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#Robot models and learning
Oct 27Tue
  1. arXiv cs.RO71

    RoboWorM: A Benchmark for Evaluating Motion Realism and Acceleration Effects in Embodied Video World Models

    The authors propose RoboWorM, a deployment-aware benchmark for robotic manipulation video world models that examines structural and spatiotemporal fidelity using hardware priors such as robot kinematic trees and fixed camera configurations. The benchmark collects 1,605 episodes across single-arm, bimanual, and humanoid morphologies, covering Markovian and long-horizon non-Markovian tasks under single-view and multi-view setups, and proposes two deterministic metrics that do not rely on vision-language models: KIVA and RIGIS.

  2. arXiv cs.RO75

    Scan2Instruction and PRPT-KS: Demonstration-free adaptation for language-guided navigation from a single scan

    The study proposes Scan2Instruction and PRPT-KS, which use a single iPhone scan to build a training environment and adapt a language-guided navigation policy without human demonstrations. Across five real environments, RealNav achieved an 85% real-robot success rate, compared with 49% for a baseline post-trained on human demonstrations; in unscanned areas of the same buildings, it still reached 82%, versus 41% for the baseline.

  3. arXiv cs.RO60

    RoboReason: Improving robotics-relevant VLM reasoning with scalable data generation

    RoboReason proposes constructing spatial and temporal ordering tasks from real robot stereo camera and wrist camera images to evaluate the robotics-relevant reasoning capabilities of vision-language models; experiments show that several advanced VLMs perform poorly on the ordering tasks. The authors accordingly propose a scalable autonomous training data generation pipeline, and supervised fine-tuning and reinforcement learning on this data both significantly improve model performance on spatiotemporal ordering and downstream robotics and visual reasoning tasks. The work was accepted to the EMNLP 2026 main conference.

  4. arXiv cs.RO60

    POD-RoPE: Physical Observation Distribution Rotary Position Encoding for Embodied AI

    The authors propose POD-RoPE, which represents a token's observation footprint as a distribution over camera centers, viewing directions, and sampling times, uses expected rotations to encode relative relationships between distributions, and reduces to pointwise RoPE when each distribution collapses to a single point.

  5. arXiv cs.RO63

    RoboJev-27B: An Open-Source Vision-Language Model for Judging Robot Task Success

    RoboJev-27B is an open-source vision-language model that takes a task instruction and 10 frames of a manipulation attempt as input and predicts whether the robot completed the task. It achieves 95.2% balanced accuracy and 0.993 AUROC on 268 held-out episodes of the same robot model (SO-101), and an AUROC of 0.890 on 459 episodes from unseen labs or robots. Median inference time is 0.26 seconds per episode on a single H100, and the model, inference code, and evaluation results for 20,628 predictions are released together.

  6. arXiv cs.RO64

    Open-World Law-to-Action: Using vision-language priors and touch to infer the motion laws of parts with hidden physics

    The authors propose the Open-World Law-to-Action (L2A) framework, in which a vision-language model proposes, for each movable part, its motion type, the terms of its law of motion, and possible locking factors; the part's law is then identified online via Bayesian linear regression with closed-form evidence, and touch can overturn the priors.

  7. arXiv cs.RO62

    EXE: Egocentric Cross-Embodiment Pretraining with Factorized Latent Actions for VLA Models

    EXE is an egocentric cross-embodiment pretraining framework that learns factorized latent action representations from human manipulation videos, separating shared manipulation priors from embodiment-specific signals. The authors state that this shared action space lets robot policies acquire generalizable manipulation semantics from diverse human demonstrations, and that the same pretraining can be used for both parallel grippers and dexterous hands. On RoboTwin 2.0, RoboCasa GR1, and real-world manipulation tasks, EXE achieves consistent improvements over baselines without pretraining.

  8. arXiv cs.RO62

    HA-DiPO: A Diffusion Policy Optimization Framework for Heterogeneous Robot Cooperative Transport

    The authors propose HA-DiPO, a hierarchical multi-agent reinforcement learning framework operating under centralized training with decentralized execution (CTDE) for heterogeneous legged robots cooperatively transporting an unfixed shared payload. At the high level, a centralized critic jointly trains each robot's independent coordination policy, and diffusion policies represent multimodal coordination responses; at the low level, embodiment-specific locomotion policies track the coordination commands. The policies are learned from scratch through reward-driven simulation interaction, and at execution they use only local observations with no inter-robot communication.

  9. arXiv cs.RO62

    WorldFailBench: Can World Models Understand Policy Failures?

    The authors propose WorldFailBench, which uses three tasks—failure reason, failure stage, and failure boundary—to test whether world models faithfully simulate policy failures. The benchmark covers manipulation tasks in the LIBERO and RoboTwin simulation environments, as well as real-world manipulation tasks with the G1 humanoid robot. Experiments on seven world models show clear gaps in their understanding of policy failures.

  11. arXiv cs.RO71

    MimicAll: A Structured Embodiment Conditioning Framework for Cross-Humanoid Whole-Body Motion Tracking

    MimicAll is a reinforcement learning framework that encodes global and joint-alignment conditions using structured embodiment descriptors, enabling heterogeneous humanoid robots to share a whole-body motion tracking policy. Training covers 4,096 variants across 16 humanoid robot families, and a benchmark of 464 bodies across five distribution strata was built. With the same amount of training transitions, shared training achieved a 99.4% 30-second success rate on randomized G1 bodies in 100STYLE, versus 42.9% for single-body specialized policies.

  12. arXiv cs.RO62

    ReSight: Task-Driven Self-Improving Memory for Embodied Agents

    ReSight is a task-driven self-improving memory system for embodied agents that retains keyframes and their spatiotemporal associations, and separates observation evidence from extensible derived fields so that historical observations can be reprocessed for new needs. Through Explore-Distill-Mount (EDM), the system identifies capability gaps from execution and exploration records and converts exploration-derived solutions into reusable memory builders, query tools, and skills.

  13. arXiv cs.RO67

    Survey: Dexterous hands, simulators, training, and evaluation in 113 learning-based manipulation methods

    A survey reviews 113 learning-based dexterous manipulation methods, covering the dexterous hands used, the training simulators, and how results are reported. The authors note that nearly half of the works use the same recipe: reinforcement learning on privileged state in a GPU simulator, then distillation into a vision-only student model transferred to hardware, and nearly all of them concentrate on the same simulator.

  14. arXiv cs.RO58

    MDPO: A Multi-Constraint Policy Optimization Reinforcement Learning Method for Dexterous Manipulation

    The authors propose Multi-constraint Dexterous Policy Optimization (MDPO), which formulates dexterous manipulation as a multi-constraint policy optimization problem involving both cumulative cost constraints and state-wise cost constraints, and integrates multi-constraint projection into the policy optimization process. In experiments on ReDMan for safe joint control, finger safety, and grasping tasks, MDPO achieved better constraint satisfaction than baselines while maintaining competitive task performance. The code is publicly available.

  16. arXiv cs.RO66

    Active Reachability Learning for Humanoid Recoverability Monitoring

    The paper proposes an active reachability learning framework for learning the stochastic finite-horizon recoverability envelope of a given fallback policy, so that fallback can be triggered before an unrecoverable fall occurs. The method reframes data exploration as a reach-avoid problem, in which an uncertainty-guided explorer discovers unseen states while avoiding confidently unrecoverable regions, and combines temporal-difference and Monte Carlo objectives to improve in-distribution value prediction and uncertainty estimation. The authors evaluate on stochastic benchmarks and a 29-DoF Unitree G1 humanoid robot, reporting clear improvements in monitoring coverage and accuracy, and zero-shot transfer to real hardware.

  18. arXiv cs.RO34

    Causal-based approaches to explain and learn from self-extension: A review

    This review surveys recent advances in aware, self-evolving robots, with the goal of enabling robots to give humans explanations for the reasons behind their actions, plan failures, or perceptual anomalies. The authors note that deep learning alone cannot close this gap, because the reasons behind the behavior of black-box models are not accessible; an underlying architecture based on deeper reasoning is needed. When explanations arise from cognitive processes and are shared with and validated by humans, the robot should be able to incorporate them into its knowledge base to improve subsequent behavior.

  19. arXiv cs.RO62

    RAIWAY: 20-inch large-wheel bipedal robot uses contact-informed blind locomotion control to traverse rugged terrain

    A Korean team proposed the RAIWAY wheeled bipedal robot and a contact-informed control method. The robot is equipped with 20-inch wheels and can roll across irregular and discontinuous terrain. The method distills multi-contact events into a latent representation in simulation, and at deployment infers it online from proprioception alone, requiring no contact sensors. Real-world tests covered steep slopes, gravel, studded surfaces, and rocky terrain, and demonstrated continuous ascent on outdoor stairs with step heights up to 15 cm, outperforming baselines without the contact-informed mechanism.

  20. AgiBot73

    AgiBot releases Genie Studio 2.0 embodied intelligence development and deployment platform

    AgiBot has released Genie Studio 2.0, an embodied intelligence application platform covering development, deployment, and operations. It consists of three modules—Flywheel, Agent, and Monitor—that connect data collection, model training, evaluation, inference, task orchestration, and robot fleet management.

    Why it matters: The original text provides the platform's three major modules and validation data from multiple production lines, which can be used to assess the engineering chain of embodied applications from development to operations.

  21. OFweek Robotics58

    Topstar's first three quarters net profit attributable to shareholders up 196% year on year, industrial robot revenue up 88.83%

    Topstar's 2026 third-quarter report shows that in the first three quarters, operating revenue was 1.969 billion yuan, up 16.68% year on year, and net profit attributable to shareholders was 146 million yuan, up 196.38% year on year. Revenue from industrial robots and automated application systems was 1.028 billion yuan, up 88.83% year on year; industrial robot orders totaled about 10,000 units and shipments about 8,600 units.

  22. 36Kr Embodied Intelligence60

    MoSense completes hundred-million-yuan angel+ round to advance full-body tactile solutions and tactile world action model

    MoSense, a company developing full-body multimodal fusion tactile solutions, recently completed a hundred-million-yuan angel+ funding round led by CDH Investments and Oasis Capital, with participation from LimX Dynamics, Loongson Venture Capital, CAS Star, Huashan Capital, Pudong Venture Capital, and a leading domestic embodied AI manufacturer.

  23. 钛媒体60

    Xingyuan Intelligence launches cross-embodiment data engine Chorus Zhongxing

    On October 27, Xingyuan Intelligence launched Chorus Zhongxing, a cross-embodiment data engine that cleans and 3D-reconstructs human manipulation videos from multiple sources, including smart glasses, chest-worn cameras, and UMI devices, and adapts them to various robot embodiments. Under the current test configuration, Chorus achieves an effective data conversion rate of about 85% and adapts to 16 robot embodiments; one hour of pre-filtered human video can generate about 13.6 hours of cross-embodiment motion trajectories, and with 3x instruction augmentation it achieves about 40x effective training data expansion.

  24. Guyuehome27

    RLT: Teaching a frozen VLA the last few millimeters with online reinforcement learning

    RLT proposes freezing the large VLA model and training only a lightweight actor-critic control head: the VLA handles visual and language understanding and coarse actions, while the small network corrects actions online during critical stages to make up the last few millimeters of precision. The two are connected through a bridging structure.

  25. New Strategy Robotics50

    Youibot Launches Industrial-Grade Embodied Intelligence Base Platform MOMABase to Accelerate Deployment of Mobile Manipulation Robots

    Based on its "one brain, multiple forms" architecture, Youibot has launched MOMABase, an industrial-grade embodied intelligence base platform that integrates underlying capabilities such as power supply, communication, computing, and interfaces. It can be paired with actuators and sensors and connect to large models such as VLA, shortening the integration and deployment cycle for multi-form robots.

  26. HuggingFace Daily Papers62

    QCP: Learning Quality-Controllable Robot Policies with Iterative Hindsight Relabeling

    The study proposes Quality-Controllable Policy (QCP), which enables inference-time control over individual quality attributes such as execution speed and motion smoothness, as well as their combinations. To extend the controllable quality range beyond the initial demonstrations, the authors designed an iterative hindsight relabeling loop: collect online rollouts under target quality labels, add successful trajectories to the dataset, then relabel the accumulated trajectories in hindsight according to their measured quality and fine-tune the policy.

Oct 26Mon
  2. New Scientist Robots58

    Cortical Labs uses a live-cell biological computer to control a robot dog's movement

    Cortical Labs' biological computer CL1, built from living cells, was trained in a simulated 2D environment and then used to control the movement of a real robot dog. A paper from the company that has not yet been peer-reviewed shows that the neural network improved over time when continuously fed the same data, and Ferrante Neri of the Education University of Hong Kong considers this preliminary evidence of learning from limited experience. Cortical Labs says the biological computer may consume less energy than digital approaches, and its cells come from blood stem cells donated with donor consent.

  3. 机器人全球资讯 · 微信公众号52

    OpenAI to develop humanoid robots; Altman says the core is building the "brain"

    Speaking on the Sources podcast, Altman confirmed that OpenAI will manufacture humanoid robots itself, arguing that appearance matters far less than the "brain" that drives the robot. He expects a striking demo prototype in 2027, but robots reaching the streets will take several more years, and the company has not announced the appearance, price, or launch date. OpenAI's careers page currently lists 27 robotics positions, covering motion control, state estimation, actuator control, whole-body behavior development, and in-house mechatronic actuators.

  4. Reddit r/robotics58

    Research team uses a single control policy to let Unitree G1 climb ladders and overcome obstacles

    Researchers developed a single control policy that lets Unitree's G1 complete ladder climbing, crawling, obstacle traversal, stepping stones, and recovery after falls by deciding the placement of nine different body parts. The robot can use its hands, elbows, knees, feet, and even pelvis for support rather than relying only on walking, achieving a 92.5% success rate on a simulated obstacle course, but how this flexibility performs in unpredictable real-world environments remains a bigger test.

  5. 未来机器人网 · 微信公众号42

    CITIC Securities Research Report: GPT-6 Astra Enters Robot Control, Physical AI Reaches a Major Inflection Point

    A CITIC Securities research report concludes that GPT-6 Astra, released by OpenAI on September 3, 2026, is extending from high-level robot task planning toward motion generation and execution control, and that general and specialized models are more likely in the near term to divide work and coordinate.

  6. 未来机器人网 · 微信公众号58

    Nomura report assesses humanoid robots: brain not yet at GPT-2 stage, dexterous hands to scale first

    Nomura Securities released a research report on humanoid robots and China's embodied intelligence industry, arguing that robots are developing along the path of large language models, but their current brain has not yet reached the GPT-2 stage, and world model maturity is even lower than AI in 2012. The report proposes five test metrics: continuous working hours, fine hand manipulation, speed of adapting to new embodiments, number of demonstrations, and factory-floor accuracy. It says robots currently fall far short on all metrics, with factory-floor accuracy at about 60% versus the industrial requirement of over 98%.

  7. 机器人产业应用 · 微信公众号62

    NVIDIA Long-WAM and Meta RoboJEPA Submitted on the Same Day: Which Is Worth More, Long Memory or Scaling Laws?

    Long-WAM from teams at NVIDIA, MIT, the University of Hong Kong, and UCSD, and RoboJEPA from Meta FAIR, Mila, and other institutions were submitted to arXiv on the same day, October 7, measuring the returns from adding visual history and from expanding training compute, respectively.

  8. 机器人产业应用 · 微信公众号72

    Zhongke Huiling completes several hundred million yuan A++ round to advance humanoid robot mass production and embodied brain R&D

    Zhongke Huiling has completed an A++ funding round worth several hundred million yuan, jointly invested by Zhilai Investment, Yonghua Investment, Maotian Investment, Yuanhe Chenkun, Guoke Kezhi, Luning Investment and others. The funds will be used for product mass production and delivery, R&D and iteration of embodied brain technology for key scenarios, and market expansion.

  9. 机器之心 · 微信公众号75

    COCO Matrix releases robot model CO01, using experience prompts to let robots learn while doing

    COCO Matrix released the robot model CO01, which treats demonstrations, failed attempts, and real-time guidance as experience prompts in context, allowing adaptation to new tasks without updating weights. Under the same pretrained model, a roughly 1-minute demonstration achieved an average success rate of 52% on unseen tasks; fine-tuning to a similar level required about 180 demonstrations and about 9 hours. In real-robot evaluations with at most five attempts per task, cumulative success rate rose from 33% on the first attempt to 84% when retaining its own history, versus 56% without retaining history.

  10. 机器之心 · 微信公众号78

    RoboCoach: Using World Models to Locate Weak Subtasks and Improve Long-Horizon Task Success Through Targeted Teaching

    Zhang Changshui and Cui Sen's team at Tsinghua University propose RoboCoach, an embodied intelligence system that uses the world model CoachWorld to run closed-loop trials of long-horizon tasks in imagination, identify the first weak subtask that times out, and request targeted human teaching for it, updating only the corresponding skill expert. Under a fixed demonstration budget, the success rate rises from 13.3% to 75.0% on Franka and from 40.0% to 83.8% on AgileX, while the uniform collection baseline reaches only 30.0% and 47.5%, respectively. On four unseen new task routes, the average success rate is 35.0%, compared with 0% for the shared-policy baseline.

    Why it matters: The paper presents a complete experiment on using a world model to locate weak subtasks and improve success rates through targeted teaching; its approach to data budget allocation may be a useful reference.

  11. 机器之心 · 微信公众号62

    UIUC and others propose DSReg, making each JEPA representation dimension correspond to a real-world latent variable

    Researchers from UIUC, Cold Spring Harbor Laboratory, Brown University, and the Max Planck Institute propose DSReg (Dependency-Sparsity Regularization), which can be added directly to an already-trained LeJEPA so that each learned dimension corresponds to a real-world variable, without reconstruction of observations, a decoder, or labels.

  13. 机器之心 · 微信公众号71

    Tsinghua and National University of Singapore Propose ParallelWorld: Multi-Step Rehearsal Improves Embodied Planning Success Rate

    Researchers from Tsinghua University and the National University of Singapore propose ParallelWorld, which adds multi-step candidate future rehearsal to high-level planning for embodied agents, raising task success rate from 56% to 83%. The method generates and verifies multiple candidate action branches in a rehearsal loop; during execution it takes only the first action of the selected trajectory, then replans based on new observations. Success rates were higher than the direct baseline across eight LIBERO-Pro settings, but adaptive search took on average about 5.8 times as long as direct execution.