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Only First party#Motion control
  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. 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. 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. 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.

  1. Deep Robotics/云深处科技60

    DEEP Robotics Lynx S10 prototype completes Arctic Ocean ice-surface and ice-water tests aboard research vessel

    DEEP Robotics' small wheeled-legged robot, the Lynx S10 prototype, completed a 76-day, 11,852-nautical-mile Arctic Ocean research test aboard Sun Yat-sen University's Polar Research Vessel Zhongshan, marking the first time a quadruped robot has set foot on Arctic Ocean ice. The unit, weighing under 20 kg including its battery, was fitted with anti-slip bionic feet mimicking polar bear paws in collaboration with Sun Yat-sen University, Westlake University, and Hangzhou Dianzi University, and completed crawling and motion-control tests with crampons; the body uses an IP67 integrated sealed design, and after increasing the surface area of its limbs for paddling, it completed swimming tests in ice water.

    Why it matters: The original article provides the specific test conditions and hardware modifications for the Lynx S10's walking on Arctic Ocean ice and operation in ice water, helping readers understand how quadruped robots are adapted for polar scenarios.

  1. PKU EPIC Lab61

    PKU EPIC Lab proposes LIMMT, a data-centric study for humanoid motion tracking, accepted to ICML 2026

    PKU EPIC Lab released LIMMT (Less Is More for Motion Tracking), which it describes as the first data-centric study for physics-based simulated humanoid motion tracking. The method defines motion data quality along three dimensions: physics feasibility, diversity, and complexity. Training on less than 3% of AMASS achieves better tracking performance than training on the full dataset, and the team also cleaned web-sourced mocap data. The paper has been accepted to ICML 2026; see arXiv:2606.06953.

    Why it matters: The paper presents a three-dimensional data quality framework and results from training on a small amount of data, offering a methodological reference for researchers working on humanoid motion tracking.

  1. Figure AI62

    Figure AI shows two Helix-02 humanoid robots collaborating to tidy a bedroom

    Figure AI demonstrated two humanoid robots equipped with Helix-02 collaborating to tidy a bedroom within two minutes, completing tasks such as opening a door, hanging clothes, putting away headphones, closing a book, taking out trash, pushing a chair under a desk, and making a bed together. The two robots run the same learned vision-language-action (VLA) policy, with no shared planner, message passing, or central coordinator; each infers the other's intent from its actions via its own cameras. The company says that to its knowledge this is the first demonstration of multi-humanoid collaborative mobile manipulation completed by a single learned neural network directly from pixels to actions.

    Why it matters: The official demonstration shows two humanoid robots driven by the same VLA system collaborating to tidy a bedroom, giving readers insight into an approach to multi-robot collaboration without a shared planner.

  1. Figure AI82

    Figure AI Releases Helix 02: A Full-Body Autonomous Control Model for Humanoid Robots

    Figure AI has released Helix 02, extending control from the upper body to the entire humanoid robot and enabling continuous, integrated autonomous tasks combining locomotion and manipulation. It adds a new System 0 whole-body controller, trained on over 1,000 hours of human motion data and simulation-based reinforcement learning, which outputs joint commands at 1 kHz via a 10M-parameter network, replacing roughly 109,504 lines of hand-written C++ code; System 1 connects head-mounted vision, palm cameras, fingertip touch sensing, and proprioception, outputting whole-body joint targets at 200 Hz.

    Why it matters: The original text provides details on Helix 02's architectural layers, training data, and demonstration tasks, allowing readers to understand the specific implementation approach for full-body autonomous control.

  1. LimX Dynamics70

    LimX Dynamics launches multi-form embodied robot TRON 2, open for pre-order from 49,800 yuan

    LimX Dynamics has launched the multi-form embodied robot TRON 2, whose single body can quickly switch between three configurations: dual-arm, bipedal, and dual-wheel-legged. It is priced from 49,800 yuan and is open for pre-order starting today. The dual-arm configuration is a 7-DoF humanoid design with a 70 cm arm span and a 10 kg dual-arm payload, and the body has a maximum load capacity of 60 kg. In bipedal form it has a 4-hour battery life and a maximum payload of 30 kg, and it can perceive its surroundings and climb stairs.

    Why it matters: The original text provides the one-body, three-form configurations, the dual-arm specifications, and the pre-order price, which readers can use to assess its suitability for VLA and mobile manipulation development.

  1. LimX Dynamics64

    LimX Dynamics showcases LimX Oli's human-like walking and TRON 1's extreme terrain capabilities at WRC

    Robot Lecture Hall reported on-site from the LimX Dynamics booth at the 2025 World Robot Conference. The full-size humanoid robot LimX Oli, 165 cm tall with 31 active degrees of freedom, made its first WRC appearance and went on sale, demonstrating outdoor live dancing and switching among multiple gaits such as a light quick step, model walk, and goose step, powered by a whole-body motion control algorithm combining traditional control, reinforcement learning, and imitation learning, with prices starting at 158,000 yuan.

    Why it matters: The author attended WRC in person and breaks down the gait switching, disturbance-rejection recovery, and teleoperation details at the LimX Dynamics booth item by item, so readers can understand its full-chain technology roadmap.

  1. Figure AI66

    Figure AI uses reinforcement learning to give Figure 02 humanoid robot a natural, human-like walk

    Figure AI released an end-to-end reinforcement learning walking controller that lets the Figure 02 humanoid robot learn a human-like gait in simulation by imitating human reference trajectories through rewards, including heel strike, toe push-off, and arm swing. Training runs in parallel across thousands of robots in GPU-accelerated physics simulation, collecting years of simulated data in hours, and combines domain randomization with kHz-level torque feedback control to achieve zero-shot transfer to the real robot; 10 Figure 02 units run the same neural network without individual tuning.

    Why it matters: The original text provides details on the training method and sim-to-real transfer for Figure 02's reinforcement-learning-based human-like walking, offering insight into its technical approach.

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