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Oct 25Sun
  1. Reddit r/robotics42

    ETH Zurich's ANYmal quadruped robot solves giant Rubik's Cube without using its hands

    ETH Zurich's ANYmal quadruped robot solved a 10-pound giant Rubik's Cube using only its feet, three times, taking about 8 to 9 minutes each time. The demonstration combines computer vision, a traditional Rubik's Cube solving algorithm, and reinforcement learning to teach the robot to balance, apply force, and correct errors. This "pedipulation" (manipulation with feet) could enable future robots to open doors, turn valves, or move obstacles without hands.

  2. 机器人产业应用 · 微信公众号72

    DEEP Robotics releases five embodied brain achievements, including DR-WM-1.0

    DEEP Robotics has released five research results built around its self-developed embodied brain, DEEPRobotics-Brain: the world model DR-WM-1.0, the embodied foundation model DR-VLA-2.0, the embodied agent DR-Agent, the policy optimization framework DR-APO, and the data closed-loop platform DR-Benchmark.

  3. 机器之心 · 微信公众号76

    Tsinghua team builds HandsOn dexterous manipulation benchmark with 66 tasks to evaluate robot bimanual capabilities

    Ding Ning's team at Tsinghua University, together with researchers from Beijing University of Posts and Telecommunications, Peking University, and Nature Will, built HandsOn, a simulation platform and evaluation benchmark for cross-embodiment dexterous manipulation, containing 66 manually verified tasks and 18,000 human simulation teleoperation demonstrations.

    Why it matters: Readers can learn why dexterous manipulation evaluation should distinguish execution from transfer, and which specific failure points current multi-task policies have on bimanual collaboration tasks.

  4. 维科网机器人 · 微信公众号72

    DEEP Robotics and seven universities release five embodied brain results

    DEEP Robotics has released five research results built around its self-developed embodied brain DEEPRobotics-Brain: the world model DR-WM-1.0, the embodied foundation model DR-VLA-2.0, the embodied agent DR-Agent, the policy optimization framework DR-APO, and the data closed-loop platform DR-Benchmark. Partners include seven universities, among them MIT, Tsinghua University, and Zhejiang University.

  5. 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.

  6. 机器之心 · 微信公众号88

    Harvard, Berkeley, NVIDIA and others propose RAX, using online exploration scaling to let robots learn skills with zero demonstrations

    A research team from Harvard, NVIDIA, Berkeley, MIT and Stanford proposes 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 reusable skills, while keeping model parameters frozen.

    Why it matters: RAX uses a frozen model plus online exploration to generate reusable programs, leading by an average of 37.4 percentage points on five simulation benchmarks with zero action demonstrations, offering a path for robot learning different from scaling data and models.

  8. HuggingFace Daily Papers80

    RynnVLA-Latent: Scaling robotic manipulation foundation models with 3D latent actions

    The research team proposes the RynnVLA-Latent framework, which uses RynnLAM to learn continuous, 3D-grounded latent actions from visual changes without requiring native robot control signals. The method processes 30.1K hours of human, robot, and simulation video, generating 745.52M latent action descriptors, then uses flow matching to predict latent action chunks and adapt them to specific embodiments.

    Why it matters: This work converts heterogeneous interaction videos into 3D latent action supervision, offering a reusable data pathway for VLA pretraining that bypasses the scarcity of action annotations.

  11. Chelsea Finn52

    Chelsea Finn shares paper on generative value classifiers: a new Q-function parameterization without action conditioning

    Chelsea Finn shared Alexander Swerdlow's paper, which proposes generative value classifiers, a way of parameterizing Q-functions that does not require action conditioning. The authors believe this type of method can be combined with large-scale pretrained policies and can learn and generalize better from richer output signals. The paper link is alexanderswerdlow.github.io/gvc/.

  13. Xbotics具身智能实验室 · 微信公众号67

    HKU and Yisheng Technology propose DexHoldem: evaluating dexterous manipulation agents with Texas Hold'em

    DexHoldem, a project involving the University of Hong Kong and Yisheng Technology, brings Texas Hold'em to real hardware, using a UR10e robotic arm and a Shadow dexterous hand to simultaneously evaluate dexterous manipulation, table-state perception, and closed-loop operation. It has been accepted to the NeurIPS 2026 Workshop.

  14. 3D视觉工坊 · 微信公众号58

    RoboWorld Challenge 2026 opens registration: three tracks — WorldNav, HA-VLN 2.0, and SafeDrive-VLA

    Registration is open for the RoboWorld Challenge 2026, which features three tracks: WorldNav, HA-VLN 2.0, and SafeDrive-VLA. They respectively test language-guided open-loop route planning, social navigation in dynamic crowds, and safe driving under conflicting instructions. The final submission deadline is November 30, 2026.

  15. 3D视觉工坊 · 微信公众号71

    HKUST proposes W2-VLA: predicting future wrist latent variables enables 80Hz fine manipulation

    Authors from the Hong Kong University of Science and Technology propose World-to-Wrist VLA (W2-VLA), which uses global task context to predict future wrist latent variables and then guide fine action generation. The method achieves 98.5% average success rate on LIBERO, 60.71% and 18.21% on the Easy/Hard settings of RoboTwin 2.0, respectively, and outperforms baselines under standard and OOD conditions on three real-world tasks: tabletop cleanup, occluded placement, and dual-arm plug insertion.

  16. Tech Xplore Robotics62

    Shanghai AI Lab and others propose wearable system EgoExplorer, collecting 100 hours of human activity data for robot training

    Researchers from Shanghai AI Lab, Zhejiang University, and Shanghai Jiao Tong University propose EgoExplorer, a wearable capture system that uses a head-mounted GoPro HERO13, 5 VIVE Ultimate Trackers, and MANUS Metagloves or Wuji gloves to record 100 hours of human activity data, aligning first-person video, full-body motion reconstruction, finger movements, and tactile observations.

Oct 24Sat
  1. 3D视觉工坊 · 微信公众号42

    After feedforward reconstruction becomes 1,200 times faster, is BA still necessary in dynamic 4D reconstruction?

    StreamSplat at ICLR 2026 achieves purely feedforward, online generation of dynamic 3DGS directly from uncalibrated video streams, at 1,200 times the speed of optimization-based methods. But MoRe at CVPR 2026 actively embeds a BA-like refinement module in its purely feedforward pipeline; removing it raises Sintel ATE from 0.147 to 0.155. BA-Track, an ICCV 2025 Oral, uses a 3D point tracker to decompose camera and object motion, adapting classic BA to dynamic scenes.

  2. CVer · 微信公众号62

    Kuaishou Kling AI and NTU Open-Source MetaView, a Single-Image Novel View Synthesis Framework, Reaching SOTA on 11 of 12 Metrics

    The Kuaishou Kling AI team, together with Nanyang Technological University and HKUST (Guangzhou), proposed MetaView, a diffusion-based single-image novel view synthesis framework, and has open-sourced the code, model weights, and an online demo. The method is built on the MM-DiT architecture, injects implicit geometric priors extracted by a feed-forward geometry-aware network through parallel attention streams, and uses metric depth to anchor scale-aware RoPE, mitigating geometric collapse and scale drift under large viewpoint changes.

  3. OFweek Robotics75

    DEEP Robotics and seven universities release five DEEPRobotics-Brain embodied brain results

    DEEP Robotics has released five research results built around its self-developed embodied brain DEEPRobotics-Brain: the world model DR-WM-1.0, the embodied foundation model DR-VLA-2.0, the embodied agent DR-Agent, the policy optimization framework DR-APO, and the data closed-loop platform DR-Benchmark. The company says the work was carried out jointly with seven universities, including MIT, Tsinghua University, and Zhejiang University.

  4. 机器之心 · 微信公众号58

    HKU and Tsinghua propose VAST: enabling offline reinforcement learning to adaptively select value update timescales

    The University of Hong Kong and Tsinghua University propose VAST (Horizon Adaptive Offline Policy Learning via Value Stitching), an offline value learning framework that adaptively selects update horizons by jointly choosing future subgoals and time spans to stitch local returns with subsequent values.

  5. 机器之心 · 微信公众号72

    Limpid Intelligence releases PrismWAM and RoboDoctor, topping RoboTwin 2.0 and RoboDojo leaderboards respectively

    Limpid Intelligence founder Li Yiming's team at Tsinghua University released the world action model PrismWAM and the execution diagnosis framework RoboDoctor. PrismWAM decouples semantic context from physical state and, without additional action-data pretraining, achieved an average success rate of 83.67% on RoboTwin 2.0 and 75.5% in the Clean-to-Random setting, ranking first on the official leaderboard.

  7. Tech Xplore Robotics58

    Peking University team proposes motion prediction method for flexible robotic arms, reducing long-range error by more than a quarter

    A team led by Xie Guangming and Zhang Feitian at Peking University proposed a hybrid model that uses an equilibrium anchoring model and a lightweight correction model to suppress the accumulation of step-by-step prediction errors in flexible robotic arms. The work was published at RSS 2026. When predicting 200 steps (6 seconds) on a simulated 3D cable-driven robotic arm, the hybrid model's error was more than a quarter lower than that of the anchoring model alone, and none of 19 tests showed loss of accuracy. On a physical hydraulic soft robotic fish tail, its error was half or less that of the anchoring model and a quarter or less that of the correction model. More stable predictions could let a controller first try out multiple actions in a computer and pick the safest one.

  8. Reddit r/robotics58

    Team turns 20 real New York/New Jersey scans into physics simulation environments and releases dataset on HF

    The team reconstructed 20 real outdoor scans of New York/New Jersey from the NYU Wanderland research dataset into interactive physics simulation environments, including separate objects and semantic labels, articulated mechanisms such as hinges and sliders, collision geometry and estimated physical properties, and PBR materials, and exported them in Isaac Sim, MuJoCo, Gazebo, and PyBullet formats. The scenes, source code, and annotations have been released on Hugging Face; the authors describe this as an early version that is not yet a validated robotics benchmark, and note that some geometry and physical properties are inferred from existing observations.

Oct 23Fri
  1. 云深处科技 · 微信公众号72

    DEEP Robotics unveils five embodied brain results: world model DR-WM-1.0, VLA model DR-VLA-2.0, and more

    DEEP Robotics has released five research results centered on its self-developed embodied brain, DEEPRobotics-Brain, including the world model DR-WM-1.0, the embodied foundation model DR-VLA-2.0, the embodied agent DR-Agent, the policy optimization framework DR-APO, and the data closed-loop platform DR-Benchmark. The company is also collaborating with seven universities, including MIT, Tsinghua, and Zhejiang University.

    Why it matters: The five results span world models, VLA, policy optimization, and data benchmarks, offering a way to compare the division of labor and metrics across the full embodied brain pipeline.

  2. 新智元 · 微信公众号58

    ZhiZhi Innovation Research Institute publishes IQuest-Q1 technical report: training details of a 320B open-source base model on one million tasks

    ZhiZhi Innovation Research Institute has published a complete technical report on IQuest-Q1, which has 320B total parameters and 15B activated parameters, disclosing results on 30 benchmarks and training details. The team built more than 1 million executable, containerized task instances from code repositories and existing agent trajectories, and set up a three-layer mechanism of environment preparation, runtime checks, and trajectory auditing to prevent reward hacking.

  3. 机器之心 · 微信公众号58

    Shanghai Jiao Tong University and other institutions propose Physical RSI framework, using L0–L5 levels to measure robot self-improvement

    Researchers from Shanghai Jiao Tong University, UC Berkeley, Stanford University and other institutions proposed the Physical RSI framework in the paper "Who Improves the Robot? Toward Physical Recursive Self-Improvement," incorporating task capability updates and improvement process updates into a unified analysis and presenting an L0–L5 grading scale.

  4. arXiv cs.RO60

    EvSLAM: A Benchmark Framework for Event-Camera SLAM in High-Speed Maneuvers

    The authors propose EvSLAM, a benchmark framework for event-camera state estimation that covers multiple data-collection platforms, extreme lighting, and challenging motion patterns, and provides an explicit definition of high-speed maneuvers along with new evaluation metrics. The paper first analyzes the limitations of existing event-camera VO/VIO methods and the shortcomings of public datasets, then uses the framework to evaluate current state-of-the-art methods, summarizing better architectures and problems that remain to be solved.

  5. arXiv cs.RO58

    Relative Localization for Cooperative UAV Formations Using Active Marker Reflections

    This paper proposes a method for onboard relative localization that uses reflections of active markers on specular near-planar surfaces, requiring only a camera, active markers, and a single-beam rangefinder, with no prior knowledge of robot size, marker configuration, or surface reflectance properties. The authors characterize, analytically and experimentally, how surface tilt biases the estimate and identify the slope range over which the level-surface assumption holds. Indoor multi-UAV and outdoor experiments validate the localization accuracy, with higher accuracy than existing vision-based active-marker methods beyond 30 meters.

  6. arXiv cs.RO62

    S²: Sensorimotor Alignment as an Offline Proxy for Embodied Policy Success

    The authors propose Sensorimotor Alignment (S²), a training-free offline metric that measures how well a model's sensory representations align with the geometric structure of physical motion. The study finds that S² correlates positively with downstream robot task success across multiple embodied model families, enabling low-cost model screening before closed-loop evaluation and identification of promising VLM backbones before fine-tuning into VLAs.

  7. arXiv cs.RO72

    WarpWeaver: A Decentralized Real-Time Planner for Multi-Robot Coordination

    The paper proposes WarpWeaver, a decentralized real-time planner that negotiates shared-space rents among robots through decentralized column generation, so that the dispersive trees searched by each robot converge to a near-optimal consistent solution. In Dynobench simulations, its total travel time is up to 22% lower than a centralized offline planner and up to 37% lower than decentralized real-time baselines, and on a graph-connectivity task it nearly doubles the success rate of a specialized planner. Onboard planning experiments with six robots verified practical deployability.

  8. arXiv cs.RO61

    RGraph-Eval: Evaluating Robot Video Generation with Task Scene Graphs

    The authors propose RGraph-Eval, a reference-demonstration-based framework for evaluating robot videos that uses task scene graphs to explicitly expose interaction errors in generated videos, such as errors involving objects, grasps, and placements. The framework uses a foundation-model pipeline to identify manipulation roles, locate object masks, infer and verify state transitions and action intervals, and reconstruct the 3D motion of manipulated objects. It outputs an interpretable score that covers visible objects, relation changes, and 3D path shape.

  9. arXiv cs.RO74

    LocoManiLab: A unified testbed for data, policies, controllers, and deployment in humanoid loco-manipulation

    LocoManiLab is a unified testbed for humanoid loco-manipulation covering data collection and generation, policy training, evaluation, and real-world deployment. Its task suite includes 234 atomic and compositional tasks across five household scene categories, supported by 51.5 hours of simulated teleoperation data.

  10. arXiv cs.RO60

    STAMP: Humanoid Motion Transitions with Human Motion Priors

    The paper proposes the STAMP framework, which uses a "robot-to-human-to-robot" process to connect independently captured humanoid motions into continuous sequences. Its core is a learned alignment model: after motions are retargeted to a human skeleton, the model ranks candidate positions, headings, and transition durations for the subsequent motion, and a human motion prior then generates the transition under the selected alignment. The study evaluates the tracking success rate of the generated transitions on the Unitree G1 and ROBOTIS K1, and demonstrates long-horizon motion transitions in real-robot demos.

  11. arXiv cs.RO62

    STR: A Spatiotemporal Preference Motion Retargeting Method for Humanoid Loco-Manipulation

    The authors propose a spatiotemporal retargeting method, STR, which uses a vision-language model to select relevant motion signals and body parts and identifies keyframes accordingly, allocating additional solver iterations to keyframes and higher interaction weights to the selected parts to reduce retargeting computational overhead.

  12. arXiv cs.RO67

    FRED: World action model learns future video representations via self-distillation

    The study proposes the FRED framework, which predicts future video representations from noise in a single step via self-distillation, using representations extracted from real future observations as targets, while historical observations provide temporal context for the video model. Across diverse real-world manipulation scenarios, FRED outperforms compared world action models, with notable gains on long-horizon and out-of-domain tasks under zero-shot environment generalization; using only human video demonstrations, it achieves a 50.0% success rate on tasks absent from robot training data, versus 19.4% for the best comparable model.

  13. HuggingFace Daily Papers56

    SkeLAM: A Skeleton-Guided Latent Action Method for Robot World Models

    The paper proposes SkeLAM, a skeleton-guided latent action model for controllable robot world modeling. It decomposes latent actions into a kinematic component supervised by the robot skeleton and a residual component that retains complementary visual dynamics, and uses cross-modal swap reconstruction to make RGB and skeleton kinematic representations interchangeable.

  14. HuggingFace Daily Papers58

    A Survey Unifying Video Foundation Models Through World Modeling: Reading, Writing, Sharing, and Interacting

    A survey examines video foundation models and video world models through the unifying lens of world modeling, organizing the literature into four complementary operations: reading, which infers world properties from observed video; writing, which synthesizes video from specified or learned conditions; sharing, which integrates understanding and generation through common modeling mechanisms; and interacting, which predicts and updates world states under actions and observations.

  15. 深蓝AI · 微信公众号67

    IROS 2026 NIDAR uses RGB for zero-shot LiDAR point cloud intensity generation

    The IROS 2026 paper NIDAR proposes generating pseudo near-infrared images from RGB images and using intrinsic decomposition to add point-level intensity to existing point clouds; after offline training, it can transfer zero-shot to target scenes. In the Warehouse and Hospital sequences in Isaac Sim, after replacing with NIDAR intensity, the ATE RMSE of ISC-LOAM decreased by about 43.0% and 41.1%, respectively, compared with the simulator's built-in intensity.

  16. 钛媒体62

    Capital rushes into AI 3D: VAST, Meshy, and Yingmou absorb over RMB 8 billion in half a year

    FoST Future Narrative published an analysis of the financing and commercialization landscape in the AI 3D sector: as of the end of September, three companies founded by people born in the 1990s—VAST, Meshy, and Yingmou—had raised a combined total of over RMB 8 billion within half a year. VAST raised over RMB 5 billion in half a year and reached a valuation of USD 2 billion; Meshy completed a USD 400 million financing round in July 2026 at a post-money valuation of USD 1.5 billion, and its ARR surpassed USD 100 million at the end of September.