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#Research
Oct 18Sun
  1. Guyuehome46

    asRoBallet: Dynamic Balance Control of Spherical Underactuated Robots via Friction-Aware Reinforcement Learning

    asRoBallet achieves dynamic balance control of spherical underactuated robots through friction-aware reinforcement learning. Starting from underactuated dynamics and the two-layer contact friction among wheel, ball, and ground, it derives the kinematic mapping and dynamic balance mechanism of the spherical chassis. The method uses proximal policy optimization (PPO) to train position-holding and velocity-tracking policies, and applies friction domain randomization to improve policy adaptability under different friction conditions.

  2. Guyuehome45

    G1 Double Spin Kick: Whole-Body Angular Momentum Control in a Humanoid Robot's Spinning Kick

    Running a real ONNX policy in physics simulation, whole-body angular momentum is computed per body for the humanoid robot G1's Double Spin Kick, breaking down spin and revolution, the scheduling of each body group, and landing impact. The analysis notes that after the double spin kick leaves the ground, total angular momentum is locked, and the ground-contact phase is the window for momentum injection and absorption.

  3. Guyuehome46

    Humanoid Robot Shooting and Goalkeeping Control Based on Motion-Prior Reinforcement Learning

    A G1 shooter based on motion-prior reinforcement learning scored on all 60 penalty kicks, and a goalkeeper using historical encoding saved 85% of shots. However, when the two models were placed in the official adversarial scenario, they scored zero goals across 20 matches; the article uses frame-by-frame data to identify a two-layer distribution-boundary mechanism.

Oct 17Sat
  1. 深蓝AI · 微信公众号50

    Zhejiang University's GuardPIBT enhances PIBT with residual reranking, completes conflict-free 3D planning for 1 million agents in simulation

    A Zhejiang University team proposed GuardPIBT, which, while retaining the PIBT execution kernel, uses a neural network to make residual reranking proposals over PIBT's native candidate actions; conflict checking, priority inheritance, and backtracking are still handled by the PIBT kernel, and counterfactual grouped gating is introduced to filter harmful interventions.

  2. 新智元 · 微信公众号71

    Scale AI and Elorian Release HSS Benchmark to Test Visual Intuition: Humans 93.1%, Strongest Model GPT-6-astra Only 53.6%

    Scale AI, in partnership with Elorian, released a new benchmark called Humanity's Sixth Sense (HSS) to test intuitive visual reasoning in multimodal models: 20 human participants scored 93.1%, while top-ranked GPT-6-astra reached only 53.6% even at maximum reasoning effort, and the median across 25 models was 30.9%.

  3. 未来机器人网 · 微信公众号72

    CAICT and Tsinghua University release Embodied Intelligence Development Report (2026): 22,000 units shipped, RMB 93.5 billion in financing

    The AI Research Institute of the China Academy of Information and Communications Technology (CAICT), together with Tsinghua University's Department of Electronic Engineering and the Embodied Intelligence Testing Laboratory, released the Embodied Intelligence Development Report (2026) on October 16. This is CAICT's third release of the report, which is divided into five parts: global trends, technology systems, application expansion, industrial ecosystem and business models, and future outlook.

  4. 机器人产业应用 · 微信公众号74

    How far has Generalist AI GEN-1.5's physical prompting paradigm progressed?

    Generalist AI released GEN-1.5 on August 19, 2026; it can attempt new tasks from a single 3-12 second task demonstration without gradient updates, achieving a one-shot average success rate of 59%±10% across 10 short manipulation tasks, and this can be raised to 83%±9% by adding about 5 minutes of data and performing 10 gradient updates.

Oct 16Fri
  2. Robotics & Automation News64

    Skild AI trains S1 model to play soccer via over 140 years of simulated self-play, then transfers it to humanoid robots

    Skild AI demonstrated a self-play training method in which its S1 robot foundation model learned through more than 140 years of simulated soccer matches in Nvidia Isaac Sim, without relying on human demonstrations. In simulation, the policy successively learned to recover from falls, dribble past opponents, shield the ball, and tackle, and was then transferred to physical humanoid robots for soccer-kicking demonstrations. The company says the method has been extended to everyday robotic tasks, and early experiments with four-agent passing plays have been conducted.

  4. HuggingFace Daily Papers67

    HexaShell: Exploring the Limits of VLM Coding Agents as Robot Agents

    Researchers propose HexaShell, a system in which a coding agent built on a vision-language model drives a dual-arm robot through a single command line; the framework only converts the agent's commands into robot motion. After each failure, the agent reads the log, identifies the cause, and writes or fixes small tool programs. The tools use only the robot's own observations, determining the relative positions of objects through triangulation and, when necessary, moving a wrist camera to obtain a second viewpoint.

  5. HuggingFace Daily Papers62

    OfficeTown: An Organizational Simulation Framework and Benchmark for Evolving Agent Capabilities

    Researchers propose OfficeTown, a professional organization simulation framework initialized from a natural-language description of an organization; members coordinate work, delegate tasks to assistant agents, and review deliverables, while artifacts, decisions, and unresolved matters continuously shape subsequent work.

  6. HuggingFace Daily Papers47

    MIST: Diagnosing Memory Failures in Video World Models

    MIST separates "executing the change" from "remembering the change" in video world model evaluation, locating the bottleneck in the model's ability to set up a scene but failure to carry out the instructed change. Based on this, the proposed M3-Agent (Make the Missing Move) recursively revises the details of the input instruction, enabling a frozen model to successfully execute the instruction, with the reveal score more than tripling relative to the baseline.

  7. HuggingFace Daily Papers47

    What Kinds of Symbols Are Suitable for Hybrid Discrete-Continuous Planning?

    The paper proposes defining the usefulness of symbols with the solving algorithm at the center, introduces the concept of "solver-sufficient symbolization," and develops a counterexample-guided symbol invention algorithm for finite-horizon piecewise-affine systems. The method can be extended to optimization objectives that use neural networks as energy functions, inducing solver-sufficient symbols from continuous optimization landscapes. Simulations and real-robot task experiments show that the resulting symbols reduce solving cost while maintaining solution quality, and improve interpretability and manipulability.

  8. HuggingFace Daily Papers55

    MaSS: Improving Cross-Domain Scalability of Self-Supervised Monocular Depth Estimation via Scene Structure Discrimination

    The MaSS framework proposes a structural representation pathway explicitly decoupled from contextual features, using a new scene-structure discrimination objective to learn layout-aware structural representations usable for depth decoding. Trained individually and jointly on four diverse datasets, MaSS significantly outperforms prior self-supervised methods and becomes the first method to consistently benefit from mixed-domain training; zero-shot evaluation on eight additional datasets further demonstrates its generalizability, especially in entirely out-of-distribution scenes. Source code: https://mias.group/MaSS.

  9. HuggingFace Daily Papers60

    RoboHarness: Simple robot framework outperforms VLA and world action models on BEHAVIOR tasks

    The research team proposes RoboHarness, which gives LLM agents a visual-geometric control panel so they can directly understand and invoke embodied tasks. With this framework, Qwen3.8-Flash-Next surpassed the current state-of-the-art VLA models on BEHAVIOR Challenge 2025 tasks, and the implementation is open source. The authors believe coding agents have the potential to achieve generalist embodied manipulation without relying on large-scale training of vision-language-action models.

  10. HuggingFace Daily Papers47

    TreeOPSD: A Tree-Aware Posterior Self-Distillation Framework for Long-Horizon Agentic Reinforcement Learning

    TreeOPSD proposes a critic-free, tree-aware posterior self-distillation framework for long-horizon agentic reinforcement learning. It treats each interaction turn as a tree node, branches alternative continuations from shared prefixes, and recursively backs up terminal rewards to obtain implicit turn-level credit.

  11. HuggingFace Daily Papers56

    StopWatch: A Goal Grounding Approach for Object-Goal Navigation

    The StopWatch paper diagnoses that the main reason success rates remain low after fine-tuning pretrained VLMs into navigation policies is a lack of goal grounding: the policy often fails to notice that the goal is already in view or struggles to estimate its distance. The authors propose an explicit grounding method that achieves state-of-the-art results on six benchmarks across three scene collections, and they correct annotation defects in these benchmarks. Analysis on the corrected benchmarks shows that, with ground-truth grounding, the policy nearly reaches the level of the expert that generated its demonstrations, indicating that the bottleneck is perception rather than policy learning.

Oct 15Thu
  1. GitHub Embodied AI Topic58

    Futa Waseda releases first public version v2026.10 of Awesome Physical AI Safety

    The first public release of Awesome Physical AI Safety, a bibliography of physical AI safety literature whose taxonomy and curation methodology were designed by Futa Waseda (NII, Japan) and whose daily curation is automated by an LLM. It covers research on safety, robustness, ethics, and trustworthy deployment for VLA, robots, autonomous systems, and embodied agents.

  2. Harbin Institute of Technology Robotics58

    HIT's Liu Yingxiang team publishes review on high-performance piezoelectric actuators for extreme manufacturing

    Professor Liu Yingxiang's team at the State Key Laboratory of Robotics and Systems at Harbin Institute of Technology (HIT) published a review on high-performance piezoelectric actuators in Extreme Manufacturing, proposing a five-dimension performance evaluation strategy covering scale, speed, force, degrees of freedom, and precision. The article explains the performance regulation mechanisms of each dimension, analyzes the performance bottlenecks of different working modes, and proposes a new approach of multi-mode fusion piezoelectric actuation to achieve high precision, large stroke, large output force, and high speed simultaneously.

  3. 中国机器人网 · 微信公众号53

    North Carolina State University develops motorless, batteryless micro soft robot Ring Leaper that can bounce hundreds of times continuously, driven by infrared light

    A team at North Carolina State University in the US has developed a motorless, batteryless micro soft hopping robot called Ring Leaper, which under continuous infrared light can autonomously complete more than hundreds of cycles of energy storage, hopping, and self-resetting.

  4. Dexmate72

    Dexmate and CMU Release MeMoREval Benchmark: Robot Storing Camera Frames Beats Text Descriptions

    Dexmate and CMU introduced the MeMoREval benchmark, which uses a two-stage protocol of experiencing first and asking questions later to evaluate what embodied agents should store as memory. It includes 53 tasks and 4 simulated indoor environments, and runs on the Vega 1 Pro dual-arm mobile manipulator. Results show that storing raw camera frames scored 70% and had lower write cost, surpassing text descriptions at 56 to 60%; adding scene graphs achieved the highest overall success rate among all tested models. The protocol also completed two memory retrieval demonstrations on a real Vega 1 Pro using a SLAM map and head camera.

    Why it matters: The benchmark was proposed jointly by the platform developer and CMU; its frame-versus-scene-graph comparison can help with memory design choices, and the real-robot transfer offers a feasibility reference.

Oct 14Wed
  1. Reddit r/robotics61

    Galbot PASSAGE system helps humanoid robots avoid full-body obstacles in cluttered spaces

    Galbot's PASSAGE system enables humanoid robots to adapt full-body motion to cluttered spaces by crouching, stepping, or turning sideways. On the Unitree G1, it completed 50 real-world tests across 45 obstacle layouts without falling or requiring safety intervention. However, in simulation, when zero contact was required, the success rate dropped from 98.67% to 70.3%.

  3. GitHub Embodied AI Topic57

    SyntheticBrains/nematode v0.7.0 released: connectome learning advantage attributed to gap-junction placement and validated in a physical body

    nematode v0.7.0 (Phase 8) is released. Experiments show that the nematode connectome's learning advantage comes mainly from gap-junction placement rather than connection strength, and that this advantage still holds in a 12-segment kinematic body. The worm learns to weathervane toward a thermal set point but does not learn klinokinesis.

  4. Xbotics具身智能实验室 · 微信公众号66

    1.7B-parameter 3D world action model UNITAS open-sourced, surpassing 8B-parameter models on LIBERO and other benchmarks

    CrossDim Intelligence, together with The Chinese University of Hong Kong, Shenzhen, Foshan University, Sun Yat-sen University, and South China University of Technology, proposed and open-sourced UNITAS, a robot task-native 3D world action model. The code and usage instructions are publicly available in the GitHub repository DexForce/UNITAS.

  5. OFweek Robotics65

    UCLA RoMeLa Lab unveils PhoneBot bipedal robot, driven by an old smartphone and costing about $400 in materials

    The UCLA RoMeLa lab has published a PhoneBot paper on arXiv: a bipedal robot 483 mm tall, weighing 1.8 kg, with 13 degrees of freedom and about $400 in material costs. A phone mounted on the torso serves as sensor, onboard computer, and interaction interface, handling motion control, image recognition, and voice interaction; speech-to-text and large-model inference still rely on an external laptop, and training uses simulation plus left-right leg mirroring. With a used phone, the total cost is under $500. The team describes it as a lab test device with no arms that does not perform complex tasks such as carrying loads or climbing stairs.

  6. 3D视觉工坊 · 微信公众号38

    Research progress on line-structured light plane calibration methods: a roadmap of precision leaps from traditional calibration to camera vision and deep learning

    This article systematically reviews line-structured light plane calibration methods. It notes that traditional methods (the wire-drawing method, toothed-target method, and cross-ratio invariance method) rely on physical measurements and geometric constraints, yielding a limited number of calibration points and limited precision. Methods combined with camera vision algorithms (the light-stripe center point method, vanishing point method, vector cross product method, and direct calibration method via centerline extraction) use the camera's intrinsic and extrinsic parameters to convert pixel coordinates into 3D coordinates, obtaining more high-precision calibration points and generally raising average measurement precision to the sub-millimeter level.

  7. Tech Xplore Robotics49

    Soft robotic fish combines high-speed swimming with 3D maneuverability

    An electrohydraulically actuated soft robotic fish developed by the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences achieved a measured swimming speed of 2.13 body lengths per second (about 26 cm/s) and a minimum cost of transport of 9.4 J/(kg·m), setting a speed record for soft robotic fish propelled by body and caudal fin (BCF) locomotion.

Oct 13Tue
  1. Tech Xplore Robotics43

    Beetle-inspired soft robot BeetleBot walks, grasps, and relays sensory feedback via finger commands

    KAIST, together with Caltech and Nanyang Technological University, developed BeetleBot, a beetle-inspired soft robot that users can remotely control to walk, turn, and grasp by bending and extending a finger. The robot is driven by 3D-printed liquid crystal elastomer artificial muscles, and an onboard camera and capacitive pressure sensors relay visual and tactile information, enabling bidirectional feedback. The research was published in Science Advances; the artificial muscles' speed, energy efficiency, and power supply still need improvement, and performance still needs to be tested in real work environments.

  2. Xbotics具身智能实验室 · 微信公众号74

    UniWAM open-sources unified world and action model, leading multiple benchmarks with 99.2% on LIBERO and 68.32% on RoboTwin C2R

    The UniWAM team has open-sourced the code and weights of UniWAM, a unified world and action model, achieving success rates of 99.2%, 92.6%, and 68.32% on LIBERO, LIBERO-Plus, and RoboTwin 2.0 C2R, respectively, the highest values among the methods listed in the corresponding tables of the paper.

  3. 36Kr Embodied Intelligence52

    North Carolina State University develops mechanically intelligent soft robot capable of switching between three swimming modes

    A team at North Carolina State University in the United States has developed a mechanically intelligent soft robot that carries no microcomputer and relies on no real-time algorithms. Using only two air tubes to control inflation and deflation, it can switch among three swimming modes—jellyfish, fish, and rotor—and perform actions such as ascending, descending, hovering, and turning. Its control logic is handled entirely by its materials and structure. The results were published in Science Advances.

  5. Reddit r/robotics42

    MEgoVista demo collection test: reconstructing hand motion when a cup blocks the fingers

    MEgoVista reconstructs hand motion when an object occludes the hand: when the demonstrator naturally grips a cup and some fingers are blocked by the cup, the system can still extend the reconstructed hand into the occluded region. The method optimizes hand motion over the entire video, combining reliable visual observations with temporal continuity and anatomical constraints, thereby using information beyond a single occluded frame. For small teams collecting robot demonstration data, this means demonstrators do not need to change their grip to expose their fingers.

  6. GitHub Robotics Topic44

    AIRclub-UdeSA jar_workshops Week 10: ROS 2 bag data of "maze victim search" recorded on a real robot

    AIRclub-UdeSA released a ROS 2 bag recorded on a real robot for jar_workshops Week 10: a 2-minute capture in an arena filled with boxes, containing lidar, camera, odometry, IMU, and TF data. wget download and extraction commands are provided, and the empty-arena map comes with the Week 10 mapa_laberinto package.