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#Data and simulation
Oct 26Mon
  1. arXiv cs.RO42

    TTT in VLA Models: Exploring Training with Human Demonstrations in InternVLA-A1 and Pi0.5

    The study integrates Test-Time Training (TTT) into InternVLA-A1 and Pi0.5, training on paired demonstrations and robot trajectories to explore action generation conditioned on human demonstrations. At the current scale, adding TTT and training on paired data did not produce consistent target selection based on demonstrations, and shorter sequences, full-trajectory gradient connectivity, and contrastive regularization also did not clearly improve responsiveness to demonstrations. When demonstrations were reduced to single-frame cues, real-robot execution still preferred cups at fixed positions.

  2. arXiv cs.RO69

    DiscoDemo: Using Reinforcement Learning to Generate Diverse Demonstration Data to Improve Imitation Learning

    DiscoDemo uses a latent-conditioned reinforcement learning generator with privileged information, together with a parallel reverse curriculum and a success-gated diversity objective, to produce multiple successful closed-loop behaviors; after safety filtering, these are exported as a static dataset for downstream visuomotor imitation learning. On four manipulation tasks, the average downstream success rate in simulation rose from 40% for the strongest baseline to 62%, and on real robots from 39% to 78%. The authors believe that the diversity of successful solutions can improve imitation learning for pretrained flow-matching VLAs and complements corrective data collection.

  3. arXiv cs.RO47

    Input Bias Correction in Gaussian Process Regression

    To address input bias in GP regression caused by mobile sensor pose estimation errors, a bias-aware Gaussian process (Ba-GP) framework is proposed. It uses the recorded GP posterior-mean gradient to propagate input bias and noise into mean and covariance corrections, and provides a numerically efficient first-order correction algorithm based on the additive covariance structure.

  4. arXiv cs.RO60

    Distributed Swarm Morphogenesis with Neural Cellular Automata

    The study proposes a modular framework that combines neural cellular automata (NCA) with existing site selection and collision-aware control for swarm robot morphogenesis. The framework introduces a decentralized attachment-site discovery mechanism in which robots already in place identify new attachment sites by updating and sharing local NCA states, eliminating the need for a predefined site list during deployment. The authors also propose offline sparse-retention fine-tuning and a false-positive loss to bridge NCA growth and gradual physical assembly. Simulations scale to several hundred robots and improve morphology accuracy, and experiments with Crazyflie quadrotors validate the integrated execution pipeline.

  5. arXiv cs.RO63

    BM2PC: A Bayesian Multimodal MPC Framework for Dynamic Manipulation of Deformable Linear Objects

    This paper proposes BM2PC, a Bayesian multimodal MPC framework for dynamic manipulation of deformable linear objects (DLOs). It formulates multimodal planning as a set optimization problem that balances control quality and behavioral diversity, and maintains multiple candidate control modes through incremental updates.

  6. arXiv cs.RO70

    EgoForesight: Scaling Policy Learning with Noisy Egocentric Action Data

    EgoForesight is a vision-language-action framework that jointly leverages robot demonstration trajectories and noisy egocentric videos for policy learning, reducing the impact of unreliable action labels through reliability-conditioned corruption and reliability-weighted regression, and supplementing supervision from demonstrated outcomes via privileged-future representation distillation.

  7. arXiv cs.RO65

    WorldMirror: An agentic framework for generating interactive simulation worlds from in-the-wild robot demonstrations

    WorldMirror is an agentic real-to-sim framework that builds interactive digital twins from in-the-wild robot demonstrations. It frames real-to-sim construction as the iterative refinement of an executable world program: a coding agent decomposes the scene, plans reconstruction steps, and dispatches image and 3D generation tools, then revises the program based on verification feedback. Experiments on DROID show that the framework can reconstruct visually faithful, geometrically consistent interactive worlds while preserving the physical validity of the recorded robot trajectories.

  8. arXiv cs.RO58

    KiRoSe: Efficiently Segmenting Robot Demonstrations with Kinematic Data

    KiRoSe proposes segmenting demonstration data using robot kinematic data rather than multi-camera video streams, compactly representing recurring motion events through Sparse Motion Tokens (SMTs). The method supervises an open language model with subtask annotations to interpret token sequences, clearly outperforming baseline segmentation methods on the F1 metric, and can transfer zero-shot to unseen robot embodiments and first-person human motion. Beyond segmentation, KiRoSe can also track task progress and detect action reversals on unseen robots.

  12. HuggingFace Daily Papers63

    PhysEvolve: A Self-Evolving Framework for Physical Reasoning Driven by Executable Worlds and Verifiers

    PhysEvolve proposes a verifier-grounded self-evolving framework that lets vision-language models learn causal physical reasoning from executable world programs they propose themselves and from simulation-generated tasks. In each round, a shared VLM proposes world programs and evidence schemas; a deterministic physics engine freezes each valid world and simulates matched baseline and body-elided counterfactual branches to generate discrete visual tasks; then exact verifier feedback updates the policy and curriculum via GRPO.

  13. HuggingFace Daily Papers80

    Rollback: Using World-Model Backtracking of Counterfactual Experience to Improve Robot Policies

    Rollback proposes a policy improvement paradigm that turns an action-conditioned world model from a one-way simulator into a backtrackable counterfactual engine: it returns to key decision points, branches to try different actions, and compares the outcomes, thereby distilling counterfactual results into action-level supervision without learning a separate value model.

    Why it matters: The world model is used as a backtrackable counterfactual engine to provide fine-grained action-level supervision for policy improvement, and the improvement margins on three real-robot tasks are reported.

Oct 25Sun
  1. 未来机器人网 · 微信公众号67

    Figure AI All-Hands Meeting Reveals Figure 04 Design Lock and Helix 3.0 Internal Testing Progress

    At an all-hands meeting made public on October 22, Figure AI announced that its fourth-generation humanoid robot, Figure 04, has completed design lock and entered the hardware verification and testing phase, and that a larger version of its in-house end-to-end vision-language-action model Helix 3.0 is expected to complete internal testing within a few weeks.

  2. The Robot Report70

    Teradyne Robotics partners with Sereact to integrate Cortex physical AI platform into UR robot arms

    Teradyne Robotics and Sereact have formed a partnership under which Sereact's Cortex physical AI platform will drive Universal Robots robot arms for unstructured tasks such as returns handling and picking; the two parties have already delivered dual-arm systems made up of hundreds of UR robot arms to customer sites in Europe.

  3. AI前线 · 微信公众号26

    ByteDance Seed recruits embodied AI talent with starting pay of 55K, targeting data collection and hardware, moving toward a full-stack layout

    ByteDance's Seed team updated its robotics hiring this month, extending roles from algorithms and simulation to data collection and robot hardware. Embodied data positions offer monthly pay of 55,000 to 85,000 yuan, and the data collection lead role starts above 70,000 yuan. The hiring covers Beijing and Shanghai and requires candidates to have experience in robot data collection, teleoperation, or on-site project management. Industry rumors say Seed has been collecting Ego data in China for several months.

  4. 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.

  5. 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.

  6. 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.

  7. 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. CVer · 微信公众号42

    Sengge Data races toward SEC listing: using Sen-3D platform to build a physically consistent training ground for embodied intelligence

    The prospectus of Shenzhen Sengge Data Technology Co., Ltd. has been made public by the U.S. Securities and Exchange Commission (SEC), advancing its listing process. Its Sen-3D platform consists of Sen-Modeling high-precision 3D reconstruction and Sen-Creation physically consistent world model generation, and works with a structured 4D world state acquisition system to build a Real2Sim2Real closed-loop training pipeline. Official materials claim a Sim2Real zero-shot transfer success rate of over 80%; this approach still requires validation on more real-world tasks.

  9. 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. 机器人全球资讯 · 微信公众号42

    Pudong embodied intelligence supply chain: over 150 firms cover the full robotics chain; Zhangjiang South platform enables one-week trial production

    Pudong is now home to more than 150 embodied intelligence companies, and the brain, chips, perception, control, body, and application scenarios needed for a robot can all be sourced locally. The Zhangjiang South embodied intelligence supply chain platform offers one-stop services: standard parts matched in one day, prototyping in three days, and trial production in one week. Yiyou Technology's automated joint production line has landed in Zhangjiang, with annual joint capacity of up to 100,000 units. AgiBot Lingxi X2 is already providing regular service in Chengdu's Kuanzhai Alley.

  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. Xbotics具身智能实验室 · 微信公众号20

    Lingxin Qiaoshou × Xbotics Third Dexterous Hand Online Training Camp Opens Registration: Recreate Continuous In-Hand Ball Rotation with the O20 in 8 Weeks

    Lingxin Qiaoshou and Xbotics have opened registration for the third dexterous hand online training camp, which starts on November 1 and runs for 8 weeks with 6–8 hours of commitment per week. The hands-on project is to recreate continuous in-hand ball rotation with the O20 in MuJoCo simulation, with O30 ball rotation as an extended example.

  15. 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.

  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. Reddit r/robotics50

    How to Build a Robot with the NVIDIA Toolchain: An Illustrated Workflow from Isaac Sim to Real-Machine Deployment

    NVIDIA released an illustrated tutorial that uses a fictional startup building a warehouse humanoid robot as an example, connecting tools including Isaac Sim, Isaac Lab, GR00T, Cosmos, and Omniverse, and covering virtual environment setup, demonstration teaching, policy training and testing, handling the Sim2Real gap, and deploying AI to real hardware. The tutorial also explains the steps between an AI model deciding to move an arm and the motor actually executing it, and the author hopes to exchange with developers who have actually used these tools about how real workflows differ from the examples.

  2. Automated Warehouse71

    Sereact partners with Teradyne Robotics to bring its Cortex physical AI platform to logistics and retail

    Sereact this week reached a partnership with Teradyne Robotics (including Universal Robots and Mobile Industrial Robots) to have the Cortex physical AI platform drive UR robotic arms in handling unstructured tasks such as returns sorting and picking; the two sides say they have already delivered hundreds of UR dual-arm robotic systems at customer sites in Europe.

  4. 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.

  5. 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.

  6. 钛媒体57

    Will data collection become a "sacrificial lamb" for embodied intelligence?

    The article reviews the current state of the embodied intelligence data collection industry: in less than two years, 106 humanoid robot data collection centers have been built nationwide, and some companies inflate reported revenue through a "sell equipment—buy back data" model. Shao Tianlan, founder of Mech-Mind, has publicly pointed out this kind of practice of packaging fake revenue. Data prices have fallen from over 200 yuan per hour last year to over 100 yuan, and STAR Market and Beijing Stock Exchange listing reviews have begun to ask fixed questions about revenue authenticity and the reasonableness of related-party transactions. With the rise of bodyless data collection, data collection centers' demand for purchasing robot bodies may weaken, and industry consolidation is expected to become concentrated between the second half of 2026 and 2027.

  9. 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.