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#Robot models and learning
Nov 6Fri
  1. CVer · 微信公众号62

    NeurIPS 2026 Paper RePO-VLA: Dual-Arm Robot Disturbance Recovery Success Rate Raised to 75%

    Sun Yat-sen University, Peng Cheng Laboratory, and other institutions propose RePO-VLA, which segments recovery trajectories, trains a progress-aware semantic value function, and performs value-conditioned fine-tuning so that dual-arm VLA policies can continue advancing tasks after disturbances. The study also builds FRBench, a failure-recovery benchmark; the RoboTwin simulation contains 46 dual-arm tasks and 23,453 trajectories. On real robots in water-pouring and towel-folding tasks, as recovery demonstrations per task increase from 50 to 200, the average disturbed success rates are 30%, 65%, and 75%, respectively.

Nov 5Thu
  1. 新智元 · 微信公众号60

    Xiamen University and NTU propose RoTA-Bench: interactive projection alignment benchmark for 3D pose reasoning

    A team from Xiamen University and Nanyang Technological University proposed RoTA-Bench, which uses interactive 3D-to-2D projection alignment to evaluate whether vision-language models can precisely adjust an object's 3D pose based on visual feedback. The benchmark contains 674 tasks, of which 100 form a frozen test set; models must choose among 24 discrete rotation actions so that the projection reaches an IoU of 0.90 with the target contour.

  2. 新智元 · 微信公众号58

    Embodied AI MaaS rises: comparing three approaches from Yuanli Lingji, Physical Intelligence, and Dream Labs

    The article argues that, limited by onboard compute and heat dissipation in robot bodies, embodied model inference is shifting to the cloud, and embodied AI MaaS is becoming a new direction. In July 2026, Yuanli Lingji released a general embodied MaaS service; in October, Physical Intelligence launched an invitation-only Pi API Beta; and Dream Labs launched Dream API backed by a $90 million seed round.

  3. Reddit r/humanoidrobotics22

    Asim is building a real-world physical intelligence benchmark for humanoid robots, focusing on dexterous manipulation

    Tristan, co-founder of YC F26-incubated company Asim, said the team is building a standardized benchmark for physical intelligence in humanoid robots, with a focus on evaluating dexterous manipulation capabilities. The benchmark uses repeatable scenarios to examine spatial reasoning, grasp adaptation, coordination, and how robots respond to physical interaction, with the goal of pinpointing where capabilities fail and providing reproducible failure cases. The team also noted the need to address comparability issues caused by differences in hardware, sensors, and control interfaces.

  4. 人形大讲堂 · 微信公众号58

    Geek+ Gino 1 Enters the Warehouse: Which Processes Will the Wheeled Humanoid Replace?

    Geek+'s wheeled humanoid Gino 1 was released on February 10, featuring 14 degrees of freedom in its dual arms, three-fingered dexterous hands, a Jetson Thor compute platform, and a VLA fast-slow collaborative system; it can perform four types of work: picking, box handling, packing, and inspection. The article compares it with cases such as Agility Robotics' Digit and Robot Era's L7, pointing out that the key to humanoids entering warehouses lies in interfaces, data sources, and the maintenance response radius, rather than the form factor itself. Geek+ says Gino 1 already has mass-production capability and can connect to its own fleet formed by its scheduling system, picking stations, and AMRs for storage, picking, handling, and inspection.

  5. 深蓝AI · 微信公众号74

    HKUST's Shen Shaojie team proposes RAVEN: an active SLAM framework unifying UAV active exploration and loop-closure localization

    The Shen Shaojie team at HKUST has proposed RAVEN, a unified active SLAM framework that uses utility vectors to separately quantify exploration efficiency and localization accuracy, selects global tours according to Pareto optimality, and uses a priority parameter λ (0–1) to determine whether to favor exploration or localization.

  6. Tech Xplore Robotics62

    MIT proposes UQ-DAAAM to correct erroneous object descriptions in robot memory using multi-view images

    MIT researchers propose the UQ-DAAAM system, which quantifies a robot's uncertainty about what it sees by comparing multiple descriptions of the same object from different viewpoints, selects additional viewpoints to correct erroneous annotations, and writes the corrections back to memory. The method adds uncertainty quantification to the DAAAM framework, uses heuristic rules to select frames that can reduce uncertainty without rerunning the vision-language model, and then uses a large language model to fuse the multiple descriptions.

  8. HuggingFace Daily Papers58

    DRAP: Dynamics-Risk Adaptive Plasticity for Continual Learning in Physical AI

    The study proposes DRAP, a replay-free continual learning method that uses locally trajectory-amplified, risk-weighted action gradients to estimate the historical importance of model tensors and scales each tensor's effective AdamW learning rate accordingly. The method is instantiated on OpenVLA's KappaTune subnetwork and sequentially adapted on the LIBERO Spatial, Object, and Goal suites; compared with standard AdamW fine-tuning and EWC, it markedly improves retention of old behaviors while maintaining the ability to learn new suites.

  9. Guyuehome47

    Deep reinforcement learning-based quadrotor target tracking control: experiments with 64×64 visual input in Isaac Lab

    A quadrotor tracks a yellow target ball using only a 64×64 camera image. The official pretrained policy was loaded to complete tracking experiments in Isaac Lab, with the target visible about 40% of the time and the quadrotor autonomously hovering to reacquire the target after losing it. The article also discusses known gaps when migrating the policy to the DJI Tello.

  10. 亿欧网 · 微信公众号62

    Wang Xingxing vs. Zhi Hui Jun: Unitree adopts general models while AgiBot develops in-house models for production lines

    The article compares Unitree and AgiBot's divergent approaches to embodied intelligence: Unitree integrates GPT-6 Astra into the G1 as an "external brain," with Wang Xingxing judging that general models will be able to handle planning after another one or two generations; AgiBot only lets Astra serve as System2, while System1 and the 100 to 1000Hz force-control closed loop are developed in-house, and GO-3 Preview tops three leaderboards: RoboDojo, RoboTwin, and RoboCosieum.

  12. Robotics & Automation News78

    AGIBOT releases embodied foundation model GO-3 Preview, tops three embodied AI benchmarks

    AGIBOT has released GO-3 Preview, an embodied foundation model built on its self-developed WRAM (World Reasoning Action Model) architecture that unifies task understanding, future state prediction, and action generation. It ranks first on three embodied AI benchmarks: RoboDojo, RoboTwin, and RoboColiseum, and is planned for full open source release by the end of 2026.

    Why it matters: AGIBOT has disclosed the architecture and three-stage training design of GO-3 Preview, which can be compared against its technical roadmap evolution from GO-1 to GO-3.

  13. 钛媒体40

    D-Robotics, High Torque Power, and Qiyuan Robotics Enter K12 Embodied AI Education

    D-Robotics partnered with Unitree Robotics to launch an embodied AI education solution for primary and secondary schools, using the Sunrise S100 chip for integrated computing and control, paired with the Go2 quadruped and R1 humanoid bodies, and designing 15 themed courses totaling 180 lessons. High Torque Power released a full embodied AI education solution the same day, with courses already running regularly at schools including RDFZ and Dongzhimen Middle School. Qiyuan Robotics pushed its first OTA to the T1 and Q1, adding Hi Echo speaking practice and AI introductory courses.

  14. 钛媒体65

    After GPT-6 Astra's release, how is the embodied intelligence industry chain landscape changing?

    After GPT-6 Astra's release, general large models can directly control robots to perform tasks; embodied data orders are rebounding, the value of body companies is being reassessed, and model companies building embodied brains now face competition on the same field as foundation model giants. In NVIDIA testing, Astra achieved a 66.3% success rate in zero-shot direct action output, higher than π0.5, but average single-inference time was 18.56 seconds, and one successful action consumes about 160,000 tokens, equivalent to $1.96. The industry currently prefers having general models handle planning and the semantic layer, while embodied models handle millisecond-level motion control and the execution layer.

  15. 钛媒体38

    GPT-6 Astra shakes up the embodied AI brain industry: risks and opportunities for Unitree, AgiBot, Qianxun and other vendors

    GPT-6 Astra, a general-purpose large model, drove a several-hundred-dollar robotic arm to complete tasks such as drawing the Golden Gate Bridge without specialized training. Unitree Technology used it to autonomously identify a palette, change paints, and clean brushes. SenseTime's evaluation says it can handle robot manipulation and navigation tasks that specialized models such as VLA and WAM perform.

  16. 爱范儿34

    Interview with Stardust Intelligence's Lai Jie: the rope-driven robot, AOS operating system, and Lumo-2 embodied model's "iPhone 4 moment" has not yet arrived

    In an interview, Lai Jie, founder and CEO of Stardust Intelligence, said the company's architecture combines a rope-driven robot body, the embodied operating system AOS, and an embodied model. AOS packages perception, movement, manipulation, and interaction into skill modules, can adapt to large models and Agents such as GPT 6, and lets developers build applications using natural language. Its Lumo-2 embodied model has implicit world dynamics prediction capability and, combined with reinforcement learning, builds a trial-and-error and error-correction loop. Lai Jie believes the robotics industry's "iPhone 4 moment" has not yet arrived, but may be closer than imagined.

  17. OFweek Robotics42

    D-Robotics, High Torque Power, and Qiyuan Robotics Enter the K12 Education Race

    On October 30, D-Robotics partnered with Unitree Robotics to launch an embodied intelligence education solution for primary and secondary schools. On the same day, High Torque Power released a comprehensive embodied intelligence education solution, and Qiyuan Robotics announced the first OTA for T1 and Q1, adding Hi Echo speaking practice and AI introductory courses. The D-Robotics solution is built around the Sunrise S100, which integrates a CPU, BPU, and MCU, paired with the Unitree Go2 quadruped and R1 humanoid bodies, and features 15 themed courses and 180 lessons covering elementary through high school. High Torque Power said the related courses have been regularly carried out at schools including Renmin University of China Affiliated High School and Dongzhimen High School.

  18. arXiv cs.RO58

    Warp: Structured Output-Space Adaptation for Frozen Robot Policies

    The authors propose Warp, a context-conditioned monotone bijective spline adapter that nonlinearly reshapes the sampled outputs of a frozen base policy while preserving per-dimension ordering. Across four ManiSkill tasks, Warp achieves the strongest recovery in a matched evolution-strategy adapter study; after switching to PPO, the ordering relationships vary by task. In a physical SO101 peg insertion task, Warp's success rate is comparable to a strong stochastic residual, with median time per successful cycle reduced by about 30%.

  19. arXiv cs.RO62

    PS2-RL: A Provably Safe and Scalable Reinforcement Learning Framework

    The authors propose PS2-RL, a two-phase framework that uses a learned backup policy to forward-integrate the system dynamics and generate an implicit control-invariant set online, thereby avoiding the dimensionality-scaling problems caused by explicitly constructing control-invariant sets. In the first phase, the backup policy is trained with a safe-arrival value function to achieve safe recovery to a certified base set; in the second phase, an RL policy is trained end-to-end through a differentiable projection layer, strictly guaranteeing the safety induced by the backup policy. The framework does not restrict the underlying RL algorithm and can be integrated into existing training pipelines. It was evaluated on robot control tasks with state dimensions up to 10.

  20. arXiv cs.RO71

    RoboTryOn: Synthesizing Robot Dexterous Manipulation Demonstrations from Human Videos via Virtual Try-On

    RoboTryOn proposes treating human-to-robot translation as virtual try-on: it preserves the demonstrated motion, objects, and background, and only replaces the human with the target robot specified by a reference image. The method autoregressively converts keyframes and uses them to guide video synthesis, with motion conditioned on the human hand skeleton; it then uses an inverse dynamics model (IDM) with depth features to infer action labels from the synthesized videos for policy learning. Experiments show that policies trained only on synthetic data improve average success rate from 2% to 60% on new tasks, including dual-arm manipulation, and reach the level of policies taught by pure teleoperation at about one-third of the collection cost.

  21. arXiv cs.RO60

    Study: How inertia mismatch affects robot policy learning when robots are heavier than objects

    This paper investigates how inertia mismatch between a robotic arm and the objects it manipulates affects robot policy learning. Through controlled simulation experiments that vary robot and object masses, it finds that reinforcement learning succeeds across a broad range of mass combinations, while behavior cloning learns more reliable policies when a lighter robot manipulates a heavier object.

  22. arXiv cs.RO58

    PriMoRetarget: Humanoid Motion Transfer via Prioritized Motion Retargeting

    The authors propose PriMoRetarget, a motion retargeting framework that replaces relative weight weighting with an explicit priority hierarchy, so that lower-priority updates are constrained to preserve higher-priority objectives. The method is evaluated on four datasets covering single-person motion, person-object manipulation, and person-person interaction; the generated motions better preserve task-relevant interactions and provide reference trajectories that are easier to track than those of commonly used retargeting methods.

  23. arXiv cs.RO67

    Reverse to Advance: Learning Hard Manipulation Policies with Reversed Easy-Task Data to Cut Teleoperation Costs

    This paper proposes a teleoperation-cost-effective framework that uses temporally reversed trajectories from easy tasks as supervision signals for hard manipulation tasks, reducing manual demonstration collection. The framework has three parts: a closed-loop data collection pipeline, hierarchical data refinement based on kinematic priors and critic-guided advantage filtering, and iterative policy learning. In two simulation benchmarks and real-robot experiments, the method improved success rates on hard tasks compared with reversal-based and reinforcement-learning baselines, with higher data efficiency and more stable training.

  24. arXiv cs.RO60

    SDN: Improving VLA policy robustness at test time via selected diffusion noise

    The authors propose Selected Diffusion Noise (SDN), a training-free method that treats diffusion noise as a controllable degree of freedom to improve the test-time reliability of VLA policies. SDN samples multiple candidate action chunks and performs two-stage selection: it first filters by task-relevant visual grounding using a counterfactual negative set, then selects the kinematically most stable action chunk based on JerkRMS. In simulation and real-world experiments with three VLA policies, it achieved improvements of 8-10% and 18.33%, respectively.

  25. arXiv cs.RO65

    InDex: Adapting VLA models to dexterous manipulation via intent-conditioned fine-tuning

    The study proposes the InDex framework, which uses a normalized aperture signal derived from retargeted demonstrations to supervise a scalar grasp intent, adapting parallel-gripper VLA models to multi-fingered dexterous hands in two stages. Across four simulated tasks, InDex with π0.5 achieved 85.8% mean task success, a 35.5 percentage point improvement over the native policy, and reached 69.0% on new objects with similar geometry without fine-tuning. It was also effective across three VLA backbones and on a separately trained real-world policy, whose mean success rate rose from 71.0% to 83.0%.

  26. arXiv cs.RO65

    Learning Soft Robot Dynamics: An Uncertainty-Aware Active Exploration Framework

    The authors propose an uncertainty-aware active exploration framework that can autonomously learn task-agnostic, generalizable dynamics models for soft robots. The method was evaluated on three simulation platforms—a continuum arm, an articulated fish in fluid, and a musculoskeletal leg—as well as on a real pneumatically actuated continuum arm. Compared with random exploration and task-specific baselines, it achieves higher modeling accuracy and yields higher returns in zero-shot control on unseen tasks, while remaining robust under sensing noise, actuation delays, and material nonlinearities.

  27. arXiv cs.RO60

    MARLIN: Guiding Multi-Agent Reinforcement Learning with Language Negotiation

    MARLIN proposes a hybrid framework in which large language models guide policy learning by generating high-level action plans through inter-robot language negotiation before the reinforcement learning policy has learned effective behaviors, and it dynamically switches between reinforcement learning and language-model negotiation during training. The authors evaluated it on simulated and physical robots with local and remote language models; results show higher early-training performance than standard multi-agent reinforcement learning, with limited impact on final performance. The code is publicly available, and the paper was accepted to ROBIO2026.

  28. arXiv cs.RO72

    Reinforcement Learning Enables Scalable Multi-Task Improvement of Generalist Robot Policies

    This paper examines whether generalist robot policies can be improved online at deployment time for multiple different tasks simultaneously, and systematically evaluates algorithmic and engineering choices such as value estimation, policy extraction, and resource allocation. The authors build a structured evaluation suite to test generalist improvement capability and find that a suitable recipe not only improves multi-task performance online but also generalizes to tasks not covered in online training, with the training task distribution and value-based reinforcement learning being key factors. The authors describe this as one of the early pieces of evidence for online generalist improvement of robot foundation models.

  29. arXiv cs.RO58

    Fluidic Body: Controlling swarm robots as a fluid extension of the hand

    The authors propose the Fluidic Body system, which lets an operator change the properties of a virtual fluid between the hand and a swarm robot collective through hand movements, thereby adjusting swarm behavior: opening the hand makes the swarm soft and deformable so it can flow around obstacles, while clenching the fist makes the swarm hard and cohesive so it can aggregate into a single body to push objects.

  31. arXiv cs.RO38

    3D Rotation Representations: Mathematical Foundations and Comparative Analysis

    This paper systematically reviews multiple rotation representations of SO(3), including Euler angles, axis-angle vectors, quaternions, rotation matrices, exponential maps, and emerging continuous and probabilistic methods, and compares them across dimensions such as continuity, susceptibility to gimbal lock, computational efficiency, storage requirements, interpolation, and composition operations. Empirical results show that quaternions are advantageous due to their compactness and efficiency, while 6D continuous representations and matrix Fisher distributions offer greater advantages in continuity and uncertainty modeling.

  32. arXiv cs.RO84

    HALO: Humanoid Autonomy System Trained on Synthetic Simulation Data Achieves Zero-Shot Real-World Transfer

    The research team proposes HALO, a humanoid robot autonomy system that synthesizes vision-language-action data across roughly one thousand procedurally generated home scenes, eliminating reliance on teleoperation data collection. HALO learns skills including sitting on chairs, navigating to specified objects, stepping sideways through half-open doors, crouching and crawling under tables, and pushing boxes, and can combine them to complete long-horizon tasks. Without ever seeing real home scenes during training, HALO transfers zero-shot to the physical world, achieving a 91% success rate across 360 trials in six unmapped real-world environments.

    Why it matters: The paper presents a complete path from synthesizing vision-language-action data using procedurally generated home scenes to zero-shot transfer to real robots; its approach to data scaling is worth referencing.

  33. arXiv cs.RO65

    VABAR: A Variational Automation Benchmark for Agentic Robotics released

    The authors propose VABAR, a Variational Automation Benchmark for agentic robotics, built on open-source tools and the Newton physics engine for simulated manipulation evaluation. The benchmark covers six manipulation task classes: tool sorting, tool hanging, gear meshing, syringe packing, cable insertion, and cable routing. It includes standardized task variants, difficulty levels, success metrics, 33 generalizable skills, and human-written programmatic reference solutions. The paper also presents an initial baseline evaluation based on Graph-as-Policy (GaP) and Isaac Lab Arena.

  34. arXiv cs.RO58

    Code as Policy Agents' Self-Repair: What Drives Retry Success

    This study compared retry mechanisms of code-as-policy agents on simulated robot manipulation tasks: on the same 252 initial failure cases, Qwen2.5-Coder-7B, using feedback templates selected by failure labels, recovered 85.3% within five retries, versus 68.3% for resampling with the same retry cap, and the advantage persisted under a fixed total token budget.

  36. arXiv cs.RO58

    Compounding Physical RSI: A Robot Policy Self-Evolution Framework That Turns Execution Failures into Inheritable Capabilities

    The authors propose Compounding Physical RSI, a self-evolving policy framework that uses automated capability-gap assessment to locate the task stage where a failure occurs, refines policies with behavior-regularized offline reinforcement learning, and builds candidates in weight space to preserve existing skills.

  38. arXiv cs.RO62

    Dynamic Shape Control of Soft Trunk Robots Based on aSSM Model and MPC

    The research team learned an adiabatic spectral submanifold (aSSM) model of the full body of a trimmed-helicoid soft trunk robot from data and used it in model predictive control (MPC), completing a range of shape control tasks on hardware. In closed-loop dynamic shape regulation, the method outperformed existing linear baselines by a factor of 10; in human-in-the-loop interactive demos, it completed section-by-section shaping and balanced a tennis ball for more than 25 seconds, whereas the linear baselines failed to complete the task. A model-free reinforcement learning policy trained on hardware for 12 hours failed on the same balancing task.

  39. arXiv cs.RO72

    soccerbot: Learning Humanoid Soccer Skills with Latent Motion Priors

    The authors propose soccerbot, a hierarchical reinforcement learning framework that distills a structured latent motion prior from large-scale human soccer motion capture data, constraining policy learning within a dynamically feasible motion manifold. A single skill-conditioned policy thereby learns moving, dribbling, trapping, and kicking in a unified way, while maintaining human-like motion and fluid transitions during skill switching. After robustness-oriented simulation design, the framework transfers zero-shot to the Unitree G1 humanoid robot, completing the full soccer skill set in real-world environments.