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Oct 25Sun
  1. 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.

  2. 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. 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.

  2. 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.

  3. 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. 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.

  6. 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. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. HuggingFace Daily Papers62

    S^2: Sensorimotor Alignment as an Offline Proxy for Embodied Policy Success

    The study proposes a training-free offline metric, Sensorimotor Alignment (S^2), that measures how well a model's sensory representations align with the geometry of physical motion. The authors report that S^2 correlates positively with downstream robot task success across model families, and that it can be used to screen models before closed-loop evaluation and to identify promising VLM backbones before costly VLA fine-tuning.

  16. HuggingFace Daily Papers75

    DiscoDemo: Using reinforcement learning to generate diverse robot demonstration data to improve imitation learning

    DiscoDemo uses reinforcement learning as a data generator, training a privileged-information latent-conditioned generator that discovers multiple successful solutions through a parallel reverse curriculum and a success-gated diversity objective, then exports them after safety filtering as a static dataset for downstream visuomotor imitation learning. On four manipulation tasks, the method raises downstream success in simulation from 40% for the strongest baseline to 62%, and on a real robot from 39% to 78%. The authors believe that the diversity of successful solutions can improve imitation learning for a pretrained flow-matching VLA, and that it complements corrective data collection.

  18. Tech Xplore Robotics65

    ETH Zurich uses mouse muscle tissue to enable tiny biohybrid robots to jump, swim, walk and grasp

    A research team at ETH Zurich published a study in Nature Communications in which they used computer simulation to co-design mouse skeletal muscle tissue and a soft hydrogel scaffold as an integrated whole, producing a biological actuator about 1.5 centimeters long with a range of motion up to 11 times that of existing systems with the same amount of muscle.

  19. Kinetix AI · YouTube62

    KAI Halo case study EgoWild2Dex: training bimanual dexterous manipulation with first-person data

    The KAI Halo case study EgoWild2Dex shows how high-quality bimanual dexterous manipulation capabilities can be derived from highly challenging egocentric data. The project page is https://mmlab.hk/egowild2dex.

    Why it matters: The material presents a complete training path from first-person human data to robotic bimanual manipulation, along with success rates; its transferable aspect lies in its approach to data utilization.

  20. HuggingFace Daily Papers58

    FrontierCS 2: A Benchmark for Computer Science R&D Released

    Researchers introduce FrontierCS 2, a benchmark of 48 open-ended tasks. Their approach takes a paper and the code its authors released, deletes the paper's contribution, and uses the remaining code as the human reference baseline. Unlike earlier benchmarks that only give a problem description and grade one program after the run, FrontierCS 2 places agents inside a research environment where they inherit a runnable repository and can submit patches at any time. The evaluator returns only a development-suite score, while the evaluator, the held-out final suite, and the reference remain private.

  21. 简智机器人 · X73

    Genrobot open-sources Gen-HumanWholeBody first-person whole-body manipulation dataset

    Genrobot has released and open-sourced Gen-HumanWholeBody, a first-person whole-body manipulation dataset it says has the industry's highest precision, hosted on Hugging Face. The dataset contains 600 hours of whole-body manipulation data, approximately 3 cm whole-body MPJPE, 16,976 real-world scenes, and 1,281 human skills, and is open for research on whole-body motion learning, human-to-robot transfer, and embodied foundation models.

    Why it matters: The original text provides the dataset's scale, precision, and open-access entry point, which readers can use to assess its usability for whole-body motion learning research.

Oct 22Thu
  3. 北京理工大学 · 微信公众号74

    BIT team publishes meter-scale titanium alloy single-crystal additive manufacturing results in Nature

    A team led by Professor Liu Changmeng at Beijing Institute of Technology has pioneered a single-crystal additive manufacturing method based on unsteady pulsed growth in a micro-melt pool, achieving meter-scale titanium alloy single-crystal additive manufacturing for the first time. The results were published in Nature on October 21 in a paper titled "Additive manufacturing ultralong and architected single crystals," with corresponding authors Liu Changmeng, Cui Yinan of Tsinghua University, and Yinmin Morris Wang of the University of California, Los Angeles.

    Why it matters: The paper presents the mechanism and cross-material validation of seed-crystal-free single-crystal additive manufacturing, which readers can use to assess its impact on the barriers to manufacturing single-crystal components.

  5. Xbotics具身智能实验室 · 微信公众号62

    HKU team proposes YOCO for dexterous-hand teleoperation mocap calibration with few paired poses

    Researchers from the University of Hong Kong, Yisheng Technology, and Shenzhen Hetao College have proposed YOCO, a few-shot session calibration method for 3D keypoints, aimed at correcting bias in low-cost motion-capture gloves used in dexterous-hand teleoperation. The paper has been accepted to CoRL 2026.

  6. Xbotics具身智能实验室 · 微信公众号72

    TaskAnchor lets VLA use execution history to address task-state confusion in long-horizon manipulation tasks

    TaskAnchor connects execution history to VLA's existing language and vision interfaces through a lightweight adapter, aiming to alleviate task-state confusion in long-horizon manipulation. It uses a dual-path design: a normalized task-state coordinate that describes semantic execution stages, and a visual residual based on context-adaptive test-time training (CA-TTT) that preserves historical details.

  7. 机器人前瞻 · 微信公众号73

    TeleAI and others propose NEXUS, a cross-terrain perception and motion control foundation, enabling Unitree G1 to perform full-body actions on stairs and slopes

    The China Telecom Artificial Intelligence Research Institute (TeleAI), Gamma Intelligence, Shanghai Jiao Tong University, the University of Hong Kong, and Zhejiang University jointly proposed NEXUS, a cross-terrain perception and motion control foundation for humanoid robots that lets robots autonomously adjust posture and contact based on terrain while preserving motion intent.

  8. 3D视觉工坊 · 微信公众号76

    SenseTime and joint team evaluate GPT-6-Astra embodied capabilities, open-source SenseNova-RoboRSI framework and 27B model

    A joint team including SenseTime and The Hong Kong University of Science and Technology (Guangzhou) released the report "Has GPT-6-Astra Achieved Embodied Intelligence?", systematically evaluating GPT-6-Astra's embodied capabilities across 12 benchmarks, and simultaneously open-sourced the SenseNova-RoboRSI evaluation framework and a 27B embodied model.

    Why it matters: The report breaks model capabilities into testable conditions and provides harness ablation data, so readers can judge the real bottlenecks in embodied capability evaluation and cost.

  9. HuggingFace Daily Papers56

    SERA: A Self-Evolving Robotic Agent Framework for History-Dependent Manipulation

    The study proposes SERA (Self-Evolving Robotic Agent), which organizes a high-level VLM into a robotic agent through an editable harness, with a fixed VLA executor providing low-level control. The harness uses observations and execution feedback to maintain task context, and the self-evolution mechanism proposes and evaluates harness revisions through environment rollouts, with the selected configuration fixed before held-out testing. On RoboMME, SERA autonomously selects a configuration reaching 51.38% overall success rate, versus 44.38% without self-evolution.

  10. 机器之心 · 微信公众号55

    ReGDiff: Exploring Metamaterial Voxel Geometry with Guided Diffusion in a Controlled Latent Space

    A team led by Dawei Zhou at Virginia Tech and researchers at Meta propose ReGDiff in a NeurIPS 2026 paper to generate metamaterial voxel structures that satisfy geometric constraints. The method has two steps: first, an RAS mechanism organizes the autoencoder latent space into a form where good and bad structures are clearly separated and good structures are evenly distributed; then, short-range repulsion (SRR) pushes latent variables away from the near neighbors of known samples during sampling, and it takes effect only at inference time.

  11. HuggingFace Daily Papers47

    PAPAV: A Unified Capability Framework for Multimodal Embodied Agents from Computer Use to Robot Use

    The paper proposes PAPAV, a capability-centric unified framework that characterizes multimodal embodied agents (MMEAs) through five functions: Perceive, Anticipate, Plan, Act, and Verify, and uses it to compare multimodal agents (MMAs) with robotic systems (RSs). An analysis across 64 benchmarks shows that explicit evaluation centers on Act, with only 5 assessing Anticipate and 3 assessing Verify. Physical constraints make errors harder to reverse, and PAPAV provides a common basis for designing reliable physical agents and measuring progress across the five capabilities.