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#Research
Oct 30Fri
  1. 雷科技84

    AgiBot and Tsinghua Release CEL Collective Experience Learning Paradigm, Achieving Autonomous Swarm Evolution with 96 Real Robots

    AgiBot, together with Tsinghua University and Infinigence AI, released the collective experience learning paradigm CEL, which lets robots use the success and failure trajectories of their peers as learning material, with humans only responsible for judging outcomes. Across five real-world tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—policies trained with CEL all exceeded the average level of 6 human teleoperators, leading by up to 16.7 percentage points; in the phone packaging task, the number of robots participating in training expanded from 16 to 96, and the success rate rose from 86.7% to 98.3%.

    Why it matters: AgiBot and Tsinghua released the CEL collective experience learning paradigm, validating with 96 real robots that increasing the number of robots can directly improve task success rates.

  2. HuggingFace Daily Papers72

    OpenFlyV2 releases onboard vision-language-action model for aerial perception and navigation

    OpenFlyV2 is a unified onboard vision-language-action architecture that uses a shared vision-language representation to support semantic understanding, language-grounded perception, spatial reasoning, and continuous flight control within a single model. The model generates action chunks of 12 waypoints each entirely onboard at 10 Hz on an NVIDIA Jetson Orin NX, and transfers zero-shot from simulation to a real quadrotor, achieving a 48% navigation success rate in four real environments without using real aerial training data.

  3. Leiphone Robotics78

    Columbia's Yunzhu Li team presents FlexiTac open-source tactile sensor progress at IROS 2026

    At IROS 2026, Columbia University's Yunzhu Li team presented progress on FlexiTac, an open-source flexible piezoresistive tactile sensor platform, a year and a half after its open-source release. The sensor can be assembled in 3 minutes, bent and cut, and wrapped around gloves, fingertips, or robot skin. The team has integrated it into LeRobot and modified the UMI handheld device to add tactile recording, enabling synchronized visual-tactile data collection and policy pretraining. ADI is advancing a commercial version with tactile resolution five times that of a human finger, manufactured at scale using semiconductor fabrication processes.

    Why it matters: From sensor design and simulation calibration to UMI data collection, it presents a complete technical path toward scaling tactile perception.

  4. 量子位72

    Tsinghua University, Infinigence AI, and AgiBot propose collective experience learning CEL, validated on a 96-robot cluster

    Tsinghua University, Infinigence AI, and AgiBot jointly proposed the collective experience learning (CEL) paradigm, which lets robots share the successful experience of their peers rather than relying only on human demonstrations, and validated it on a cluster of 96 real robots. Two robots that did not participate in training were tested 600 times in total across five tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—with 15 failures, for an overall success rate of 97.5%, all exceeding the average level of 6 human operators; the success rate on the phone packaging task rose from 79.2% to 98.3%.

  5. HuggingFace Daily Papers75

    TinyAstral: Transferring a self-evolving harness to small generalist manipulation agents

    TinyAstral proposes a two-stage method: first, freeze an open-weight teacher model and use recursive self-improvement (RSI) to have frontier LLM agents read the teacher's trajectories and propose harness modifications, with each modification accepted only if it passes a paired statistical test; then freeze the final harness and distill a small student model.

  6. HuggingFace Daily Papers65

    CausalWAM: Learning the causal sources of actions in world action models

    The study proposes CWAM (Causal WAM), which adds a sparse routing path to the action head of a pretrained world action model, separating action-relevant latent variables from the other variables that world modeling depends on, while keeping the world modeling objective unchanged. The authors prove that under a sparsity constraint, the latent variables used to generate actions are merely an invertible function of the action-relevant latent variables, that the true structure is identifiable up to a permutation of the latent variables, and that this holds without relying on sufficient temporal dependence or assumptions about the evolution of unobserved states. Synthetic data and real-robot experiments validate the theory and show improved closed-loop success rate efficiency.

  7. HuggingFace Daily Papers80

    RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory

    RoboArchitect lets a coding model observe robot rollouts, diagnose failures, and iteratively revise the agent harness around a frozen policy. Without updating any model weights, it raises average success from 9.7% to 43.3% (4.5x) on unseen scenes and from 12.7% to 40.2% (3.2x) on unseen tasks.

    Why it matters: It automatically evolves control code and persistent memory outside a frozen policy, offering a transferable approach to improving manipulation success without finetuning weights.

  8. HuggingFace Daily Papers58

    Study: Probe evaluation of spatial state representations in visual foundation models

    The study uses frozen-feature probes to examine ten visual foundation model representations across four levels—spatial structure, object persistence, predictive spatial update, and actionability—under streaming first-person observations of static indoor environments. Results show that strong readout of the visible scene does not guarantee accurate object persistence or future-feature retrieval, and a spatial reader trained on real features does not transfer unchanged to predicted features. Object persistence and actionability depend on camera pose readout, while feature prediction depends on visible-scene readout.

  9. CMU Robotics Institute62

    CMU proposes Wiggle and Go: estimating physical properties of ropes by wiggling to plan manipulation

    A team at CMU's Robotics Institute has proposed the Wiggle and Go system, in which a robot first briefly wiggles a rope or cable while an overhead camera records how the waves propagate; a computer vision algorithm then tracks the motion and estimates the object's physical properties, which are used to plan subsequent actions without repeated trial and error. The model is trained entirely in simulation, and properties are then estimated from the motion of real ropes being wiggled to improve prediction accuracy on real hardware. The research has been accepted to the 2026 Conference on Robot Learning (CoRL) and will be presented next month in Austin; it is partly funded by the U.S. National Science Foundation.

    Why it matters: The method estimates a rope's physical properties from a single wiggle and then plans actions accordingly, offering a transferable approach to deformable object manipulation.

Oct 29Thu
  1. 机器人全球资讯 · 微信公众号56

    ETH Zurich biohybrid microrobot sets walking speed record at 0.4 body lengths per second

    ETH Zurich published a blog post on October 22 showcasing a biohybrid microrobot about 1.5 centimeters long, made from mouse skeletal muscle cells and a hydrogel scaffold, that can jump, run, swim, and grasp. The team used an algorithm to simultaneously optimize muscle tissue distribution, volume, and the hydrogel scaffold's shape and mechanical properties, achieving motion amplitudes up to 11 times greater than previous systems with similar muscle mass; the walking version reaches a speed of 6.2 millimeters per second, about 0.4 body lengths per second, which the study says is the highest speed reported to date for a walking biohybrid robot.

  4. 机器之心 · 微信公众号67

    Tianjin University team proposes FAME: an active 3D mapping method using Flow Matching to generate multiple exploration goals, selected for NeurIPS 2026 Spotlight

    A team from Tianjin University and Hefei University of Technology proposes FAME, which uses Conditional Flow Matching to simultaneously generate multiple exploration anchors, then determines routes through A* path planning, path clustering, and hierarchical selection. The paper was accepted to NeurIPS 2026 and selected for Spotlight.

  5. CVer · 微信公众号83

    Harvard, Nvidia and others propose RAX: robot agent online exploration, leading by 37.4 points on average across five benchmarks

    A research team from Harvard, Nvidia, Berkeley, MIT and Stanford proposed RAX (Robot Agent eXploration), which connects a general coding agent to robot perception and control interfaces, uses online exploration to write successful decisions into executable programs and accumulate them as skills, while keeping model parameters frozen.

    Why it matters: RAX uses a frozen model plus online exploration to generate reusable programs, offering a robot learning path that does not depend on action demonstrations.

  8. Guyuehome58

    JEPLO: Terrain-Adaptive Gait Control for Quadruped Robots Based on JEPA and LiDAR Perception

    JEPLO proposes a terrain-adaptive gait control method for quadruped robots that aggregates single-LiDAR point clouds into depth observations and predicts the next-beat latent variable in latent space, without reconstructing images. The method is supplemented by teacher-student distillation and distribution regularization; pretrained weights are reproduced in simulation, and principal component analysis of the latent variables clusters clearly by terrain.

  9. Guyuehome60

    OpenDoge uses observation-history implicit state estimation to control quadruped robot locomotion

    OpenDoge proposes a hybrid internal model for quadruped robot locomotion control that, without terrain maps or contact sensors, encodes observation history into a velocity estimate and a 16-dimensional latent state to determine gait phase and external disturbances. The article combines real inference data to examine what information the latent state carries and what it lacks.

  10. Guyuehome58

    FTNCBF: Fault-Tolerant Neural Control Barrier Functions Under Sensor Failures and Attacks

    FTNCBF addresses the failure of barrier-function safety guarantees when sensors are attacked. It runs estimators in parallel for each fault hypothesis, converts estimation uncertainty into first- and second-order correction terms in the barrier condition, and then synthesizes fault-tolerant control via a minimum-norm program. The method was reproduced in spacecraft and UAV scenarios.

  11. Guyuehome58

    AME: Terrain-attention encoding controls G1 humanoid robot walking on complex terrain

    The article introduces a terrain-attention encoding method called AME that encodes a 1.6×1.0-meter elevation map for the G1 humanoid robot into 187 terrain tokens, uses the robot's proprioceptive state as a single query for cross-attention to extract terrain features, and the policy uses these to walk on steps, plum blossom piles, and slopes. The article derives the encoder structure, attention mechanism, and reward design; the full body of the text is not provided in the materials.

  12. Guyuehome58

    Lidar-E2E: End-to-End Obstacle Avoidance Control with Onboard LiDAR for Quadrotors

    Lidar-E2E proposes an end-to-end obstacle avoidance control method for quadrotors using onboard LiDAR. It pools point clouds from a 1944-line LiDAR into a 36×6 range image, concatenates this with a 20-dimensional body state, and directly outputs attitude increments and throttle, which are executed by a low-level PX4 cascaded attitude controller. The article also derives a rotor identification model, the sensing pipeline, and reward shaping, and uses Isaac Sim.

  13. Guyuehome58

    FishSim: Tendon-Driven and Hydrodynamics-Based Propulsion Control for a Biomimetic Robotic Fish

    FishSim uses a single motor, two crossed elastic tendons, and a five-coefficient stateless fluid model to make biomimetic robotic fish propulsion an identifiable pipeline. The paper presents a comparison of simulated and real swimming speeds at eight frequencies; trajectory error fell from 592 mm to 91 mm after identification, and it discusses coefficient sensitivity and the limits of the turning experiments.

  14. Guyuehome0

    From Jev's structured judgment to UniIntervene: How robot reinforcement learning decides to "continue or take over"

    A team at Nanyang Technological University proposed UniIntervene, a human-in-the-loop reinforcement learning method for real robots, aiming to reduce reliance on manual takeover during robot training. The paper validated the method on grasping, insertion, wiping, and folding tasks with a UR7e robotic arm.

  15. HuggingFace Daily Papers79

    CaPtain: A Post-Training Pipeline for Code-as-Policy Robot Agents

    CaPtain proposes an open post-training pipeline that trains small open vision-language models such as Qwen3.5-4B into code-as-policy (CaP) robot agents that do not rely on VLAs, frontier models, or privileged state at test time.

    Why it matters: The work proposes using a physical critic to assign credit across multi-turn code policies and presents a complete training procedure for small models on LIBERO and real robots.

  16. HuggingFace Daily Papers62

    HiFOR: A Hierarchical Foresight Framework with Action-Guided World Retrieval for Vision-Language-Action Models

    HiFOR proposes a hierarchical foresight framework with action-guided world retrieval for vision-language-action (VLA) models. Its hierarchical foresight jointly predicts short-term state evolution, semantic milestones, and the final goal state through a Hierarchical Dependency Mask (HDM), while structure-aware latent world modeling organizes future representations along the temporal, view, and spatial dimensions, and action-guided factorized world retrieval retrieves relevant future information by action representation during denoising. The authors report that the method's effectiveness was validated on two simulation benchmarks and six real-world robotic manipulation tasks.

  18. HuggingFace Daily Papers71

    WorldBridge: Bridging Video and Action Experts for Task Generalization in World Action Models

    WorldBridge proposes a World Action Model (WAM) that connects separate video and action experts through structured attention and trains them with joint flow matching. On RoboTwin 2.0, using only in-distribution video-action supervision yields a 25.07% success rate on out-of-distribution tasks; adding task videos without action labels raises it to 55.90%, and adding one action-labeled demonstration per task further raises it to 70.70%.

  19. Reddit r/robotics62

    LEAN: Reinforcement Learning-Based Energy-Aware Navigation Planner Released

    The authors have released LEAN, a reinforcement learning-based local energy-aware navigation planner. The code is open-sourced on GitHub, and the paper was published in IEEE Robotics and Automation Letters. The authors state that the method does not require using proximity to the goal as a reward; instead, it lets the agent learn from failed trajectories and provides an explicit energy signal. An accompanying video compares the model's inference performance in real environments against an ablation version without energy awareness and a classical NMPC planner.

  20. HuggingFace Daily Papers55

    QUAN: Quaternion Approximate Networks Improve Image Classification and Oriented Object Detection

    The paper proposes Quaternion Approximate Networks (QUAN), which approximate quaternion convolution through Hamilton product decomposition using real-valued operations, paired with custom CUDA kernels to achieve rotation-equivariant image classification and object detection. The authors also propose Independent Quaternion Batch Normalization (IQBN) to stabilize training and extend quaternion operations to spatial attention mechanisms.

  22. Tech Xplore Robotics58

    University of Toronto develops flexible foot pad for one-legged hopping robot Hopcopter, improving performance on sand and ice

    A University of Toronto engineering team has developed a flexible foot pad for the one-legged hopping robot Hopcopter, enabling it to hop on difficult terrain such as sand and ice. By enlarging the contact area, using a spherical joint that tilts with the terrain, a rubber band for return, and short spikes on the bottom for grip, the pad reduces takeoff error by up to 66%, improves efficiency by up to 64%, and cuts cross-terrain performance variation by 84%. The related paper was published in Advanced Intelligent Systems; the team's next step is to develop autonomous navigation, targeting scenarios such as search and rescue, pipeline inspection, and mining area inspection.

Oct 28Wed
  1. The Robot Report78

    WorldOracle releases action-conditioned world model benchmark; seven models show deviations between predictions and real execution

    Princeton University, UT Austin, the University of Toronto, NVIDIA, and AgiBot jointly launched WorldOracle, an action-conditioned world model benchmark that uses the same initial frame and action sequence to compare model predictions with real execution.

    Why it matters: The benchmark compares world model predictions frame by frame against real execution of the same actions and releases failure samples, making it easier to identify sources of model deviation over long sequences.

  2. 人形大讲堂 · 微信公众号62

    Three cross-embodiment papers examined: body, motion, and environment data still need manual translation

    The article examines three cross-embodiment research papers published between October 24 and 26, arguing that cross-embodiment is more like translation than a spontaneous model capability. MimicAll uses a structured body-condition table covering 16 series and 4,096 body variants.

  3. Guyuehome58

    Jumper: Dynamic Jumping Control for a Crab Robot Based on Reference Motion and Residual Reinforcement Learning

    Jumper uses a reference-guided residual reinforcement learning method to control dynamic jumping in a crab robot. The reference motion records only the nominal trajectory, and the policy learns only the corrections to the reference commands. In this method, the takeoff impulse determines the launch velocity, the ballistic trajectory determines the airborne phase, and the official policy is used to complete jumping and boundary tests.

  4. Tech Xplore Robotics60

    UCLA and UC Davis develop 185-milligram insect-inspired flying robot that can resume flight after collisions

    A UCLA and UC Davis team published in Science Robotics a fully polymer micro flying robot weighing 185 milligrams that uses electrostrictive polymer films to directly drive flexible conical wings, with no mechanical transmission. In tests it can generate lift about three times its own weight, take off autonomously at an average speed of 40 centimeters per second, and still resume flight after being hit by a fly swatter or pressed under a load about 11,000 times its own weight. The current prototype is powered through external wires; the team's next steps are to improve steering and landing control, increase payload, and advance onboard power, control electronics, and sensing toward autonomy.

  5. Robotics & Automation News55

    Leiden University study: Ryberg Healthcare's autonomous UV-C disinfection robot Omnia meets disinfection standards

    Researchers at Leiden University evaluated Ryberg Healthcare's autonomous UV-C disinfection robot Omnia in a laboratory designed to emulate a hospital environment; the results, published in the peer-reviewed journal Infection Prevention in Practice, show it met the acceptance criterion of at least 5 log reduction.

  7. 爱范儿58

    Vine-inspired soft robotic intubation system SRIS achieves 87% first-attempt success rate in cadaver tests

    A team led by Elliot Hawkes at Stanford University designed the soft robotic intubation system SRIS based on the vine robot principle. In cadaver tests, 12 participants, including 8 emergency responders, received 5 minutes of training; 87% achieved successful intubation on the first attempt, with an average time of 21 seconds. Under the same conditions, video laryngoscopy had a 63% success rate and an average time of 44 seconds.

  9. 量子位55

    Wika releases high-quality dataset for embodied intelligence-enabled construction machinery

    Wika Mobile Control released the "High-Quality Dataset for Embodied Intelligence-Enabled Construction Machinery" at the 10th Xuzhou Artificial Intelligence Conference 2026, and it is now available on data repositories including Hugging Face. The first phase of the dataset has 36 million frames, covers 12 typical operation scenarios, and synchronizes five dimensions of data—vision, LiDAR, pose, force feedback, and joystick input—using a unified timestamp. Wika says real-machine data collection for excavators costs about 5,000 yuan per hour, and training a usable model roughly requires data on the order of 100,000 hours.