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#Grasping and manipulation
  1. 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.

  1. arXiv cs.RO80

    Learning-augmented robotic automation achieves cable insertion and soldering on motor production line

    The study proposes Learning-Augmented Robotic Automation, a hybrid system that integrates learned task controllers and a neural 3D safety monitor into conventional industrial workflows to automatically perform flexible cable insertion and soldering on a motor production line. Using less than 20 minutes of real-world data per task, the system ran continuously for 5 hours 10 minutes without physical fencing, producing 108 motors with a 99.4% pass rate on product-level quality inspection, a takt time close to that of human workers with smaller cycle-time variation, and solder-joint quality comparable to that of skilled operators.

    Why it matters: The paper reports data from continuous operation of learning-augmented industrial automation on a real production line for 5 hours 10 minutes, with 108 motors and a 99.4% pass rate, serving as a practical reference for learned control entering manufacturing.

  2. arXiv cs.RO80

    Paper analyzes agentic manipulation policies: AMP1 achieves 85.6% zero-shot success rate

    The paper proposes agentic manipulation policies (AMP), in which a generalist AI model directly controls a robotic arm and adjusts its actions as the task progresses. The authors' reference implementation, AMP1, runs unchanged across single-arm, bimanual, and mobile manipulation.

    Why it matters: The paper reports zero-shot success rates and real-world trial data for a generalist AI model directly controlling a robotic arm, which can be compared with specialist policies such as VLA and WAM.

  1. 至简动力 · 微信公众号72

    Zhijian Dynamics and Hangcha Group deploy robot sorting line for supermarket front warehouses

    On November 3, Zhijian Dynamics and Hangcha Group completed the deployment of the first robot sorting line for a supermarket front warehouse, connecting the full workflow from AMR merge feeding, robot autonomous planning and dexterous grasping and turning, and scan verification to order distribution. The line uses the iX robot body that Zhijian Dynamics customized for Hangcha, and relies on visual perception and VLA task planning to handle boxed, bagged, bottled, and other product forms without the need to model each SKU separately. Measured single-machine sorting efficiency reaches up to 450 items per hour, with accuracy stable above 98%, and under full load it can support 24-hour continuous fault-free operation.

    Why it matters: Readers can learn about the actual deployment conditions of the supermarket front-warehouse sorting line and the single-machine efficiency and accuracy data.

  2. Beijing Humanoid Robot Innovation Center70

    Beijing Humanoid releases Hi-WBC general whole-body control model on "Huisikaiwu" platform

    Beijing Humanoid released the Hi-WBC general whole-body control model on the "Huisikaiwu" platform on November 2. The model combines human motion references with real physical constraints, enabling robots to adjust how they execute tasks as payload, external forces, and terrain change. It uses a continual skill expansion framework based on MoE and policy distillation, Contact-aware Learning that incorporates contact force prediction, and Perceptive Control that introduces terrain awareness.

    Why it matters: The original text discloses Hi-WBC's technical approach to handling payload, external force, and terrain constraints beyond motion tracking, offering insight into how whole-body control is moving from demonstrations toward real work.

  3. HuggingFace Daily Papers83

    A2-MEAS proposes embodied multi-agent framework with code as scaling interface

    The research team proposes A2-MEAS, an embodied multi-agent framework that uses code as a scaling interface. It combines correction codes with corrective language, spatial evidence, execution history, and action proposals, while MetaBrain provides training supervision and runtime guidance.

    Why it matters: The framework uses code as a scaling interface, injects corrective language and execution history into the policy, and presents comparison data across multiple benchmarks and real-robot experiments.

  4. 机器之心 · 微信公众号80

    LiberAI releases world action model Liber-0, publishes technical report and online simulator

    LiberAI, founded by Liu Songming, has released Liber-0, a new-generation world action model that uses three expert streams for language, vision, and action along with Multimodal Attention to jointly predict future frames and actions.

    Why it matters: The technical report uses controlled experiments to break down the contributions of human data, action supervision, and embodiment diversity to dexterous manipulation, which can inform judgments about data-mix strategies.

  1. AgiBot72

    Six AGIBOT G2 robots complete 24-hour trial at Shanghai convenience store, performing 1,730 tasks

    Six AGIBOT G2 wheeled humanoid robots completed a 24-hour trial at an operating convenience store in Xuhui District, Shanghai, performing a total of 1,730 tasks, with four of them passing the final assessment.

    Why it matters: AGIBOT disclosed the task distribution and assessment results of six G2 robots over 24 hours in a real convenience store, offering a view of how embodied intelligence performs in continuous operation.

  2. HuggingFace Daily Papers78

    PyRUA-DataFarm: Using LLM agents instead of teleoperation to generate real-ready robot data

    PyRUA-DataFarm proposes having a coding agent rebuild tasks in simulation, with a video model rendering them into real-ready data, replacing teleoperation for collecting robot demonstrations. On five real-robot tasks, policies trained only on this data achieved 93/100 successes, versus 84/100 for policies trained on teleoperated data; under an unseen tablecloth and colored lighting, the former kept 87 successes while the latter fell to 46.

    Why it matters: The paper presents a complete pipeline and cost comparison for using a coding agent to replace teleoperation in collecting demonstrations, which can be used to assess the room for replacing this data-collection route.

  1. 未来不远 · 微信公众号76

    Future Not Far releases Self-Evolving WAM self-evolving world-action model

    Future Not Far released the Self-Evolving WAM self-evolving world-action model, integrating joint world-action modeling, interactive experience learning, and training-effect feedback into a single system. In update attempts from round 0 to round 30, all four groups of household tasks showed gains in autonomous full-completion success rates: folding clothes rose from 54.6% to 96.8%, hanging clothes from 46.2% to 91.2%, making pasta from 42.7% to 89.5%, and stir-fried pork with chili peppers from 38.2% to 68.9%.

    Why it matters: The original text gives the success-rate changes for four groups of household tasks from round 0 to round 30 updates, showing the practical benefits of the self-evolving training loop.

  1. PsiBot/灵初智能71

    Lingchu Intelligence and collaborators propose FAFM, a continuous action generation method for heterogeneous-frequency data, accepted to NeurIPS 2026

    Lingchu Intelligence, together with Beihang University, Peking University, and The Chinese University of Hong Kong, proposed a frequency-aware flow matching method called FAFM; the paper was accepted to the NeurIPS 2026 main conference. The method uses the discrete cosine transform (DCT-II) to represent actions as frequency-domain coefficients corresponding to real time, performs flow matching in coefficient space and constrains the first-order time derivative, requires no additional learnable parameters, and can be used independently or integrated into VLA models such as π₀ and π₀.₅.

    Why it matters: The paper presents a frequency-domain action representation that adds no learnable parameters and can be integrated into existing VLA models, offering a transferable approach for joint training on heterogeneous-frequency data.

  2. PsiBot/灵初智能71

    PKU-Spirit AI Joint Lab's RoboFFT Accepted to CoRL2026, Uses Reinforcement Learning Post-Training to Generate Robotic Policies

    RoboFFT, proposed by the PKU-Spirit AI Joint Lab, has been accepted to CoRL2026. It is a reinforcement learning post-training method for generative robotic policies that reuses the forward noising process from diffusion model and flow model pretraining to construct an approximate probability ratio for PPO-style clipped updates.

    Why it matters: The method reuses the forward noising loss of diffusion and flow models to construct policy update signals, offering a reusable approach for post-training generative policies.

  1. arXiv cs.RO80

    TacClaw-Infinity: A Large-Scale Vision-Tactile-Language-Action Dataset for Dexterous Manipulation

    The research team released TacClaw-Infinity, a large-scale structured Vision-Tactile-Language-Action (VTLA) dataset for contact-rich manipulation, spanning over 10,000 hours, 1 million episodes, and 1 billion frames across more than 1,000 task types.

    Why it matters: This dataset provides a collection interface and quality control pipeline for ten-thousand-hour-scale tactile manipulation data, and its cross-embodiment transfer validation approach can serve as a reference.

  2. arXiv cs.RO77

    From Teleoperation to Long-Horizon Dexterity: Evaluating Visuomotor Skills and Offline Proxies for Dexterous Manipulation

    The research team used a five-fingered robotic hand to grasp and insert RJ45 connectors, evaluating 281 imitation-learning policies spanning seven policy families in approximately 1,700 real-robot trials. Controlled ablations showed that observation and action representations had a greater impact than conventional optimizer and sampling choices. For point-cloud policies, region-of-interest cropping, representation in the robot-base frame, and normalization within the point encoder each improved success by 70 to 100 percentage points. For the vision-language-action policy, switching to absolute action prediction improved insertion success by up to 70 percentage points.

    Why it matters: Approximately 1,700 real-robot trials compared 281 imitation-learning policies, indicating that observation and action representations determine real-world success more than optimizer and sampling choices.

  1. Leiphone Robotics78

    Columbia's Yunzhu Li team presents FlexiTac open-source tactile sensor progress at IROS 2026

    At IROS 2026, Columbia University's Yunzhu Li team presented progress on FlexiTac, an open-source flexible piezoresistive tactile sensor platform, a year and a half after its open-source release. The sensor can be assembled in 3 minutes, bent and cut, and wrapped around gloves, fingertips, or robot skin. The team has integrated it into LeRobot and modified the UMI handheld device to add tactile recording, enabling synchronized visual-tactile data collection and policy pretraining. ADI is advancing a commercial version with tactile resolution five times that of a human finger, manufactured at scale using semiconductor fabrication processes.

    Why it matters: From sensor design and simulation calibration to UMI data collection, it presents a complete technical path toward scaling tactile perception.

  2. HuggingFace Daily Papers80

    RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory

    RoboArchitect lets a coding model observe robot rollouts, diagnose failures, and iteratively revise the agent harness around a frozen policy. Without updating any model weights, it raises average success from 9.7% to 43.3% (4.5x) on unseen scenes and from 12.7% to 40.2% (3.2x) on unseen tasks.

    Why it matters: It automatically evolves control code and persistent memory outside a frozen policy, offering a transferable approach to improving manipulation success without finetuning weights.

  3. CMU Robotics Institute62

    CMU proposes Wiggle and Go: estimating physical properties of ropes by wiggling to plan manipulation

    A team at CMU's Robotics Institute has proposed the Wiggle and Go system, in which a robot first briefly wiggles a rope or cable while an overhead camera records how the waves propagate; a computer vision algorithm then tracks the motion and estimates the object's physical properties, which are used to plan subsequent actions without repeated trial and error. The model is trained entirely in simulation, and properties are then estimated from the motion of real ropes being wiggled to improve prediction accuracy on real hardware. The research has been accepted to the 2026 Conference on Robot Learning (CoRL) and will be presented next month in Austin; it is partly funded by the U.S. National Science Foundation.

    Why it matters: The method estimates a rope's physical properties from a single wiggle and then plans actions accordingly, offering a transferable approach to deformable object manipulation.

  1. 机器之心 · 微信公众号82

    X-Era AI opensources 4D world action model E0.5, real-robot success rate surpasses π0.5

    X-Era AI has open-sourced its native 4D world action model E0.5, achieving overall average success rates of 85.0% and 88.3% on the AgileX Piper dual-arm robot and the Zerith H1 humanoid robot, respectively, 13.3 and 26.7 percentage points higher than π0.5.

    Why it matters: The original text provides real-robot success rate comparisons between E0.5 and π0.5 on dual-arm and humanoid platforms, which can be used to assess the manipulation generalization performance of the 3D geometric backbone approach.

  2. 戴盟机器人72

    Daimon Robotics completes hundreds of millions in strategic financing, led by Ant Group

    Daimon Robotics announced the completion of a strategic funding round of hundreds of millions, led by Ant Group with existing shareholders oversubscribing, just two months after its hundred-million-yuan Series A. The company also launched its tactile-anchored world model Daimon-TWM, with parameter scale at the 10B level, capable of real-time inference on the NVIDIA RTX 5090 platform, and claims that its average success rate on contact-intensive manipulation tasks is twice that of π0.5. Daimon has also built the tactile full-modality dataset Daimon-Infinity; after the first 10,000 hours of data were open-sourced, it received nearly 5 million downloads on the ModelScope community.

    Why it matters: Beyond the funding news, the original article also details the architecture and data-collection layout of the tactile world model Daimon-TWM, which helps in understanding the technical proposition of the tactile approach.

  3. HuggingFace Daily Papers79

    CaPtain: A Post-Training Pipeline for Code-as-Policy Robot Agents

    CaPtain proposes an open post-training pipeline that trains small open vision-language models such as Qwen3.5-4B into code-as-policy (CaP) robot agents that do not rely on VLAs, frontier models, or privileged state at test time.

    Why it matters: The work proposes using a physical critic to assign credit across multi-turn code policies and presents a complete training procedure for small models on LIBERO and real robots.