ECE), a method that deliberately makes contact with the environment to reduce the dimensionality and computational complexity of motion planning. The authors integrate ECE into an RRT-based planner, biasing exploration toward task-relevant regions and using contact to reduce uncertainty and improve execution robustness, and demonstrate practical results in real-world scenarios. The work consolidates and extends prior research, showing that ECE can simplify motion planning and enhance adaptability in complex environments.

  16. arXiv cs.RO60

    CARE: Consistent Action-Response Execution for Human-Guided Robot Transport

    The authors propose Consistent Action-Response Execution (CARE), which grounds local control in executable-action-indexed joint human-robot transitions, so that the same executable action simultaneously determines operator response prediction, joint human-robot rollout, and candidate selection. On training and held-out maps, CARE maintains near-saturated performance when the indicated route remains locally passable, and achieves the highest success rate and service availability when the operator can continue but the robot must detour.

  17. arXiv cs.RO58

    FH-HBTS: A Haptic Bilateral Teleoperation System for Free-Hand Dental Procedures

    The authors propose FH-HBTS, a haptic bilateral teleoperation system for free-hand dental procedures, featuring a mechanical end-effector compatible with standard clinical tools and equipped with an endoscopic camera. The system improves the operator's sensory awareness and motor accuracy through motion and force correspondence between the operator and the robot, and limits interaction forces by scaling the motion references provided to the admittance controller based solely on measured contact forces, thereby achieving force limitation without prior knowledge of the environment. Technical evaluations and in vitro preclinical studies on dental models showed that the system improves the naturalness, safety, and precision of teleoperation.

  18. arXiv cs.RO68

    Retiming Robot Demonstrations with a Learned Speed Prior

    The study proposes learning a speed prior from large-scale demonstration data: given a task instruction, a motion path, and images along the path, it predicts a distribution of execution speed along the path and uses a selected quantile as a unified clock to retime each demonstration. The method fits the speed prior on robot teleoperation data and on human handheld-gripper demonstrations, retimes robot demonstrations to the handheld pace, and then fine-tunes a vision-language-action policy; at execution, a real-time optimal-control layer tracks the predicted action chunks under robot constraints.

  19. arXiv cs.RO72

    ViT-H: Generating humanoid motions directly from video with diffusion models and post-training via tracking feedback

    ViT-H proposes a generation-plus-tracking video-to-humanoid control framework. It uses a diffusion model to generate humanoid reference motions directly from monocular video, skipping human motion reconstruction and retargeting, and uses motion-tracking results to post-train the generator with direct preference optimization. The authors also present an autoregressive variant that enables real-time humanoid video teleoperation with sub-400 ms latency. Experiments on the Unitree G1 show that ViT-H outperforms existing reconstruction-retargeting-tracking pipelines in motion quality and executability, and that performance improves with model size and with pre-training and post-training data scale.

  21. arXiv cs.RO58

    GTB: A Trajectory Bundle Graph Motion Planning Method for Nonlinear Dynamics

    The authors propose the Graphs of Trajectory Bundles (GTB) method, which combines sampling-based motion planning, trajectory optimization, and graph search for motion planning of systems with nonlinear and even black-box dynamics. Based on a graph-of-convex-sets decomposition of the workspace, the method iteratively constructs a graph of trajectory bundles that captures the local dynamics at each vertex, then solves a shortest-path problem on the graph to obtain trajectories that satisfy the system dynamics and constraints such as collision avoidance and safety in the limit. The authors claim it can handle highly nonlinear, unstable, and even black-box dynamics with complex constraints.

  23. arXiv cs.RO60

    From Tabletop to Walking Robot: Deploying a VLA Model on a Legged Manipulation Robot

    The research team deployed a VLA model on a Unitree Go2 quadruped equipped with an AgileX Piper robotic arm, achieving closed-loop mobile manipulation, and reported choices such as model variant, action horizon, and demonstration conventions that made real-robot deployment feasible. For the pick-and-place task, expanding demonstration coverage reduced held-out end-effector error by 30%, and adding secondary skills such as pushing introduced new behavior modes without degrading baseline performance. Closed-loop real-robot experiments showed that dataset composition affects task success.

  24. arXiv cs.RO63

    Lunar swarm robots exhibit emergent cooperative thermoregulation via reinforcement learning

    The study used reinforcement learning to train a single policy shared by a set of twenty robots, with only the mean temperature of the five coldest robots at the end of the night as a terminal reward, and two behaviors emerged: aggregation and rotation between the core and the edge. The minimum stored energy required per robot for survival was measured at 155.6 +/- 1.3 pcm-h (1 pcm-h is 7.2 kJ of stored heat), 37% lower than stationary huddling and within 13% of a physics-based lower bound; under an energy budget in which stationary huddling would lose six robots, all robots survived.

  25. arXiv cs.RO63

    BM2PC: A Bayesian Multimodal MPC Framework for Dynamic Manipulation of Deformable Linear Objects

    This paper proposes BM2PC, a Bayesian multimodal MPC framework for dynamic manipulation of deformable linear objects (DLOs). It formulates multimodal planning as a set optimization problem that balances control quality and behavioral diversity, and maintains multiple candidate control modes through incremental updates.

  26. arXiv cs.RO64

    PrefO2O: Zero-Shot Preference Reinforcement Learning via Offline-to-Online Latent Preference Transfer

    PrefO2O proposes an offline-to-online latent preference transfer framework that enables zero-shot preference-based reinforcement learning (PbRL) on target tasks without any human feedback. The method learns a task-invariant latent preference space from a small amount of offline preference data on source tasks, disentangles transferable preference structures from task-specific variations, and uses confidence-weighted optimal transport alignment to generate pseudo-preference supervision for online reward adaptation.

  27. Reddit r/robotics42

    Menlo Research Marks One Year of Open-Source Humanoid Robot Asimov 1: From Sketches to Deployment

    Menlo Research's Emre reviewed the progress of the open-source humanoid robot Asimov 1 in the year since the project began, saying it has advanced from sketches to the deployment stage. The team cut a short film from self-shot footage to document the year, and opened discussion on hardware, locomotion strategies, and the detours taken along the way.