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#Robot models and learning
Oct 26Mon
  1. 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.

  2. 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.

  3. 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.

  5. 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.

  6. Reddit r/robotics65

    DUET: Humanoids learn new manipulation skills without retraining the balance controller

    DUET proposes separating a humanoid robot's manipulation policy from its balance controller and combining the two commands only at the robot interface, so learning new manipulation skills no longer requires retraining the gait controller. The authors say this removes the hidden cost of retraining the balance controller every time the manipulation policy changes; the paper, code, and a full summary are in the first comment.

  7. 人形大讲堂 · 微信公众号40

    Unitree's Wang Xingxing: The Tipping Point for Physical AI Is Within the Next Year or Two

    Speaking at the 2026 World Physical AI Conference, Wang Xingxing said that GPT-6 can directly control a robot to paint and autonomously wash brushes and change colors with zero training, and that robot planning and navigation problems have been largely solved; the remaining difficulties are the success rate of interaction with real objects and alignment with the physical world. He expects large models to solve assembly problems in one to three years, with voice-commanded room tidying achievable as soon as next year, and announced that the World Physical AI Competition will feature eight tracks and offer prizes of several tens of millions of yuan each year.

  8. 深蓝AI · 微信公众号75

    NVIDIA and Simon Fraser University propose GATOR: reconstructing posed 3D assets from casual photos

    A NVIDIA team, together with Simon Fraser University, proposes GATOR, which uses generative reconstruction as a base and multimodal agents for local refinement to reconstruct complete 3D objects with scene poses from casually taken photos. The method is trained only on synthetic scenes and, without any real-world fine-tuning, leads baselines on geometry, appearance, and pose metrics in real tabletop and indoor scans; without refinement, it takes about 0.5 minutes to outperform GPT-6-Astra running for 20 minutes. The authors also note that reliance on synthetic data, binding to commercial APIs, and stability under extreme poses remain open issues for deployment.

  9. 中科慧灵 · 微信公众号72

    CASBOT completes several-hundred-million-yuan A++ round to advance embodied intelligence mass production and delivery

    CASBOT completed an A++ funding round of 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 iteration of embodied brain technology, and market expansion. The company's self-developed CASBOT PA perception-to-action model has been validated at actual workstations in a shoe factory in Putian, achieving a cycle time of 7 seconds per piece for the outsole stamping process and 18 seconds per pair for sole pressing. In 2026, its total orders for embodied shoe-making robots exceeded 400 million yuan, with equipment production scheduling reaching the scale of several thousand units.

    Why it matters: The funding announcement also disclosed shoe-making workstation cycle times and order scale, which can be used to assess the industrial deployment progress of embodied manipulation models.

  11. OFweek Robotics36

    Is the GPT Moment for Physical AI Approaching?

    General-purpose large models are beginning to take part in robot control. Models such as GPT-6 Astra can already understand complex instructions, recognize objects, and plan operation steps in tests, but fine manipulation still requires coordination with dedicated control systems. Approaches such as HexaShell and WorldScape-RSI explore a division of labor of "large-model decision-making plus specialized-model execution," and tests show some improvement in task performance, though the results mainly come from controlled environments. On data collection, head-mounted operation videos are quoted at about 100 to 300 yuan per hour, while complex real-machine data can reach about 1,000 yuan per hour.

  12. Robotics & Automation News78

    Dream Labs raises $90 million seed round and launches Dream API and Dream-1 model

    Dream Labs announced it has completed a $90 million seed round led by B Capital, with participation from Kindred Ventures and others. The company also launched Dream API, which can convert robot camera footage and natural-language instructions directly into motor commands, and said its flagship model Dream-1 is trained on videos of everyday human tasks and can zero-shot control any robot with a gripper.

    Why it matters: Dream-1's cross-hardware zero-shot control and the open approach of Dream API provide a concrete reference for judging the transferability of general-purpose robot models.

  13. 量子位75

    Tesla Fremont factory begins initial Optimus production, plans to build 10,000 to 20,000 units first for training at Optimus Academy

    In its third-quarter earnings report, Tesla confirmed that the Optimus humanoid robot production line at its Fremont factory has begun initial production, with the first batch of robots meeting mass-production manufacturing standards rolling off the line; the line is planned for an annual capacity of 1 million units. On the earnings call, Musk said the plan is to first build about 10,000 to 20,000 robots and send them to a training facility called Optimus Academy to practice autonomously, using the resulting data to iterate the next generation of AI models, and he expects deliveries to external companies to begin around the end of 2027.

  14. 智东西42

    Unitree's Wang Xingxing: Physical AI Nearing a Tipping Point, GPT-6 Already Deployed to Unitree Robots With Zero Training

    Speaking at the 2026 World Physical AI Conference, Unitree Technology founder Wang Xingxing said physical AI is showing signs of an imminent breakthrough, likely within the next one to two years, and that GPT-6 has been deployed to Unitree robots with completely zero training, enabling tasks such as drawing on its own, playing board games, and finding kitchen tools.

  15. OFweek Robotics58

    Songyan Dynamics reportedly files confidentially with Hong Kong Stock Exchange, 10,000-yuan-class Bumi races toward 10,000-unit delivery

    Citing people familiar with the matter, Bloomberg reports that Beijing robotics company Songyan Dynamics has confidentially filed a Hong Kong IPO application, planning to raise hundreds of millions of dollars and potentially list as early as next year; the company did not respond to the rumors. Songyan Dynamics has built a product matrix including the N-series and E-series bipedal robots, the bionic humanoid head Xiaonuo, and the 10,000-yuan-class Bumi. Founder Jiang Zheyuan set a 10,000-unit delivery target for Bumi in 2026; as of September 2026, cumulative deliveries exceeded 5,000 units, with B-end orders approaching 2,000 units.

  16. arXiv cs.RO60

    CATS: Smooth Control of Deep Reinforcement Learning Policies via Temporal Regularization

    The authors propose the CATS method, which combines a temporal penalty with linear ramp-up to suppress action oscillations in deep reinforcement learning policies. The paper proves that the temporal penalty can bound the expected action differences between current states that share the same successor state, thereby producing a spatial smoothing effect. In simulations and real-world environments, the method is verified to significantly reduce action oscillations without degrading task performance, with very low computational overhead. The work was submitted to ICRA 2027.

  17. arXiv cs.RO62

    TouchSafeBench: A Human-Robot Collaboration Benchmark for Evaluating Collision Grounding in Vision-Language Models

    The authors propose TouchSafeBench, a physically grounded benchmark built in Habitat 3.0 for evaluating the collision grounding capability of vision-language models (VLMs). It contains 2,940 simulated indoor co-presence episodes covering two scenario types, social navigation and social rearrangement, and provides synchronized multi-view RGB-D observations, top-down trajectory maps, calibrated camera metadata, and simulator contact labels.

  18. arXiv cs.RO57

    DepTR-MOT: Depth-Informed Trajectory Refinement for Multi-Object Tracking

    DepTR-MOT proposes a category-agnostic DETR-based detector that embeds instance-level depth awareness into the detector through instance-level soft depth label supervision and dense depth map distillation, and outputs instance depth at inference to improve multi-object tracking under occlusion and close-proximity interactions.

  19. arXiv cs.RO62

    ArtifactArena: Evaluating models by the robots they build in the physical world

    ArtifactArena is an open-ended evaluation platform that challenges models with hardware-software co-design: building working robots in a simulated arena and pitting them against each other. The platform guides models to improve their robot artifacts through three channels: text descriptions, physics simulator feedback, and gameplay data, and compares frontier models using Elo rankings from head-to-head tournaments. The authors are releasing the framework and tournament infrastructure for ongoing community submissions, creating a non-saturating long-term testbed.

  20. arXiv cs.RO60

    RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation

    The authors propose RMMBench, a mobile manipulation benchmark that requires robots to understand language instructions and execute long-horizon tasks in continuous space, with a "navigation-manipulation" task suite covering 70 typical task scenarios. Results show that leading vision-language models (VLMs) still face notable challenges in spatial localization for mobile manipulation tasks, and their spatial perception in long-horizon interactions needs strengthening. The work has been accepted to CoRL 2026.

  22. arXiv cs.RO62

    Do Robotic World Models Really Follow Actions? WorldEcho Diagnoses and WorldSync Aligns Action-Conditioned Generation

    The authors propose WorldEcho, which evaluates whether action-conditioned world models truly follow commanded actions over a broader action distribution through visual integrity and SE(3) end-effector trajectory alignment. The diagnosis shows that models trained on expert data perform well on demonstrated actions but produce action-inconsistent or visually invalid generations on non-expert trajectories.

  23. arXiv cs.RO62

    PartialBiGrasp: A dual-arm grasping framework that infers hidden local geometry from partial views

    PartialBiGrasp is a dual-arm grasp generation framework that operates directly on partial point cloud observations. It implicitly learns geometric features through a convolutional occupancy network to reason about graspability, collision-free contact regions, and object thickness. Based on this, the method generates force-closure-compliant grasp pairs and uses sampling-based optimization to correct for ambiguity caused by incomplete geometry. The authors evaluated the method using analytical force-closure metrics, large-scale simulation experiments, and noisy partial point clouds on a real robot.

  25. arXiv cs.RO66

    RAPAC-DP: Response-Aligned Pending-Action Compensation for Diffusion Policies under Delayed Execution

    RAPAC-DP proposes a response-aligned pending-action compensation framework that enables diffusion and flow-based action generators to still output full action chunks aligned with the handover moment under cloud inference latency. The method uses a lightweight pending-action encoder and LoRA adapters to learn the compensation while keeping the base policy frozen, and constructs delay-conditioned training samples from delay-free demonstrations, requiring neither an explicit dynamics model nor additional delay data.

  28. arXiv cs.RO60

    Survey: 3D Generation for Embodied AI and Robotic Simulation

    This survey examines three roles of 3D generation in embodied AI and robotic simulation: as a data generator producing simulation-ready assets with articulated structure, physical properties, and deformable content; as a builder of interactive, task-oriented scenes for simulation environments; and as a sim-to-real bridge supporting digital twin reconstruction, data augmentation, and synthetic demonstrations. The authors note that the field is shifting from visual realism toward interactive usability, and they list key bottlenecks including limited physical annotation, the disconnect between geometric quality and physical validity, fragmented evaluation, and the sim-to-real gap.

  29. arXiv cs.RO62

    Continuum Robot State Estimation Based on Factor Graphs and Magnus Expansion Priors

    This paper proposes estimating the coefficients of a compact Geometric Variable Strain (GVS) parameterization within a factor graph framework, and uses a kinematic factor derived from the Magnus expansion of the strain field to link the GVS strain coefficients with backbone pose variables, thereby reconstructing the shape of a continuum manipulator. The method is compared against a Gaussian process regression baseline on simulated and experimental data, achieving higher accuracy in partial and sparse measurement scenarios while maintaining computational efficiency. The state vector is compact and can be used directly for model-based control.

  30. arXiv cs.RO68

    ENAP: Neural Automaton Policies That Learn Symbolic Structure from Visuomotor Trajectories

    The authors propose ENAP (Emergent Neural Automaton Policy), a neuro-symbolic imitation learning framework that uses adaptive clustering and an extension of the L* algorithm to learn a Mealy state machine from demonstrations as an interpretable high-level planner, which then guides a low-level reactive residual network to learn continuous control via behavior cloning.

  31. arXiv cs.RO62

    VORL-EXPLORE: A Hybrid Learning Planning Approach for Multi-Robot Exploration in Dynamic Environments

    The authors propose VORL-EXPLORE, a hybrid learning and planning framework that uses execution fidelity to estimate local navigability and execution reliability, coupling it into a Voronoi allocation objective with inter-robot repulsion and arbitrating during navigation between global A* guidance and a reactive reinforcement learning policy. The fidelity model is recalibrated using self-supervised pseudo-labels generated from recent progress and safety outcomes. Experiments on randomized dynamic grids and an ablation under severe congestion show higher success rates, shorter exploration paths, and lower inter-robot coverage overlap, along with a deployment example in a Gazebo factory scenario; the code is open-sourced.

  32. arXiv cs.RO63

    Deep Learning Warm Starts Accelerate Trajectory Optimization for Astrobee on the International Space Station

    A research team completed an on-orbit demonstration on the International Space Station's Astrobee free-flying robot of machine learning-based warm starts to accelerate trajectory optimization, using a neural network to predict initial values for a sequential convex programming (SCP) solver, which then enforces safety constraints. In scenarios including rotational dynamics, the trained network reduced the number of solver iterations required for convergence by 60%, and by 50% in obstacle scenarios drawn from the training distribution. The work was published at iSpaRo 2025.

  33. arXiv cs.RO65

    SceneBloom: An Agentic Indoor Environment Generation Framework for Embodied Learning

    SceneBloom proposes an agentic framework that combines visual layout sampling with contract-guided 3D compilation to generate realistic and diverse simulated indoor environments. Its hierarchical sampler uses diffusion priors to generate furniture layouts within functional zones, and the compiler then performs geometric fitting and local pose correction based on relations extracted from each proposal.

  34. arXiv cs.RO67

    RobustRLlib released: unifies 17 robust reinforcement learning algorithms and provides evaluation across 273 perturbation configurations

    RobustRLlib is an algorithm-centric library and benchmark for robust reinforcement learning that integrates 17 representative online, offline, and safe reinforcement learning algorithms under a unified interface. Its evaluation platform covers a variety of real-world shifts as well as humanoid robot and vision-language-action tasks, and it presents the capability distribution of current robust reinforcement learning algorithms across 273 perturbation configurations, identifying which scenarios remain effective and where substantial robustness gaps persist. The project provides open-source code, a project page, and tutorials to facilitate reproduction and extension.

  35. arXiv cs.RO60

    SMM-AC: Regularized reinforcement learning improves sim-to-real transfer

    The authors propose SMM-AC, a regularized reinforcement learning algorithm based on the soft mellowmax value function. On MuJoCo continuous control benchmarks it is comparable to baselines such as SAC, and on real robots it achieves higher returns while being more robust to perturbations in observations, the environment, and dynamics. The study compares the sim-to-real transfer performance of commonly used reinforcement learning algorithms and finds that regularization improves policy robustness to the sim-to-real gap and to real-world environment changes.

  36. arXiv cs.RO68

    Retiming Robot Demonstrations with a Learned Speed Prior

    The study proposes learning a speed prior from large-scale demonstration data: given a task instruction, a motion path, and images along the path, it predicts a distribution of execution speed along the path and uses a selected quantile as a unified clock to retime each demonstration. The method fits the speed prior on robot teleoperation data and on human handheld-gripper demonstrations, retimes robot demonstrations to the handheld pace, and then fine-tunes a vision-language-action policy; at execution, a real-time optimal-control layer tracks the predicted action chunks under robot constraints.

  37. arXiv cs.RO47

    CEMIR vs. FISER: A Comparative Study of Reasoning-Based and Enrichment-Based Approaches to Pragmatic Intent Recognition

    The study compares FISER, which is based on structured social and embodied reasoning, with CEMIR, which performs multimodal enrichment by synthesizing narratives and photorealistic images from text prompts, on the HandMeThat v2 benchmark, covering four large language models from Qwen2.5-Omni-3B to GPT-5.

  38. arXiv cs.RO61

    SAGE: Work-zone path prediction under temporary traffic control combining vision-language and geometric frameworks

    The authors propose the SAGE (Semantic-to-Action Grounded Execution) framework, which uses trajectories output by a vision-language model (VLM) as preferences over complete geometric candidate paths, and combines each candidate's geometric score with its agreement with the VLM trajectory through rank fusion.

  39. arXiv cs.RO42

    StackGen3D: Generating Stable 3D Block Stack Structures from Multiview Silhouettes

    The StackGen3D framework generates stable block assembly structures from a given block inventory and three orthographic silhouette images. It first uses a physics-informed graph neural network (PI-GNN) to model support relationships between blocks and to curate a dataset of stable structures, then trains a conditional flow-matching model to learn the multimodal distribution of block configurations that match the target silhouettes. In preliminary experiments, Best-of-10 selection raised physical stability from 45.3% to 72.4%, with a silhouette IoU of 0.970.