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#Motion control
  1. OFweek Robotics77

    Apple unveils HALO humanoid robot control system; Unitree G1 achieves 91% task success rate in unfamiliar environments

    Apple's research team published the paper "Perceptive Humanoid Motion from Simulation" on arXiv, unveiling HALO, an autonomous humanoid robot control system, and validating it on the Unitree G1.

    Why it matters: Apple's HALO splits task orchestration, motion generation, and tracking into three layers, and reports comparative results of 91% in unfamiliar environments and 70% on composite tasks, offering a look at its layered design and data synthesis pipeline.

  1. 机器之心 · 微信公众号78

    Yuance Future Releases Anchor 1 Whole-Body Control Model for Humanoid Robots

    Yuance Future has released Anchor 1, a whole-body control model positioned as the System 0 cerebellum layer within a whole-body intelligence architecture. It uses five anchor points—the pelvis, left and right wrists, and left and right ankles—as commands and does not rely on online retargeting.

    Why it matters: The original article provides quantitative comparisons of the whole-body control model's real-machine error and teleoperation collection efficiency, which can be used to assess how the cerebellum layer's precision affects the success rate of upper-layer VLA tasks.

  1. Beijing Humanoid Robot Innovation Center70

    Beijing Humanoid releases Hi-WBC general whole-body control model on "Huisikaiwu" platform

    Beijing Humanoid released the Hi-WBC general whole-body control model on the "Huisikaiwu" platform on November 2. The model combines human motion references with real physical constraints, enabling robots to adjust how they execute tasks as payload, external forces, and terrain change. It uses a continual skill expansion framework based on MoE and policy distillation, Contact-aware Learning that incorporates contact force prediction, and Perceptive Control that introduces terrain awareness.

    Why it matters: The original text discloses Hi-WBC's technical approach to handling payload, external force, and terrain constraints beyond motion tracking, offering insight into how whole-body control is moving from demonstrations toward real work.

  1. arXiv cs.RO84

    HALO: Humanoid Autonomy System Trained on Synthetic Simulation Data Achieves Zero-Shot Real-World Transfer

    The research team proposes HALO, a humanoid robot autonomy system that synthesizes vision-language-action data across roughly one thousand procedurally generated home scenes, eliminating reliance on teleoperation data collection. HALO learns skills including sitting on chairs, navigating to specified objects, stepping sideways through half-open doors, crouching and crawling under tables, and pushing boxes, and can combine them to complete long-horizon tasks. Without ever seeing real home scenes during training, HALO transfers zero-shot to the physical world, achieving a 91% success rate across 360 trials in six unmapped real-world environments.

    Why it matters: The paper presents a complete path from synthesizing vision-language-action data using procedurally generated home scenes to zero-shot transfer to real robots; its approach to data scaling is worth referencing.

  1. arXiv cs.RO80

    Movens-Zero: Scaling humanoid motion foundation models with large-scale human videos

    The research team proposes Movens-Zero, a scalable understanding-generation-execution architecture that supports real-time closed-loop humanoid motion generation under text, speech, music, and video inputs. Its accompanying dataset, Movens-5M, is built from Internet videos through clip generation, quality filtering, semantic annotation, motion recovery, and physical refinement with humanoid retargeting. It contains 15 million human motion sequences and 5 million physically executable humanoid motion sequences, totaling over 10,000 hours.

    Why it matters: This technical report presents a complete path for building a humanoid motion dataset from Internet videos and training a unified understanding-generation-execution model, offering a reference for data scale and architecture design.

  2. arXiv cs.RO78

    ∇Mimic: Efficient Whole-Body Control for Humanoid Robots with Differentiable Simulation

    The authors propose ∇Mimic, which uses exact gradients from a differentiable simulator to train whole-body motion-tracking policies for humanoid robots, reaching a score of 0.82 in 20 minutes and 0.874 in 1.8 hours on a single GPU, whereas the world-model method takes 1.2 hours to reach 0.82.

    Why it matters: The paper presents a complete scheme for training humanoid policies with exact gradients under differentiable simulation and open-sources the engine and code, making it easy to compare the efficiency of first-order and zeroth-order methods.

  1. arXiv cs.RO78

    X-WBC: A Cross-Embodiment Foundation Model for Humanoid Whole-Body Control

    X-WBC is a cross-embodiment humanoid whole-body control foundation framework that separates relatively shared human motion semantics from embodiment-specific physical execution. It uses human-centered command tokens to align full human motion, robot reference motion, and sparse VR observations; a causal Transformer learns reusable temporal structure from mixed multi-robot rollouts, and lightweight robot-specific modules map the shared representation to each robot's proprioception and action space.

    Why it matters: The paper proposes a cross-embodiment humanoid whole-body control framework that decouples shared human motion semantics from each embodiment's execution, and validates joint training on nine simulated embodiments and four real robots.

  1. 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.

  1. 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.

  1. 至简动力 · 微信公众号67

    Zhijian Dynamics Showcases SimpleWAM On-Device Real-Robot Operations and CNC High-Precision Manipulation Research at IROS 2026

    At IROS 2026, Zhijian Dynamics debuted a real-robot demonstration of its self-developed world action model SimpleWAM, with the i7 Pro running on-device to complete tasks such as tape sealing and box packing; the i7 Pro and iX demonstrated motion control capabilities together on site.

    Why it matters: The original article provides specific optimization data for on-device inference latency and real-robot demonstration scenarios, which can be used to assess the practical level of this model's deployment.

  2. Singularity Hub76

    KAIST quadruped robot RAIBO2 completes marathon without battery swap, ~40 miles per charge

    KAIST's quadruped robot RAIBO2 finished the Persimmon Marathon in Sangju, South Korea, in 4 hours 19 minutes 52 seconds, slightly faster than the human average, without swapping or recharging its battery, and still had 34% charge at the finish; the team described the work in a Nature paper last week.

    Why it matters: The research moves energy saving for legged robots from optimizing single components to holistic design across joints and circuits, offering concrete methods transferable to long-duration outdoor robots.

  1. 珞石机器人60

    ROKAE's 10,000th humanoid force-controlled arm rolls off the line and is delivered

    ROKAE's 10,000th humanoid force-controlled arm officially rolled off the line and was delivered on September 29 at its smart manufacturing future factory. The company says it has partnered with more than 80 leading industry partners, and that its force-controlled arms continue to enter real application scenarios. Its humanoid force-controlled product line includes the Xuanji HSA series force-controlled joints with built-in high-precision torque sensors; the Taibai AR series humanoid force-controlled arms, which cover a payload range of 3-20 kg and adopt a humanoid seven-degree-of-freedom and cross-wrist configuration; and the Xuanhe iNex control system, which integrates force control with WBC whole-body motion control.

    Why it matters: The piece explains in fairly specific terms the path of transferring a decade of industrial force-control expertise to humanoid actuation units, as well as the product line composition that supports the 10,000-unit delivery.

  1. CMU Robotics Institute62

    CMU and Siemens Partner to Propose PRISM, a Safety-Stop Monitoring Method for Humanoid Robots

    CMU and Siemens partnered to develop a method for evaluating the "safe stoppability" of humanoid robots, and used simulation to define the conditions under which a robot can be safely stopped. The result is PRISM (Proactive Refinement of Importance-sampled Stoppability Monitor).

    Why it matters: The article introduces PRISM, a method that uses simulation to define the conditions under which a humanoid robot can be safely stopped, and it is a useful reference for readers interested in humanoid robot safety monitoring and certification.

  1. Harbin Institute of Technology Robotics73

    HIT's Li Tianlong team publishes research in Science Advances on magnetically controlled CD5+ dendritic cell microrobot immunotherapy

    Li Tianlong's team at Harbin Institute of Technology (HIT) developed an engineered microrobot system based on CD5+ dendritic cells that can achieve precise intravascular navigation through external magnetic field control and activate immune cells in situ; the results were published in Science Advances. Under a conical magnetic field, the system self-assembles into a ribbon-like swarm that migrates downstream; under a rotating magnetic field, it assembles into a chain-like swarm that moves upstream against blood flow; combined with tumor chemotaxis, it achieves targeted enrichment and deep infiltration. In an orthotopic mouse colorectal cancer model, a single injection combined with magnetic navigation reduced the tumor bioluminescence signal by more than 90%. HIT is the first affiliation of the paper.

    Why it matters: The HIT team presents the construction strategy and magnetic navigation results of the CD5+ dendritic cell microrobot, giving readers a look at the specific approach of this therapeutic path of in situ immune activation.

  1. 加速进化 · 微信公众号72

    Tsinghua's Zhao Mingguo team publishes Science Robotics paper proposing a unified perception-motion reinforcement learning framework for vision-driven humanoid soccer skills

    On August 19, Professor Zhao Mingguo's team in Tsinghua University's Department of Automation published the paper "Learning Vision-Driven Reactive Soccer Skills for Humanoid Robots" in Science Robotics, selected for the August 2026 humanoid robotics special issue; the robot platform used was a humanoid robot independently developed by Booster.

    Why it matters: The paper breaks down three failure points of the traditional perception-planning-control pipeline and presents a reproducible sim-to-real transfer method.

  1. PKU EPIC Lab66

    PKU EPIC Lab releases HumanTracker humanoid motion tracking benchmark and HumanScore metric

    PKU EPIC Lab proposed the HumanTracker benchmark for evaluating humanoid motion tracking, accepted to ECCV 2026. The benchmark contains approximately 153 hours of optical motion trajectories from multiple professional performers, organized into four motion families with text labels; the accompanying HumanScore metric is trained on 12K motion pairs containing 24K motions, aligns with human preferences, and can reveal contact and stability failures that kinematic metrics often miss, such as foot skating and mistimed landings.

    Why it matters: The original work proposes a benchmark and metric that align humanoid motion tracking evaluation with human perception, and points out that existing kinematic errors miss contact issues such as foot skating.

  1. LimX Dynamics61

    LimX Dynamics releases humanoid VLA model V³-0 alongside COSA 0.5

    LimX Dynamics released V³-0 alongside COSA 0.5, a humanoid VLA technology stack in its VLA-tools layer that supports mobile whole-body manipulation, running on the 43-DOF LimX humanoid robot Oli.

    Why it matters: The original text explains the architectural composition of V³-0 and its human-in-the-loop real-robot reinforcement learning loop, which lets readers understand where the mobile whole-body manipulation capability comes from.

  1. RoboParty · 微信公众号75

    RoboParty Lab Established, Open-Sources Three Humanoid Robot Toolchains for Training, Control, and Data

    RoboParty announced the establishment of RoboParty Lab and open-sourced three humanoid robot toolchains: the supervised learning training framework MimicLite, the unsupervised reinforcement learning control framework UFO, and the motion retargeting tool hhtools.

    Why it matters: The three toolchains provide specific figures on training compute, time required, and the policies already integrated, which readers can use to assess their reuse value in their own R&D workflows.

  1. Figure AI63

    Figure 03 arrives at BMW Spartanburg plant for sequencing sorting tasks

    Figure AI announced that the Figure 03 humanoid robot has entered Hall 52 at BMW Group Plant Spartanburg, shifting from the sheet metal loading and unloading work previously done by Figure 02 to a sequencing sorting use case; Figure 02 took part in assembling 30,000 vehicles last year.

    Why it matters: The company has released details of Figure 03's deployment scenario at the BMW plant and of its Helix 02 drive, so readers can learn about progress in bringing humanoid robots from material handling to complex sorting.

  1. Harbin Institute of Technology Robotics62

    HIT's Zhu Yanhe Team Publishes Paper on Cooperative Reconfiguration for Self-Repair in Modular Robots

    Professor Zhu Yanhe's team at Harbin Institute of Technology (HIT) published a paper in the International Journal of Extreme Manufacturing (IJEM), proposing an autonomous fault-tolerant repair strategy for modular robots based on cooperative reconfiguration, which restores joint fault damage by changing morphology without backup module replacement. The method combines topological description and kinematic self-modeling, particle swarm optimization to search for target configurations, and an improved RRT cooperative reconfiguration algorithm. In experiments, the fault-tolerance upper limit was 7 times higher than that of fixed-configuration redundancy methods. HIT is the sole completing institution, and doctoral student Qi Jian is the first author.

    Why it matters: The paper presents a cooperative reconfiguration fault-tolerance strategy that requires no backup modules and data showing a 7-fold increase in the fault-tolerance upper limit, allowing readers to understand a concrete implementation path for self-repair in robot swarms under extreme environments.