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#Robot models and learning
TodayNov 11Wed4 updates
  1. Apptronik Social67

    Apptronik shares Gemini Robotics 2.5 release, highlighting its breadth of skills

    Google DeepMind has released Gemini Robotics 2.5, saying this physical AI can learn hundreds of everyday skills, including folding clothes, serving pastries, and autonomously sorting car parts in factories. Apptronik shared the news and added examples such as tying a bow and sorting car parts, saying this reflects the model's breadth of skills.

    Why it matters: Gemini Robotics 2.5 demonstrates hundreds of skills spanning household chores and factory sorting, offering a view of the capability boundaries of general-purpose manipulation models.

  2. Guyuehome30

    Dexterous Hand Practice 21: How VLA Controls a Dexterous Hand, from Language Instructions to Constrained Actions

    Dexterous Hand Practice 21 focuses on how VLA (vision-language-action models) control a dexterous hand, outlining the process from language instruction input to the generation of constrained actions. The content is tutorial in nature, explaining this control chain, and does not provide specific robot models, experimental data, or success rate metrics.

  3. Reddit r/robotics58

    Building photorealistic simulation environments with augmentable Gaussian splats to train robot policies

    The author turned to high-quality Gaussian Splats of real sites to build augmentable, relightable photorealistic simulation environments, because policies trained in animated simulation were hard to transfer to real machines. The pipeline, refined over hundreds of iterations, converts iPhone scans into Splats plus meshes, selects robot-operable assets, and splits them by unit to generate detailed assets placed back at their original positions in the Splat. Physics and rendering use MuJoCo Warp, and the robot is a Franka Panda.

Nov 10Tue
  1. The Humanoid Hub78

    Robbyant open-sources LingBot-Touch tactile foundation model

    Robbyant has open-sourced LingBot-Touch, a tactile foundation model pretrained on about 5,000 hours of real tactile data that reads magnetic, vision-based tactile, and piezoresistive sensor inputs into a unified token space. Across 26 real robot tasks and two dexterous hands, four policies using these tokens all achieved the highest average success rates; on specific sensor models unseen during pretraining, a frozen encoder reduced contact-center error from 5.89 mm to 0.16 mm, outperforming an adapter built specifically for it.

    Why it matters: The tactile foundation model unifies multiple types of tactile sensors into the same token space and provides cross-sensor and cross-task transfer results.

  2. 新智元 · 微信公众号71

    Xi'an Jiaotong University's Lan Xuguang team proposes AffordSim: generating robot manipulation data via 3D affordance prediction

    Professor Lan Xuguang's team at Xi'an Jiaotong University has proposed AffordSim, which connects open-vocabulary 3D affordance prediction to simulation trajectory generation. Starting from a natural language task description, it locates an object's functional regions, then combines cuRobo motion planning to generate executable manipulation demonstrations.

  3. APPSO · 微信公众号0

    NVIDIA launches GR00T 2 to let robots learn fine manipulation from human demonstrations

    At CoRL, NVIDIA unveiled the GR00T 2 robot learning model, built on NVIDIA Cosmos, which can learn from human demonstrations and transfer skills across different robot embodiments. Of the 50 tasks it evaluated, 40 were successfully completed at least once with no task demonstrations; after one demonstration per task, the average success rate was 71%, and after five demonstrations it reached 95%.

  4. 机器人前瞻 · 微信公众号88

    NVIDIA releases GR00T 2 series world-action models, trained on 400,000 hours of human video

    At CoRL 2026, NVIDIA launched the open-source world-action model series GR00T 2, comprising two versions, GR00T 2 and GR00T 2 Turbo. The former focuses on generalization and fine manipulation, while the latter has fewer parameters and can be deployed directly on the Unitree G1, with a single inference taking 124 milliseconds on the onboard Jetson Thor.

    Why it matters: NVIDIA released the GR00T 2 series at CoRL 2026 and disclosed the scale of three types of pre-training data along with success rates for cross-embodiment transfer and few-shot adaptation, which can be used to assess the current capability boundaries of world-action models.

  5. 机器人前瞻 · 微信公众号72

    Gamma Intelligence, TeleAI and Unitree launch LocoManiLab humanoid mobile manipulation evaluation framework

    Gamma Intelligence, TeleAI and Unitree have jointly launched LocoManiLab, a unified evaluation framework for humanoid robot mobile manipulation. It covers five types of home spaces, 13 scenario instances and 234 tasks, with the Unitree G1 and Dex3 dexterous hands as the unified test platform.

  6. Google DeepMind · X73

    Google DeepMind releases Gemini Robotics 2.5 physical AI model

    Google DeepMind has released Gemini Robotics 2.5, saying this physical AI model can learn hundreds of everyday skills, including folding clothes, serving pastries, and autonomously sorting auto parts on a factory floor. The official announcement outlined its capability scope with a set of images and did not disclose the model size, training data, or evaluation metrics.

    Why it matters: The official announcement gives the capability scope of Gemini Robotics 2.5, which can learn hundreds of everyday skills, making it easy to compare against the previous generation to judge progress.

  7. Google DeepMind · X53

    Google DeepMind, Apptronik and Mercedes-Benz complete Gemini Robotic 2.5 proof of concept; Apollo 2 achieves 96% accuracy sorting car parts

    Google DeepMind, Apptronik and Mercedes-Benz conducted a proof of concept in which Gemini Robotic 2.5 drove the Apollo 2 robot to pick and organize car parts for assembly, achieving 96% accuracy. The result is currently at the proof-of-concept stage; deployment scale and production-line conditions have not been specified.

  8. 未来机器人网 · 微信公众号55

    MouShen Intelligence Launches General Embodied Brain Industry Innovation Consortium with 42 Institutions

    On November 7, the General Embodied Brain Industry Innovation Consortium was launched at a side exchange meeting of the China International Import Expo in Shanghai. Initiated by MouShen Intelligence, it brings together 42 enterprises and research institutions, with members including Fudan University's School of Future Information Innovation, the National and Local Co-built Humanoid Robot Innovation Center, Unitree Robotics, and UBTECH.

  9. 人形大讲堂 · 微信公众号71

    UniX AI completes RMB 500 million Pre-A funding round; Wanda 2.0 humanoid robot priced at RMB 88,000

    Suzhou-based general embodied intelligence company UniX AI announced on November 9 that it has completed a RMB 500 million Pre-A funding round. Existing shareholders Xienuo Investment and Yonghua Investment increased their investment, while Greenland Group, Keli Sensing, Huagong Venture Capital, Xinjing Fuying, Redbird Qihang Fund, and Yulin Industrial Investment joined as new investors.

  12. Tech Xplore Robotics58

    UC San Diego proposes EgoThumb wearable robotic thumb for one-handed completion of two-handed tasks

    A team at the University of California San Diego proposed EgoThumb in an arXiv preprint, a wearable robotic thumb driven by a palm-side wide-angle camera and an imitation learning model that can autonomously grasp and stabilize objects, allowing users to complete tasks such as unscrewing bottle caps and stirring tea with one hand.

  13. 银河通用机器人 · X38

    GALBOT ET1 Wins CES 2027 Robotics Innovation Award

    Galbot's bipedal humanoid robot GALBOT ET1 has won Best of Innovation, the highest honor in the robotics category of the CES 2027 Innovation Awards, becoming the first bipedal humanoid robot in the award's history to receive this honor. ET1 relies on the AstraBrain-WBC foundation model to learn new movements by watching live human demonstrations, without the need for separate motion capture or pre-recorded videos for each new movement, combining perception, interaction, and motion. This capability is currently used in real-world scenarios such as greeting and reception and interactive performances.

  14. 量子位87

    NVIDIA releases world action model GR00T 2, enabling cross-embodiment transfer of human demonstrations

    NVIDIA's GEAR team released its next-generation world action model GR00T 2 at the CoRL conference. It can simultaneously predict future frames and robot actions. The model, data, simulation environment, and collection hardware will be open-sourced progressively, and 100,000 hours of human data have already been released first.

    Why it matters: NVIDIA GR00T 2 pretrains on a mix of human video, UMI, and teleoperation data, and reports quantitative success rates for cross-embodiment transfer, which can be used to assess the data strategy for robot foundation models.

  15. 芯东西78

    Ant Group's Lingbo open-sources LingBot-Touch tactile pretrained model, unifying signals from multiple sensor types

    Ant Group's Lingbo on November 10 open-sourced LingBot-Touch, a tactile pretrained foundation model for dexterous manipulation that can encode different sensing signals such as piezoresistive and vision-based tactile signals into unified tactile tokens for use by different dexterous hands and policy models.

    Why it matters: The original article provides comparative data on the cross-sensor tactile pretrained model across 26 real-robot tasks, which can be used to assess the actual impact of tactile representations on policy success rates.

  16. 量子位88

    Ant Group's Lingbo open-sources LingBot-Touch, a tactile pretraining foundation model

    Ant Group's Lingbo has open-sourced LingBot-Touch, a tactile pretraining foundation model that uniformly encodes magnetic, visuotactile, and piezoresistive tactile signals into tactile tokens, which are then passed to the action model. Team experiments showed that when π0.5 was directly fed raw tactile readings on a zipper-pulling task, its success rate dropped from 39% to 15%; after connecting LingBot-Touch, it rose to 59%.

    Why it matters: The original provides a controlled experiment showing that feeding raw tactile signals into the action model instead lowers the success rate, along with the improvement after unifying tactile tokens, which can serve as a reference for tactile representation design.

  17. 量子位80

    Ant Group's Lingbo open-sources LingBot-Touch tactile foundation model

    On November 10, Ant Group's Lingbo Technology open-sourced LingBot-Touch, a tactile foundation model pre-trained on nearly 5,000 hours of real tactile data. It covers three sensing principles—magnetic tactile, visuotactile, and piezoresistive—and outputs unified tactile tokens for use by different dexterous hands and action models.

    Why it matters: The original text provides a unified representation across three types of tactile sensing and data from 26 real-machine tests, which can be used to assess the gains a tactile foundation model brings to existing action models.

  18. OFweek Robotics40

    Robots enter factories to "work," but why do bosses only dare let them "trial"?

    At the China International Import Expo, robots have entered scenarios such as drug picking, depalletizing, sorting, handling, assembly, and teaching-free welding, but most are still in the "trial" stage, with only some scenarios deployed at scale. Apple HALO completed 360 single-task tests in 6 real environments without pre-built maps, with an average success rate of 91%; after GPT-6Astra was connected to different robot bodies, some tasks took from one or two minutes to more than 30 minutes or even an hour. Industry observers judge that the "ChatGPT moment" for embodied intelligence may arrive in the second half of 2027, and the turning point may appear separately by scenario such as retail pharmacies and industrial sorting.

  19. The Robot Report88

    NVIDIA launches GR00T 2 world action model and Isaac Lab 3.0 at CoRL

    NVIDIA launched the open world action model GR00T 2 and the open-source robot learning framework Isaac Lab 3.0 at CoRL. GR00T 2 was trained on 100,000 hours of NVIDIA open-source first-person data; with only one demonstration per task, it reached an average success rate of 71% across 50 tasks, rising to 95% with five demonstrations, and without task-specific demonstrations it completed at least once each of 40 of the 50 tasks.

    Why it matters: GR00T 2's few-shot success rates and Isaac Lab 3.0's training throughput gains provide the current baseline for embodied models and the simulation training stack.

  20. Robotics & Automation News72

    Nature Will releases 50-billion-parameter dexterous hand model Natura-1

    Nature Will has released Natura-1, a 50-billion-parameter physical general foundation model for dexterous hand control and zero-shot instruction following. The model is based on a mixture-of-experts architecture, jointly trained on language, images, video, and actions, pre-trained on more than 1 million hours of first-person human data, and runs with a 100-millisecond control loop. The company says it can autonomously execute sustained workflows and handle spatial constraints, multi-object relationships, and negation conditions.

  21. 达纳灵动 · X58

    Dyna Robotics unboxes Taku live at CoRL, powered by Dyna-2.1 for zero-shot long-horizon whole-body mobile manipulation

    Dyna Robotics unboxed Taku on-site at the CoRL conference and put it straight to work. Driven by Dyna-2.1, the robot completed long-horizon whole-body mobile manipulation zero-shot, with no on-site data collection or post-training. The team demonstrated it live at Booth 500 in Griffin Hall.

Nov 9Mon
  1. Union Square Ventures · X58

    Tutor launches TRACE deployment-time compute scaling method and releases TI-0.1 model

    Tutor launches TRACE, which uses deployment-time compute scaling for robot learning: starting from a small number of Tutor demonstrations, a frontier large language model agent reconstructs the task in simulation and trains reliable, human-speed policies. TI-0.1 is the first model trained with TRACE. USV believes this further shows that deploying robots in the field accelerates learning, with each deployment generating training data, giving Tutor a new way to make robot learning continuously accumulate.

  2. Tech Xplore Robotics78

    UC Berkeley and NVIDIA propose agentic robotics to generate and improve robot programs in simulation

    Ken Goldberg's team at UC Berkeley and Spencer Huang, NVIDIA's head of robotics engineering, proposed an "agentic robotics" method that lets AI agents generate, test, and correct robot control programs in simulation without additional data collection.

    Why it matters: The report describes a method for using multi-agent systems to generate, test, and correct robot programs in simulation, and presents real-world data from warehouse sorting.

  3. Guyuehome50

    GlanceWAM in practice: a source code walkthrough of visual imagination, Goal Attention, and Flow Matching training

    A source code walkthrough of GlanceWAM examines its visual imagination, Goal Attention, time conditioning, Flow Matching training, and asynchronous inference mechanisms. The approach reduces WA through low-frequency future frame generation and high-frequency action prediction (the original text is truncated here and does not give the complete metric).

  6. arXiv cs.RO57

    Spectral Alignment of Forward-Backward Representations: Mitigating Spectral Mismatch via Temporal Abstraction

    The paper analyzes the spectral mismatch between the high-rank transition dynamics of continuous environments and the low-rank bottleneck in forward-backward (FB) representations, and proposes temporal abstraction to mitigate this problem. Starting from the spectral properties of the transition operator, the authors show that temporal abstraction acts like a low-pass filter: it suppresses high-frequency components and reduces the effective rank of the successor representation, while preserving a formal bound on the value function error. Experiments show that this alignment is a key factor for stable FB learning at high discount factors. The work has been accepted to NeurIPS 2026.

  7. arXiv cs.RO60

    LeWAM: Robot planning method based on JEPA world action model and diffusion-guided MPC

    The authors propose LeWAM, a bidirectional Transformer trained end-to-end on a decoder-free JEPA latent space that supports four modes: forward, backward, inverse dynamics, and policy prediction. Linear probe experiments show that LeWAM's latent space reads robot and object state better than the forward-only Le World Model, and that its degree of ignoring visual distractors is comparable to LeWM and clearly better than reconstruction-based WAM.

  8. arXiv cs.RO62

    VOMMI: A Portable Demonstration Collection and Learning Framework for Mobile Manipulation

    VOMMI is a portable demonstration collection and learning framework that connects portable RGB demonstrations to VLA post-training through offline trajectory reconstruction and online visual-motion conditioning. R2-VO refines trajectories using sparse geometric anchors, reducing the average absolute trajectory error of body and hand streams by 24.6%; a policy post-trained only on portable demonstrations has an 18.2% lower base velocity error than a policy trained on robot-collected demonstrations, with comparable end-effector translation accuracy.

  9. arXiv cs.RO60

    Geometric Prior Pretraining Improves Manipulation Imitation Learning Efficiency

    The study constructs a geometric visual pretraining dataset containing only a plane, an object, and a cube hand, with trajectories generated automatically and scenes without textures or backgrounds, so that the policy is first exposed to the geometric relationship between the hand and the object. The authors evaluate ACT on three simulated robots with five manipulation tasks each and on three real-world robot tasks; in most combinations, fine-tuning from the geometric prior yields higher success rates in early training than training from scratch, and requires only a small number of task demonstrations. The results indicate that highly simplified geometric scenes can also provide an initialization that transfers across robots and to real-world tasks.

  10. arXiv cs.RO58

    MATE: Asynchronous Multi-Rate Diffusion Policy Composition Framework

    The authors propose LAG-Fusion, a latency-aware guidance fusion framework for asynchronous multimodal diffusion policy composition that allows modality-specific policies to run at their own inference rates and provide denoising guidance whenever output is available. To keep asynchronous composition consistent, the authors derive a reference-frame rebasing rule for diffusion variables under relative action representations, so that delayed guidance is aligned before fusion. In contact-rich manipulation, the authors compose a low-frequency vision policy with a high-frequency force policy; experiments with heterogeneous modality latency show better responsiveness and task performance than synchronous fusion and a purpose-designed force-aware baseline.

  12. arXiv cs.RO62

    NoContactNoWorries: Estimating contact via vision and proprioception to support in-hand dexterous manipulation

    NoContactNoWorries proposes a Transformer-based multimodal framework that fuses RGB-D vision with robot proprioception to infer binary contact states for hand-object interaction as a pseudo-tactile signal. The authors train a single contact prediction model on multiple objects and verify that this inferred signal can support downstream reinforcement learning agents in completing in-hand object reorientation, and that it generalizes to new objects. Both simulation and real-robot experiments validate the feasibility of inferring contact from vision and proprioception. The paper has been accepted to IROS 2026.

  13. arXiv cs.RO67

    DexTeleop-0: A Force-Aware Bimanual Dexterous Teleoperation Framework for Shared Autonomy

    The authors propose DexTeleop-0, a bimanual dexterous teleoperation framework that uses fingertip tactile feedback to overlay robot-side contact corrections on human tracking commands, mitigating kinematic mapping errors and the inability of vision alone to regulate contact force. The method computes localized corrections in real time via contact-stiffness approximation and the kinematic Jacobian, and adds per-contact force tracking, soft regularization of predicted contact moments, and bounded hand-joint residuals.