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Only First party#Grasping and manipulation
  1. 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.

  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.

  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. 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. 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.

  1. Yaskawa70

    Yaskawa Electric and GAZOO Racing to Bring AI Autonomous Welding Robot Technology to GR KART Manufacturing Site

    Yaskawa Electric and Toyota GAZOO Racing have jointly developed AI-driven autonomous robot production technology, with deployment beginning in September 2026 at the Gamagori GR KART workshop. The technology builds on the SFA method (Sequence Freezing Arc Welding) jointly developed by the two parties in 2024, and uses MOTOMAN NEXT robots equipped with GPUs and AI to autonomously recognize the state of pipe frames, complete welding, confirm and correct quality, and analyze appearance images, welding sounds, and various data in an integrated manner.

    Why it matters: Yaskawa and GAZOO Racing are extending their SFA welding experience into an AI autonomous welding line, and it has already gone into actual production at the GR KART workshop, offering a view of how welding automation moves from validation to the mass-production floor.

  1. 珞石机器人70

    ROKAE Zhuxing H wheeled humanoid robots enter Schaeffler Taicang embodied intelligence digital factory production line

    On October 20, Schaeffler's Taicang embodied intelligence robot digital factory opened, with 5 ROKAE Zhuxing H series wheeled humanoid robots participating in the intelligent assembly line for tasks including bin handling, parts sorting, press-fitting and screw assembly, functional testing, marking and packaging, producing joint module components including actuators for next-generation humanoid robots.

    Why it matters: The original text provides the specific processes and technical configuration of complete machines entering a mass-production line, helping readers judge the current usable boundaries of wheeled humanoid robots in real manufacturing scenarios.

  2. Kinetix AI · YouTube62

    KAI Halo case study EgoWild2Dex: training bimanual dexterous manipulation with first-person data

    The KAI Halo case study EgoWild2Dex shows how high-quality bimanual dexterous manipulation capabilities can be derived from highly challenging egocentric data. The project page is https://mmlab.hk/egowild2dex.

    Why it matters: The material presents a complete training path from first-person human data to robotic bimanual manipulation, along with success rates; its transferable aspect lies in its approach to data utilization.

  1. Agile Robots/思灵机器人62

    Agile Robots and Audi pilot mobile humanoid robot parts sorting at Ingolstadt plant

    Agile Robots, Audi, Dassault Systèmes and NVIDIA announced a proof-of-concept collaboration at NVIDIA GTC Berlin to use mobile humanoid robots for parts picking and sequencing at Audi's Ingolstadt plant for final vehicle assembly.

    Why it matters: The original text explains the validation process in which a virtual twin precedes physical deployment, letting readers understand the practical path for humanoid robots entering automotive logistics.

  1. CMU Robotics Institute68

    CMU research team develops human-inspired multisensory fingertips for DeltaHands robotic hand

    CMU researchers designed human-hand-inspired fingertips that can be mounted on the DeltaHands flexible robotic hand. They combine strain gauges to measure pinch pressure with a microphone that captures micro-vibrations through a plastic fingernail, enabling the hand to count paper cups, identify materials, and grasp fragile items such as tofu cubes and potato chips. Each fingertip costs less than 100 US dollars, compared with about 800 US dollars to build a similar setup from off-the-shelf products. The work was presented at the 2026 Intelligent Robots and Systems Conference in Pittsburgh.

    Why it matters: The original text provides details on the multimodal tactile design of the human-inspired fingertips and their cost of less than 100 US dollars, helping readers understand how force and vibration sensing support manipulation control.

  1. Sanctuary AI78

    Sanctuary AI releases TIGER method to accelerate robot reinforcement learning with imitation policies as dense rewards

    Sanctuary AI proposes TIGER (Task-Space Imitation Guidance for Efficient Reinforcement Learning), which does not execute the imitation policy but instead maps its action chunks through the controller into end-effector reference trajectories, converting them into dense rewards for reinforcement learning.

    Why it matters: The original text provides the specific approach for converting imitation policies into dense rewards along with ablation data, allowing readers to judge whether it can be applied to their own sparse-reward manipulation training.

  1. Boston Dynamics67

    Boston Dynamics unveils dexterous hand for next-gen Atlas robot: 13-DOF four-finger design

    Boston Dynamics has unveiled a dexterous hand for its next-generation Atlas humanoid robot, increasing from GR2's 7 degrees of freedom to 13, using a four-finger design with a 4-DOF opposable thumb, direct drive, and only a single actuator type. The design drops the pinky finger to reduce cost and complexity, is equipped with fingertip and palm pressure tactile sensors, supports pinch grasping, three-finger grasping, and pressing tool triggers, and was optimized for high-fidelity simulation for sim-to-real transfer and reinforcement learning, with preliminary sim-to-real results on dynamic tasks already available.

    Why it matters: The original article details the degrees of freedom, actuation, and simulation design tradeoffs of the next-generation Atlas dexterous hand, letting readers understand how the hand design serves reinforcement learning and real-world work.

  1. MagicLab/魔法原子70

    MagicLab analyzes Magic-Brain's in-context visual guidance design; magicVLA grasp success rate rises from 70% to 94.4%

    MagicLab introduces the visual guidance approach in the Magic-Brain framework: a VLM Agent outputs a bbox, and a segmentation model refines it into a three-color mask overlaid on the observation image, converting semantic guidance into visual guidance.

    Why it matters: MagicLab details the design motivation and implementation of Magic-Brain's visual guidance layer, letting readers understand the impact on grasp success rate of converting language instructions into pixel guidance.

  1. Figure AI79

    Figure AI releases Helix 2.5, achieving zero-shot full-body autonomy in 30 unfamiliar homes

    Figure AI released Helix 2.5, which completed three long-horizon full-body behaviors—tidying a living room, folding towels, and making a bed—zero-shot in 30 Bay Area homes where no data had ever been collected, without collecting data or fine-tuning in the evaluation environments.

    Why it matters: The original article provides controlled comparison data on Index pretraining for zero-shot generalization and evaluation details, helping readers understand the actual level of humanoid robot pretraining transfer.

  1. PKU EPIC Lab64

    PKU EPIC Lab proposes UniPart for zero-shot language-driven 3D part segmentation

    PKU EPIC Lab proposes UniPart, a feed-forward cross-modal 3D Transformer conditioned on CLIP text embeddings that uses free-text phrases to select functional parts on point clouds, achieving zero-shot language-driven 3D part segmentation.

    Why it matters: The paper presents a text-driven 3D part segmentation method and a self-built dataset, and validates transfer to real-world language-conditioned grasping.

  2. PKU EPIC Lab73

    FoldNet++: A Large-Scale Synthetic Dataset for Robotic T-Shirt Folding and Unfolding

    PKU EPIC Lab has released FoldNet++, a large-scale synthetic dataset for robotic T-shirt folding and unfolding, covering 6 robotic embodiments, 1K T-shirts, 1K environmental assets, and 120K richly annotated demonstrations.

    Why it matters: The source gives the dataset scale and the real-world deployment success rate, so readers can assess the feasibility of training garment manipulation policies purely on synthetic data.

  1. MIT CSAIL61

    MIT CSAIL and others launch InstructMesh tool to repair AI-generated 3D models and manufacture them on demand

    MIT CSAIL, together with Google and Northeastern researchers, launched InstructMesh, which combines TRELLIS with GPT-4 to let users repair AI-generated 3D models using natural language before 3D printing them.

    Why it matters: The research presents a new tool and user experiment data, letting readers learn how ordinary users can repair AI-generated 3D models and complete printing.

  1. PKU EPIC Lab68

    GIF: An agentic generation framework for interactive and functional object compositions for robot learning

    PKU EPIC Lab proposes GIF, an agentic generation framework for robot learning that reframes the problem as disentangled reconstruction followed by relative pose recovery. CoGen combines 2D and 3D generative models to produce instance-disentangled meshes and coarse initial poses; GPRM refines the relative poses under joint geometric and physical guidance; and a VLM verifier selects the candidate that best matches the structured specification.

    Why it matters: The paper splits interactive and functional object composition generation into disentangled reconstruction and relative pose recovery, and provides a benchmark of eight contact-geometry classes along with collision-rate figures, which can help readers assess this route to simulation data generation.

  1. PKU EPIC Lab79

    ZETA: A Controlled Study of Zero-Shot Cross-Embodiment VLA Transfer for Tabletop Manipulation

    PKU EPIC Lab published the ZETA study on arXiv (arXiv:2609.02546), proposing a unified definition of zero-shot cross-embodiment VLA transfer and a controlled benchmark covering 14 held-out target embodiments, distinguishing strict zero-shot transfer from pretrain-exposed transfer. Experiments show that local end-effector state-action representations, source embodiment diversity, and auxiliary co-training improve transfer by about 15, 18, and 7 percentage points, respectively, and that adding only 5% target-embodiment data during pretraining raises average target-embodiment progress by 13.4 percentage points.

    Why it matters: The paper provides a unified definition of zero-shot cross-embodiment transfer and a controlled benchmark, allowing readers to understand the actual benefits of different representation and data strategies.