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Sep 24Thu
  1. Tech Xplore Robotics45

    DirectHop micro jumping robot can precisely control jump height

    DirectHop is a roughly 1-gram micro jumping robot developed at the University of Washington that uses a tiny motor to drive takeoff directly rather than a spring, achieving jump-height precision of 1 centimeter; it can jump repeatedly and clear a standard step height. Its self-righting success rate after landing is 90%. The current prototype requires external power and is controlled by a laptop, and the team is developing onboard power and a camera to enable autonomous navigation. The paper will be published at IROS 2026, and the researchers estimate a production cost of about 10 dollars per unit.

  2. Tech Xplore Robotics49

    ETH Zurich's new humanoid robotic hand can walk autonomously, press keys, and move objects

    ETH Zurich's Soft Robotics Lab taught an off-the-shelf humanoid robotic hand to walk autonomously, press arrow keys, and push blocks. With a battery, motion sensors, and a microcomputer added to the back of the hand, and after reinforcement learning training in simulation, the hand crawled on 14 indoor and outdoor surfaces, and recovered its standing posture on its own in 21 of 25 sideways-fall tests. It pressed the correct arrow key 29 out of 32 times. In 15 tests, the block was pushed to within 2 centimeters of the target. The paper Fingers as Legs has been published on arXiv.

Sep 23Wed
  1. Tech Xplore Robotics73

    University of Florida study: black-and-white stripe patterns can fool depth estimation in autonomous vehicles and robots

    A University of Florida study found that simple black-and-white stripes or checkerboard patterns can cause errors in the stereo camera depth estimation of autonomous vehicles and robots, leading them to misjudge obstacle distances and even trigger sudden braking or collisions. The research team tested a variety of sensors, algorithms, and AI models and found that this fundamental vulnerability, which is based on repeating textures, is widespread, and that an attacker need only place a small-area pattern in a scene to affect depth computation; such patterns may also appear in natural environments.

  2. 帕西尼 · 微信公众号46

    PaXini PX6AX tactile sensor supports Shenzhen University RoboLab team in solving cross-operator generalization challenge in touch sensing

    A team led by Assistant Professor Zhang Shuyang at Shenzhen University's College of Mechatronics and Control Engineering used the PaXini PX6AX multi-dimensional tactile sensor as its validation platform to address cross-operator generalization in tactile material recognition, proposing the lightweight tactile model Lite-TacFormer, which improves cross-operator generalization through a few-shot calibration mechanism and features an extremely small parameter count, minimal sample dependence, and a very low computing threshold. Built around the self-developed FUSE tactile sensing chip, the PX6AX maintains millinewton-level accuracy and microsecond-level response after tens of millions of force measurements, and its integrated sensing-computing-transmission design shortens experiment cycles.

  3. Skild AI62

    Skild AI releases physical self-play post-training results; S1 model learns soccer in simulation and transfers to humanoid robots

    Skild AI announced that it post-trained its foundation model S1 through physical self-play, letting the policy play against a recent version of itself with scoring as its sole objective. In NVIDIA Isaac Sim, over 140 years of simulated time, it learned skills such as dribbling past opponents, shielding the ball, and tackling, and these skills were successfully transferred to humanoid robots for matches. The authors believe self-play can break through the capability ceiling of pretrained human data, and the next step will be to extend it to everyday tasks such as collaborative manipulation and city-scale navigation.

    Why it matters: The authors describe using physical self-play to let a foundation model acquire soccer skills in simulation and transfer them to real robots, which lets readers learn about a feasible path for self-play-style post-training.

  4. MagicLab/魔法原子70

    MagicLab analyzes Magic-Brain's in-context visual guidance design; magicVLA grasp success rate rises from 70% to 94.4%

    MagicLab introduces the visual guidance approach in the Magic-Brain framework: a VLM Agent outputs a bbox, and a segmentation model refines it into a three-color mask overlaid on the observation image, converting semantic guidance into visual guidance.

    Why it matters: MagicLab details the design motivation and implementation of Magic-Brain's visual guidance layer, letting readers understand the impact on grasp success rate of converting language instructions into pixel guidance.

Sep 22Tue
  1. 自变量机器人 · 微信公众号45

    X-Era Robotics helps wrap up Tsinghua University's 3rd Embodied Intelligence Skills Challenge

    As a co-building enterprise for embodied intelligence model technology, X-Era Robotics provided open-source models, hardware equipment, and real-machine evaluation support for Tsinghua University's 3rd Embodied Intelligence Challenge. The event drew 46 contestants and 10 teams from Tsinghua, Peking University, and other universities, with the finals concluding at Tsinghua University on September 17.

  2. Sergey Levine63

    Sergey Levine's team proposes ARLI to guide large robot foundation models with small RL policies

    Sergey Levine shared research conducted with Siemens and Berkeley on how to pair a small RL policy with a large robot foundation model under real-time chunking (RTC). Because the RL policy infers faster, it can observe newer images, thereby steering the foundation model's policy toward better behavior. The introduction he cited notes that asynchronous VLA inference reduces inference latency but breaks the Markov assumption required for reinforcement learning fine-tuning, and ARLI is proposed to address this problem. The project page is https://async-rl-intermediate-information.github.io/ . The work was led by Brian Zhu, Momen Khalil, Emanuele Poggi (Siemens), and @ehharrison4 (Berkeley), among others.

Sep 18Fri
Sep 14Mon
  1. Weekly Robotics45

    Weekly Robotics #376: Humanoid robotics research, FailBench evaluation, and Agility Robotics financial results

    Weekly Robotics issue 376 covers two studies: a dual-depth-camera humanoid from Duke University and a monkey-bar-traversing humanoid from the ETH Robotic Systems Lab. FailBench shows that the best vision-language model for judging robot task success or failure reaches only 0.77 accuracy. Agility Robotics reported 2025 revenue of $1.8M and an operating loss of $140M, with robot-as-a-service pricing at $8,500 per month.

Sep 11Fri
  1. PKU EPIC Lab64

    PKU EPIC Lab proposes UniPart for zero-shot language-driven 3D part segmentation

    PKU EPIC Lab proposes UniPart, a feed-forward cross-modal 3D Transformer conditioned on CLIP text embeddings that uses free-text phrases to select functional parts on point clouds, achieving zero-shot language-driven 3D part segmentation.

    Why it matters: The paper presents a text-driven 3D part segmentation method and a self-built dataset, and validates transfer to real-world language-conditioned grasping.

  2. MIT CSAIL52

    MIT CSAIL releases X-Hinges design software that 3D-prints everyday objects into interactive devices with built-in sensing

    Researchers at MIT CSAIL and Tianjin University have introduced X-Hinges design software, which automatically embeds two different materials when 3D-printing interactive objects, allowing sensor signals and wire-like traces to avoid interfering with each other, and supports up to three axes of motion. The team used it to make a smart glove that transmits grasp-motion data to a humanoid robotic hand, an origami-style desk lamp that senses pinching to adjust brightness, and a detection device that can identify the objects placed on it, and used a computer vision system to align sensor-reading differences among devices from the same batch. The work will be presented at UIST.

  3. PKU EPIC Lab73

    FoldNet++: A Large-Scale Synthetic Dataset for Robotic T-Shirt Folding and Unfolding

    PKU EPIC Lab has released FoldNet++, a large-scale synthetic dataset for robotic T-shirt folding and unfolding, covering 6 robotic embodiments, 1K T-shirts, 1K environmental assets, and 120K richly annotated demonstrations.

    Why it matters: The source gives the dataset scale and the real-world deployment success rate, so readers can assess the feasibility of training garment manipulation policies purely on synthetic data.

Sep 9Wed
  1. MIT CSAIL61

    MIT CSAIL and others launch InstructMesh tool to repair AI-generated 3D models and manufacture them on demand

    MIT CSAIL, together with Google and Northeastern researchers, launched InstructMesh, which combines TRELLIS with GPT-4 to let users repair AI-generated 3D models using natural language before 3D printing them.

    Why it matters: The research presents a new tool and user experiment data, letting readers learn how ordinary users can repair AI-generated 3D models and complete printing.

Sep 7Mon
  1. RLCN强化学习研究 · 微信公众号44

    Roundtable Discussion | RLChina Paper Seminar Episode 168 Live Preview (2026.09.08)

    Episode 168 of the RLChina Paper Seminar will be livestreamed on Bilibili and Tencent Meeting on September 8, 2026, from 16:00 to 16:30, hosted by Hu Zican, a doctoral student at Nanjing University, with master's student Xie Xinyi presenting the paper "Look Before You Leap: Distilling Tree Search into Action Evaluation for Frozen VLA Models."

  2. TUM MIRMI46

    TUM MIRMI uses Embodied Laboratory Intelligence to collaboratively explore the internal mechanisms of cancer cells

    The Cell and BioRobotics Group (CBoRG lab) at the Technical University of Munich (TUM) Munich Institute of Robotics and Machine Intelligence (TUM MIRMI) proposes "Embodied Laboratory Intelligence," in which robotic assistants and virtual agents work together to explore the internal mechanisms of cancer cells.

Sep 5Sat
  1. PKU EPIC Lab68

    GIF: An agentic generation framework for interactive and functional object compositions for robot learning

    PKU EPIC Lab proposes GIF, an agentic generation framework for robot learning that reframes the problem as disentangled reconstruction followed by relative pose recovery. CoGen combines 2D and 3D generative models to produce instance-disentangled meshes and coarse initial poses; GPRM refines the relative poses under joint geometric and physical guidance; and a VLM verifier selects the candidate that best matches the structured specification.

    Why it matters: The paper splits interactive and functional object composition generation into disentangled reconstruction and relative pose recovery, and provides a benchmark of eight contact-geometry classes along with collision-rate figures, which can help readers assess this route to simulation data generation.

Sep 4Fri
  1. 中科慧灵 · 微信公众号65

    CASIA and others propose World4RL: diffusion world model combined with reinforcement learning to optimize robot manipulation policies, real-world success rate rises to 93.3%

    The Institute of Automation of the Chinese Academy of Sciences, Zhongguancun Academy, and the CASBOT team propose World4RL, a policy optimization framework for robot manipulation that combines a diffusion world model with reinforcement learning, enabling robot policies to be optimized end-to-end in imagined trajectories generated by the model without additional real-robot interaction.

    Why it matters: The paper turns the world model from a trajectory-prediction tool used at test time into a reinforcement learning training environment, giving readers a concrete path to understanding how the cost of real-robot interaction can be reduced.

  2. OFweek Robotics72

    Fourier Intelligence, Shanghai Jiao Tong University, and Fudan University Open-Source HandEdit Dexterous Hand Image Editing Dataset and Benchmark

    Fourier Intelligence, together with Shanghai Jiao Tong University, Fudan University, and others, has open-sourced the HandEdit dataset and benchmark, which studies editing first-person human hand images into specified robotic dexterous hand configurations. The data contains more than 200 million image editing samples and over 300,000 video clips, covering 26 URDF configurations, more than 600 scenes, over 1,100 object types, and more than 400 task categories; it uniformly tests 11 image editing methods, sets up two evaluation tracks, Hand-only and Hand-Arm, and combines general similarity, VLM judgment, and embodied task metrics.

Sep 2Wed
  1. PKU EPIC Lab79

    ZETA: A Controlled Study of Zero-Shot Cross-Embodiment VLA Transfer for Tabletop Manipulation

    PKU EPIC Lab published the ZETA study on arXiv (arXiv:2609.02546), proposing a unified definition of zero-shot cross-embodiment VLA transfer and a controlled benchmark covering 14 held-out target embodiments, distinguishing strict zero-shot transfer from pretrain-exposed transfer. Experiments show that local end-effector state-action representations, source embodiment diversity, and auxiliary co-training improve transfer by about 15, 18, and 7 percentage points, respectively, and that adding only 5% target-embodiment data during pretraining raises average target-embodiment progress by 13.4 percentage points.

    Why it matters: The paper provides a unified definition of zero-shot cross-embodiment transfer and a controlled benchmark, allowing readers to understand the actual benefits of different representation and data strategies.

  2. RLCN强化学习研究 · 微信公众号72

    Seven questions on DreamerV3: general reinforcement learning with fixed hyperparameters via a world model

    The RLChina "Seven Questions" column examines DreamerV3, a general reinforcement learning algorithm proposed by Google DeepMind and the University of Toronto. Based on a world model, the algorithm first trains an actor-critic on imagined trajectories, and with a single set of fixed hyperparameters it matches or exceeds domain-specific algorithms across 8 major categories and more than 150 tasks.

  3. IEEE RAS47

    Robots in Society, Business and Culture: August 2026

    The August robotics monthly roundup covers developments in society, business, research, and culture, including a hardware-buying trip to Shenzhen and a U.S. Department of Energy laboratory investigation into the security risks of Chinese LiDAR. A3 data shows that 8,940 robots were ordered in North America in the second quarter of 2026, worth $622 million, with unit volume up 4.3% and revenue up 21.3%. About 300 companies exhibited more than 2,000 products at the World Robot Conference in Beijing; Unitree's closing price was 460% above its IPO price before falling nearly 30% on August 28.

Sep 1Tue
  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.

Aug 28Fri
  1. RLCN强化学习研究 · 微信公众号28

    First RLChina Frontier Academic Forum Held in Changzhou: Four Talks Focus on Large-Model Memory, Multi-Agent Reinforcement Learning, Self-Evolving Agents, and AI Economic Simulation

    On the afternoon of August 26, the first RLChina Frontier Academic Forum was held at the China-Israel Changzhou Innovation Park, hosted by the Hua Luogeng Yuan Dian Academy and organized by the Institute of General Decision Intelligence, with Associate Researcher Zhang Haifeng of the Institute of Automation, Chinese Academy of Sciences, presiding.

Aug 24Mon
Aug 21Fri
  1. 智源研究院 · 微信公众号75

    BAAI releases FlagEval-Robo embodied intelligence evaluation system and dual leaderboards

    BAAI released the FlagEval-Robo embodied intelligence evaluation system on August 21, launching the FlagEval-Robo-Sim and FlagEval-Robo-Real leaderboards and publishing evaluation results for 12 open-source models. All models underwent equal post-training on the evaluation dataset.

    Why it matters: The evaluation uses a unified dual-track benchmark to quantify the performance gap between simulation and real robots, helping readers judge the limits of how far simulation leaderboard results can be generalized.

Aug 20Thu
  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.

Aug 19Wed
  2. OFweek Robotics73

    University of Auckland and Xi'an Jiaotong University release Video2Knowledge framework to extract robot task knowledge from monocular phone video

    The LISMS lab at the University of Auckland and Xi'an Jiaotong University jointly proposed the Video2Knowledge framework, which extracts 6D hand and object trajectories from an ordinary monocular RGB video and converts them into robot action sequences. The system uses WiLoR to recover 3D hand meshes, combines OnePoseViaGen for object tracking, and designs a four-stage physics-aware correction module; manipulated-object pose accuracy rises from 52.5% to 59.0% under frontal views.

Aug 17Mon
Aug 14Fri
  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.

  2. Harbin Institute of Technology Robotics65

    HIT's Wu Zhiguang team develops adaptive magnetically controlled nanorobot swarm platform, achieving navigation in the spinal canal of mice and non-human primates

    Professor Wu Zhiguang's team at Harbin Institute of Technology (HIT), together with collaborators, developed an adaptive magnetically controlled nanorobot platform that can form swarm motion patterns such as vortex, chain, and ribbon under external magnetic field drive, and achieved targeted navigation within the spinal canal in mice and non-human primates. The results were published in Science Advances.

    Why it matters: The research team released experimental results on navigation of magnetically controlled nanorobot swarms in non-human primates. Readers interested in the medical translation of micro- and nanorobots can learn about its technical approach.

Aug 9Sun
  1. LeadeRobot77

    Mila and Polytechnique Montréal team open-source ~$2,000 robot guide dog Milo, built on Unitree Go2 for autonomous indoor and outdoor navigation

    Mila-Quebec AI Institute and a Polytechnique Montréal team have released Milo, an open-source robot guide dog that they say can cut the roughly $50,000 cost of a guide dog to about $2,000. The hardware base is a Unitree Go2 Air/Pro plus an NVIDIA Jetson Orin Nano; a magnetic-encoder telescoping handle is used to inversely solve the user's 3D position, a bird's-eye-view map is built first and then a navigation policy is run, and the policy is trained in about 10 minutes in GPU-accelerated simulation and deployed zero-shot on the real robot.

    Why it matters: The original article fully breaks down Milo's perception, policy training, and real-world testing details, so readers can reuse its engineering trade-offs in their own navigation projects.

  2. LeadeRobot79

    Italy's IIT and INAIL unveil ergoCub humanoid robot; human-robot collaborative lifting can reduce load on the human lumbosacral joint

    The Italian Institute of Technology (IIT) and INAIL proposed a shared embodied intelligence architecture and built the humanoid robot ergoCub; the research was published in Nature Machine Intelligence.

    Why it matters: The original article lays out methods for building ergonomics into robot hardware optimization along with real-robot data, letting readers understand how design and control are organized around human-robot collaboration.

Aug 8Sat
Aug 4Tue
  1. OFweek Robotics71

    X Square Robot open-sources HOST framework; robots can learn new skills from a 29-second video

    X Square Robot has released and open-sourced the HOST framework, in which a robot can reproduce a new skill after watching an average 29-second human demonstration video, achieving a 62% success rate with only 1/50 the data and 1/500 the learning time of the Pi-0.5 fine-tuning approach. HOST does not imitate human actions frame by frame; instead, it uses visual outcomes as an intermediate language, aligning video with robot execution by task progress rather than time. It adopts a dual-expert MoT architecture, with learning occurring at the inference stage while weights stay frozen, and it retains old skills better than the fine-tuning approach; under lighting and scene changes, the success rate remains above 50%. The paper and code have been open-sourced.

Aug 3Mon
Jul 31Fri
  1. RoboParty · 微信公众号67

    RoboParty Lab, Zhejiang University, and Tsinghua AIR release and open-source world model control method INTACT

    RoboParty Lab, Zhejiang University, Tsinghua University Institute for AI Industry Research (AIR), and InSpatio jointly released and open-sourced the world model control method INTACT (INtent-To-ACTion).

    Why it matters: The original article provides latency and success-rate comparisons with traditional CEM search, which readers can use to judge the benefit boundaries of search-free world model control.

Jul 22Wed
  1. 正行创新 · X68

    Harness VLA open-sourced, achieves 82.4% on LIBERO-Pro

    @ChaoYuTHU has open-sourced Harness VLA, which adds a Harness Layer to organize VLA models without modifying their weights. On LIBERO-Pro, Harness VLA scores 82.4%, compared with Pi_RLinf at 50%, Berkeley RATS at 43.8%, and NVIDIA Cap-X at 18.2%. Striding AI posted this tweet and sees it as a possible sign of a shift from end-to-end VLA to Harness VLA.

    Why it matters: The tweet provides comparison numbers with other VLA approaches on the same benchmark, letting readers judge how much headroom a plug-in structure that does not change weights offers.