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#Grasping and manipulation
Oct 27Tue
  1. 移动机器人产业联盟 · 微信公众号58

    Behind 14 M&A Deals: The Mobile Robot Industry Is Racing for Technology, Use Cases, and System Control Points

    According to statistics from the Mobile Robot Industry Alliance, as of October 26, 2026, 14 acquisitions or mergers had occurred in the mobile robot sector, covering key technologies, complete machines, software systems, and mature businesses. The article groups these deals into three categories: filling technology gaps, entering new use cases, and completing the warehouse operation chain, and argues that industry competition is shifting from individual product capabilities to key control points within complete systems.

  2. 甲子光年 · 微信公众号60

    Force Infinite's Chen Jiayu: General large models will eat up task planning—where should robot brains go?

    In an interview with Jiazi Guangnian, Force Infinite chief scientist Chen Jiayu judged that general large models such as GPT-6 Astra will completely eat up the task planning (Planner) portion, so the team will no longer continue investing in some high-level memory mechanism research and will instead focus on execution capabilities in the physical world.

  3. 高工咨询 · 微信公众号58

    Sightbit Robotics launches dual-robot collaborative welding workstation and Kunwu industrial simulation platform

    At the 2026 China Marine Economy Expo, Sightbit Robotics showcased a dual-robot collaborative welding workstation for shipbuilding, a humanoid welding robot prototype, an intelligent residual-material scanning system, a steel plate pitting 3D inspection system, and the Kunwu industrial simulation platform. The SpeedWeld intelligent welding control system supports reverse modeling with 3D structured-light cameras to generate welding trajectories, and can reduce changeover and commissioning cycles by up to 90%. The latest SpeedWeld 2.9 version supports dual-robot collaborative, asynchronous welding, and independent welding modes.

  4. 机器之心 · 微信公众号76

    Ropedia Releases Six Research Results, Exploring How First-Person Human Data Can Be Turned into Robot Capabilities

    Ropedia released six studies at the first Ropedia Super Week, held October 19 to 23, centered on how first-person human video can be turned into training and execution information usable by robots.

    Why it matters: The six studies collectively address how first-person human data can be converted into training and execution information usable by robots, and present cross-embodiment and real-robot comparison results.

  5. 人形大讲堂 · 微信公众号78

    CASBOT (Zhongke Huiling) completes hundreds-of-millions-yuan A++ round, deploying in mines and shoe production lines

    On October 26, Zhongke Huiling (CASBOT) announced the completion of an A++ funding round worth hundreds of millions of yuan, jointly funded by six institutions: Zhilai Investment, Yonghua Investment, Maotian Investment, Yuanhe Chenkun, Guoke Kezhi, and Luning Investment. Founder Zhang Zhengtao plans to allocate 60% of the funds to embodied brain R&D and 30% to product mass-production and delivery.

    Why it matters: Beyond the funding, the article provides measured cycle times and payback periods for shoe-making workstations as well as progress on the mine demonstration, which can be used to assess the economic model of vertical embodied-intelligence deployment.

  7. Xbotics具身智能实验室 · 微信公众号75

    Tsinghua team proposes SimpleTouch: fine manipulation learned from 50 demonstrations without tactile policy pretraining

    A Tsinghua University research team proposes SimpleTouch, which adds a tactile expert to the pretrained VLA π0.5 and uses single-stage training with only 50 demonstrations per task, without additional tactile policy pretraining or a separate alignment stage.

  8. 斯帝尔科技 · 微信公众号55

    Stial Wins Bid for Intelligent Grinding Workstation Project for Railway Equipment Company's Continuous Welded Rail

    Stial has won the bid for an intelligent grinding workstation project for a continuous welded rail at a domestic railway-specific equipment manufacturer, and will provide an AI flexible grinding solution for the core workpieces of its turnouts. The continuous welded rail workpieces present conditions including large dimensions of over 2 meters, high hardness of HRC 45–50°, and thickness deviation and warping. The solution uses the PolishX flexible force control system to sense contact status in real time, and NextBrain AI Mushroom Cloud matches grinding trajectories and force control parameters according to geometric features and performs real-time force-position compensation. Stial said the project verified its engineering delivery capability across the closed technology loop of perception, analysis, decision-making, and execution.

  9. 深圳市机器人协会 · 微信公众号25

    Anno AI Robot Latte Art Coffee Printing Bar Wins 2026 Canton Fair Design Innovation Award (CF Award) Gold Prize

    Anno Robotics' "Anno AI Robot Latte Art Coffee Printing Bar" won the Gold Prize at the 2026 Canton Fair Design Innovation Award (CF Award). A total of 2,112 companies and 4,218 products were entered, and only 222 won awards. The product uses a self-developed six-axis robotic arm and motion control system to complete the full process from grinding, extraction, and milk frothing to latte art and printing, producing a cup in 90 seconds with repeat positioning accuracy of ±0.03mm, and it connects to Alibaba's Qwen large model to support one-click AIGC image generation. The machine occupies less than 2㎡, supports 24-hour unattended operation, and has entered more than 100 cities in China and been sold to more than 80 countries and regions.

  10. 量子位58

    Xiaomi unveils 21-DoF biomimetic dexterous hand, validated on 1,827 factory SOPs

    Xiaomi has disclosed its exploration results for a biomimetic dexterous hand, designing a new 21-DoF biomimetic dexterous hand based on the collection and analysis of 1,827 standard operating procedures (SOPs) at Xiaomi's automobile factory. Simulation ablation analysis showed that in the 17–21 DoF range, the average stable grasp success rate improved by 15%–38% compared with 6–15 DoF, while the improvement slowed markedly at 23 DoF and above. The hand uses a hybrid actuation and tendon tension control scheme, with systematic optimization of the heat dissipation system and full-palm tactile sensing.

  11. 36Kr Embodied Intelligence58

    Xiaomi unveils 21-DoF biomimetic dexterous hand: validated on 1,827 factory SOPs

    The Xiaomi robotics team has disclosed its biomimetic dexterous hand technology exploration results, completing data collection and analysis validation based on 1,827 standard operating procedures (SOPs) at an automotive factory to design a new 21-DoF biomimetic dexterous hand. The hand uses a hybrid actuation and tendon tension control scheme to achieve precise grasp force control, and has undergone systematic optimization of its thermal management system and full-palm tactile sensing.

  14. arXiv cs.RO58

    Vision-Language-Action Model Robot Learning Framework for Agricultural Manipulation

    A research team proposed a simulation-to-real robot learning framework for agricultural reaching and grasping, building a MuJoCo-based agricultural environment with diverse tomato plant structures and collecting expert demonstrations via joystick teleoperation. Two pretrained generalist policies, Octo and π0.5, were first adapted with simulated demonstrations, then fine-tuned with real demonstrations on a Franka Research 3 robotic arm. Results showed that π0.5 scored higher than Octo on task performance and degraded less under distribution shifts such as viewpoint and spatial offsets; the study also examined the effect of proprioceptive information on policy performance.

  15. arXiv cs.RO63

    CLARO: Open-vocabulary grasping in cluttered scenes with a VLM agent

    CLARO is a VLM agent that orchestrates skills such as open-vocabulary object detection, segmentation, 6-DoF grasping, and non-prehensile pushing to locate and retrieve a language-specified target in cluttered environments. It records past actions to support long-horizon execution and outputs grasp poses or 3D waypoints, enabling transfer to different robot arms without task-specific training. In a dynamic simulation environment, CLARO achieved an average success rate of 89.1%, a relative improvement of over 16% on the strongest competing method in highly cluttered scenes; on a real robot arm, its success rate was 88.8%.

  16. arXiv cs.RO71

    PlayPolicy: Learning Dexterous Manipulation from Supervised Play

    The authors propose the PlayPolicy framework, in which a robot first learns a play policy from minutes of human play data through behavior cloning, then deploys it autonomously to collect hours of interaction data to train a world model; at inference time, this policy proposes candidate action sequences for model-predictive control (MPC).

  17. arXiv cs.RO62

    DiVeR: Decision-Critical Verifier Learning for VLA Test-Time Scaling

    DiVeR estimates decision criticality from the dispersion of sampled action representations and uses it to reweight verifier learning, focusing learning on states where action selection matters most, without requiring step-level annotations or additional environment interaction. In experiments on LIBERO, RoboCasa, and a Franka Research 3 real robot, DiVeR consistently improves task success rates by guiding action selection with a more effective verifier, and verifier inference overhead is negligible.

  18. arXiv cs.RO59

    EPCC: Faster-than-Demonstration Planning with Slow Expert Demonstrations and Fast Play

    The authors propose Expert-Play Contouring Control (EPCC), which combines slow expert demonstrations with fast, non-expert play to achieve execution speeds faster than the demonstrations. Expert demonstrations train a latent trajectory generator whose predictions are reparameterized into a time-independent contour of task progress, while play data trains a world model of fast-action outcomes. At deployment, the planner optimizes actions along this contour and penalizes deviation from it.

  19. arXiv cs.RO65

    ForceDelta-VLA: Distilling Force-Conditioned Action Corrections for Contact-Rich Manipulation

    ForceDelta-VLA proposes a correction-distillation framework that constructs an explicit force-correction target from the force-conditioned and force-agnostic predictions of a frozen teacher model, and adds a separate delay-correction target to handle reference-action mismatch. Across nine single-arm and bimanual contact-rich tasks, the system achieves a mean success rate of 82.2%, compared with 54.4% for the ForceVLA baseline and 70.6% for direct execution of the Stage-1 temporal teacher. Compared with ForceVLA, the full system reduces the mean peak contact force of successful trials by about 26% on both platforms.

  21. arXiv cs.RO67

    LaWAM: Efficient Dynamics-Aware Robot Policies with Latent World Action Models

    LaWAM proposes a Latent World Action Model that exposes predictive dynamics to robot policies through compact latent visual subgoals rather than reconstructed future video. Its core LaWM trains a latent action model in the latent space of a pretrained vision foundation model and reuses its forward decoder to predict future observation features.

  23. arXiv cs.RO61

    MRO-GWM: Action-Conditional and Object-Centric Gaussian Splatting World Model for Rigid Objects

    The authors propose MRO-GWM, an object-centric Gaussian world model for learning action-conditional dynamics of 3D rigid objects. The model represents arbitrary shapes and multi-object scenes with Gaussians in an object-centric frame, and uses a temporal-spatial Transformer to predict rigid body motion from past Gaussians and future actions; training is based on multi-view reconstruction, which requires handling partial observations caused by occlusion.

  24. arXiv cs.RO58

    Haptic Sorter: A Unified Planning Framework for Online Shape Estimation and Real-Time Pose Inference

    The authors propose Haptic Sorter, a unified model-based geometric framework that integrates robotic haptic perception, modeling, and manipulation planning for manipulation when object shape and pose are unknown. The framework uses Bayesian optimization to guide haptic exploration and approximates geometric boundaries with superellipses, describes quasi-static robot-object interaction with an adaptive manipulation potential that encodes object geometry, and proposes an online ordinary differential equation based on model prediction and tactile feedback for real-time pose inference. The system is deployed on a 2D robotic sorting task, varying object geometries to validate the framework's robustness and generalizability in both simulation and a real-world multi-arm setup.

  25. arXiv cs.RO58

    LLM-Guided Future Hypotheses for Horizon-Aware Exploration in Multi-Step Robot Manipulation

    The paper proposes Future-Experience Conditioning (FEC), which uses a latent representation of a short future video as a condition for closed-loop policies, for exploration and policy adaptation in multi-step robot manipulation. Future clips are generated in three stages: an LLM reasoner performs task-ontology reasoning initialized from the current scene state, a robot-free digital twin rolls out the intended object motion, and a mask-free video diffusion model synthesizes a robot-consistent future clip.

  26. arXiv cs.RO72

    RoboEvolve: A Planner-Simulator Co-Evolution Framework for Robotic Manipulation

    RoboEvolve couples a VLM planner and a VGM simulator into a mutually reinforcing co-evolutionary loop, bootstrapped from unlabeled seed images to alleviate manipulation data scarcity. Experiments show it raises the base planner by 30 absolute points and improves simulator success by 48% on average, surpassing fully supervised baselines with only 500 unlabeled seed images and reducing initial reinforcement learning data by 50 times.

  27. arXiv cs.RO60

    BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation

    BEACON proposes a theory-guided cross-domain co-training framework that trains diffusion-based visuomotor policies using abundant source demonstrations and limited target demonstrations, and formulates co-training as a discrepancy-aware importance reweighting problem. The method introduces scalable instance-level discrepancy proxies, stochastic alternating updates of policy and weights, and a multi-source extension, improving robustness and data efficiency over target-only, fixed-ratio co-training, and feature-alignment baselines in sim-to-sim, sim-to-real, and multi-source manipulation settings.

  28. arXiv cs.RO62

    Visibility-aware mobile grasping system for dynamic environments

    The authors propose a unified system that simultaneously constrains collision risk along the whole-body trajectory and target visibility during grasping in unknown and dynamic environments, comprising three parts: a velocity-aware gaze policy, a hierarchical subgoal policy, and a receding-horizon whole-body planner. In 400 randomized simulation scenarios and real-world trials on a Fetch mobile manipulator, the system achieved success rates of 70.4% in unknown static environments and 57.9% in dynamic environments, 16.3 and 9.3 percentage points higher than decoupled navigation and manipulation policies, respectively, while significantly reducing collisions.

  29. arXiv cs.RO67

    AffordSim: A Scalable Data Generator and Benchmark for Affordance-Aware Robotic Manipulation

    AffordSim is a scalable data generator and benchmark that integrates open-vocabulary 3D affordance prediction into simulation trajectory generation. Given a natural-language task description, it synthesizes scenes, generates affordance queries, and samples region-constrained grasps, then uses motion planning to filter executable candidates.

  30. arXiv cs.RO70

    FlowHOI: Flow-Matching-Based Semantics-Grounded Generation of Hand-Object Interaction Sequences for Dexterous Manipulation

    FlowHOI is a two-stage flow-matching framework that generates semantically grounded hand-object interaction sequences, including hand poses, object poses, and contact states, conditioned on first-person observations, language instructions, and 3D Gaussian splatting scene reconstructions.

  31. arXiv cs.RO71

    UniManip: An Open-World Robotic Manipulation Framework Based on Agentic Operational Graphs

    UniManip proposes a unified framework centered on the Agentic Operational Graph (AOG) for task orchestration, state tracking, execution verification, and closed-loop failure recovery in open-world robotic manipulation. The method grounds high-level semantic intent into executable primitives through a physically grounded scene graph, and combines a safety-aware local planner to generate collision-free trajectories. It achieves state-of-the-art performance on LIBERO-Pro, and in real-world experiments its task success rate is 22.5% and 25.0% higher than leading VLA models and hierarchical baselines, respectively.

  32. arXiv cs.RO57

    Collaborative Continuum Robots: A Survey Constructing a Closed-Loop Task Architecture and Three Collaboration Modes from an Automation-System Perspective

    This survey constructs a closed-loop task-execution architecture for collaborative continuum robots (CCRs) from an automation-system perspective, explaining how target-task specifications are transformed into coordinated execution by multiple continuum robots. Based on this, the authors classify CCRs into three modes: separated collaboration, assistance collaboration, and parallel collaboration. They then review research progress in terms of multi-robot task allocation, structural design, modeling, motion planning, control strategies, and sensing mechanisms, and finally discuss key technical opportunities and the system-integration challenges of real-world deployment.

  33. arXiv cs.RO58

    CRAFT-E: An Interpretable and Editable Neuro-Symbolic Framework for Affordance-Grounded Object Retrieval

    CRAFT-E is a modular neuro-symbolic framework that combines an LLM-derived verb-property-object knowledge graph with CLIP-based visual alignment and energy-based grasp reasoning for affordance-grounded object retrieval. The system provides interpretable grounding paths, incorporates grasp feasibility into affordance inference, and supports graph edits without retraining.

  34. arXiv cs.RO53

    Study proposes virtual nonholonomic constraints for nonprehensile robotic catching

    The paper proposes using virtual nonholonomic constraints (VNHCs) to address the problem of nonprehensile robotic catching, designing and controlling the motion of the end-effector directly based on the object's state before it is caught, without the need to predict the object's trajectory and generate a time-varying robot trajectory. In the planar case, the method can catch the object at a desired height and minimize the relative normal velocity at the instant of contact; the same VNHC function depends only on the target catching height and can cover a range of initial conditions. After contact, model predictive control (MPC) is used to bring the object and end-effector to rest at a specified position. The approach has so far been validated in simulation.

  35. arXiv cs.RO42

    Nonlinear Control of Aerial Manipulator Robots with a Quadrotor and a Two-DOF Robotic Arm

    For a quadrotor equipped with a two-DOF robotic arm that can physically interact with its environment, this study implemented and compared two controllers: Cartesian impedance control and adaptive sliding mode control. Under bounded disturbances, both controllers performed a simple hovering task with the same initial conditions and target position, and were validated based on a full dynamics model.

  37. arXiv cs.RO72

    FLORE: Learning Force-Reactive Humanoid Loco-Manipulation

    The research team proposes the FLORE framework, which uses two force-sensing UMI-style handheld grippers and wearable trackers to collect robot-free demonstrations, and trains a force-reactive diffusion policy that takes vision, proprioception, and measured force wrenches as input and outputs actions and per-hand force commands. A companion hybrid compliant whole-body controller uses integral force error from real force/torque sensors to achieve single-hand force tracking while maintaining balance. In real-robot tests on three contact-rich tasks, the force-reactive policy achieved an 80% success rate, compared with 26.7% for a position-only policy under the same controller.

  40. arXiv cs.RO67

    3D Gaussian Splatting-Based Precise Reaching for Wrist-Camera VLA

    This work proposes a 3D Gaussian Splatting (3DGS)-based data generation pipeline for adapting wrist-camera VLA policies to fine reaching tasks. The method reconstructs the real workspace and renders wrist-camera observations from robot-aligned poses, pairing them with smooth, structured reaching trajectories for VLA fine-tuning, and adds an instruction-conditioned reach verifier that judges termination before physical contact.