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#Grasping and manipulation
Oct 28Wed
  1. arXiv cs.RO70

    From Grasping to Dexterous Manipulation: Large-Scale Grasp Pretraining for Dexterous Manipulation

    The research team proposes pretraining the low-level controller of a hierarchical imitation learning framework with large-scale dexterous grasp data, then fine-tuning it on downstream task demonstrations to support functional dexterity in articulated tool use. They built a 355k-trajectory grasp-pretraining dataset from large-scale dexterous grasp annotations and introduced DexCraft, a simulation benchmark with six articulated tool-use tasks. In simulation and real-world experiments, the method outperforms end-to-end diffusion policy baselines and hierarchical policies trained from scratch, improving real-world full-task success by 33.3 percentage points over DP3.

  2. arXiv cs.RO78

    FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors

    The research team proposes FTP-1, a generalist foundation tactile policy pretrained across multiple tactile sensors and embodiments. It uses heterogeneous encoders to map image-, array-, and state-based tactile signals into unified morphology-aware latent tokens, which are jointly modeled by a shared tactile Transformer expert.

    Why it matters: This work unifies multiple tactile signals into a shared policy and reports changes in success rates for cross-sensor transfer, offering a reference for tactile manipulation research.

  3. arXiv cs.RO70

    FAWAM: Force-Aware World Action Model for Closed-Loop Contact-Rich Manipulation

    The authors propose FAWAM, a force-aware world action model that incorporates force information at three levels: perception, prediction, and closed-loop execution. It first encodes historical 6-axis force/torque signals to modulate action generation, then jointly predicts future actions and end-effector wrenches to explicitly model contact evolution, and uses the predicted wrench trajectory as an execution-time reference while correcting actions online with real-time force feedback. In real-world experiments across multiple contact-rich tasks, FAWAM achieved an average success rate 36.25% higher than vision-only baselines and 21.25% higher than existing force-aware baselines.

  4. arXiv cs.RO64

    GTP-FA: A Two-Stage Grasp-Then-Plan Manipulation Framework with Failure Attribution

    The authors propose GTP-FA, a two-stage "grasp-then-plan" framework for robotic manipulation: it first generates grasp candidates, then performs downstream motion planning conditioned on the selected grasp. The framework learns a failure attribution model that outputs a distribution over failure modes for failed trajectories, and uses it to separately optimize the grasp scoring and planning modules. Simulation and real-robot experiments show that GTP-FA improves overall task success rates over baselines including RL, IL, diffusion policies, and VLA, and the paper has been accepted to NeurIPS 2026.

  5. arXiv cs.RO71

    RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models

    RePO-VLA proposes a recovery-driven policy optimization framework that assigns distinct roles to success, recovery, and failure trajectories for long-horizon, contact-rich manipulation tasks. The method first uses recovery-aware initialization to slice corrective segments into new episodes, then learns a progress-aware semantic value function, and uses value-conditioned refinement to make the policy prefer high-progress actions.

  6. arXiv cs.RO62

    RAPID: A Robotic Agent Platform for EV Battery Pack Disassembly

    The authors propose RAPID, a robotic agent disassembly platform that integrates a gantry-mounted industrial robotic arm, RGB-D perception, and an automated nut-running tool to remove fasteners on a full-scale EV battery pack. Open-vocabulary object detection achieves 0.9757 mAP50; among three one-shot fastener removal strategies, taught-in poses achieved a 97% success rate in 24 minutes, one-shot vision execution achieved 57% in 29 minutes, and visual servoing achieved 83% in 36 minutes (n=204).

  7. arXiv cs.RO62

    Semantic-Geometric Task Representation for Bimanual Manipulation from Human Demonstrations to Robot Action Planning

    The authors propose a semantic-geometric graph task representation that jointly encodes object identities, inter-object semantic relations, and each object's motion history, consisting of an MPNN encoder and a Transformer decoder; the encoder processes only the temporal scene graph and produces structured representations decoupled from action labels.

  8. arXiv cs.RO67

    DreamTacVLA: A VLA framework that uses a tactile world model to predict future contact

    DreamTacVLA proposes a framework that grounds VLA models in contact physics, learning contact dynamics by predicting future tactile signals. The model uses hierarchical perception, taking high-resolution tactile images as micro-vision inputs combined with wrist-camera local vision and third-person macro vision, and uses a Hierarchical Spatial Alignment (HSA) loss to align tactile tokens with their spatial counterparts in the wrist and third-person views.

  9. arXiv cs.RO75

    In-hand object pose reaching strategy and safety filter for the LEAP dexterous hand

    The research team proposed a training recipe for in-hand object pose reaching with the LEAP dexterous hand. It trains in simulation without human data and, after system identification, transfers zero-shot to the real robot, using either motion capture or a single wrist-mounted monocular depth camera. In a real-robot evaluation with 4 objects and 4,800 unseen targets over 13.3 hours, the state-based policy reached 95.2% of targets within 2 cm and 0.4 rad; adding a learned residual safety filter with adjustable conservativeness reduced drops by 75% while still reaching 91.5% of targets.

  10. arXiv cs.RO63

    EgoThumb: A Vision-Driven Supernumerary Robotic Thumb for Autonomous Grasping and Holding Assistance

    EgoThumb is a fully open-source, vision-driven supernumerary robotic thumb that uses a visual imitation learning policy to map first-person images from a palm-facing camera directly to continuous joint positions, enabling autonomous grasping and holding assistance. Its compact dual tendon-driven finger relocates the actuation unit to the forearm to reduce distal weight and support precise active joint control. The authors state that the design generalizes across different users and environments, that both hardware and software are open-sourced, and that the paper has been accepted to IROS.

  11. arXiv cs.RO58

    CRS: A Joint-Constraint-Referenced Evaluation Method for Articulated Object Manipulation

    The authors propose Constraint-Referenced Strategy (CRS), which projects the end-effector twist commanded by a policy onto the object's joint axis, separating progress along the joint direction from off-constraint waste. On 74 simulated articulated objects, binary success rate only shows a lower transfer failure rate without attribution, whereas the two CRS channels give each a distinct signature.

  12. arXiv cs.RO65

    ARC: Using Human Corrections to Guide Residual Correction of Robot Policies

    The paper proposes Adaptive Residual Correction (ARC), which learns residual corrections via reinforcement learning on top of a frozen imitation policy, using sparse human corrections to decide when to activate residual control and in which direction of the residual action space to explore. On three precision manipulation tasks with a real robot, ARC raises average task success from 33.3% to 75.0%; the human corrections define 2-3 dimensional task-relevant subspaces of the seven-joint residual space, retaining 96.0-97.7% of the squared correction magnitude.

  13. arXiv cs.RO60

    StabBot: Robotic Pipe Stabbing with Learned Stereo Vision and Residual Reinforcement Learning

    The StabBot robotic stabbing system uses a pendulum Kalman filter to track pipe joints in stereo images, approaches at matched speed, and confirms the final few millimeters by touch. In 200 randomized jobs, StabBot completed every stab, versus 74% for a conventional fixed guide; median contact speed fell from 18.4 cm/s to 2.3 cm/s, and median peak head-pipe force fell from 11.3 kN to 1.4 kN.

  14. arXiv cs.RO55

    Which Hand Was Late? Diagnosis for Robot Failure Recovery in Bimanual Tool Use

    The study measures, on a simulated bimanual tool-use task, which failure causes each sensing channel lets a diagnoser identify: a tool slipping out of its socket can be read on the tool-hand side; which hand was late requires information from both arms, while a sensor on one hand can only read that hand's own delay and part of the other hand's delay; and when the tool hand is late, passive sensing cannot determine whether the push force was also insufficient.

  15. arXiv cs.RO62

    QCP: Learning Quality-Controllable Robot Policies with Iterative Hindsight Relabeling

    The authors propose Quality-Controllable Policy (QCP), which enables inference-time control over individual quality attributes such as execution speed and motion smoothness, as well as their combinations. To extend the controllable quality range beyond the initial demonstrations, the authors design an iterative hindsight relabeling loop: collect online rollouts under target quality labels, add successful trajectories to the dataset, then relabel the accumulated trajectories in hindsight according to their measured quality and fine-tune the policy.

  16. arXiv cs.RO58

    Tokens of Motion: Primitive Motion Learning for Human-Humanoid Skill Transfer

    The paper proposes the Adaptive Failure-Informed Learning (AFIL) framework, which uses a pretrained VLA to generate failure trajectories online as adaptive negative guidance for diffusion- and flow-based VLA policies, avoiding the need for handcrafted failure modes or human intervention for recovery. The method jointly trains two action generators (DAGs) for success and failure on a shared vision-language backbone, and at sampling time adjusts the guidance strength according to the distance between the success and failure distributions at each diffusion step. On in-domain and out-of-domain, short-horizon and long-horizon robotic manipulation tasks, AFIL improves task success rate and robustness compared with existing VLA baselines.

  17. arXiv cs.RO71

    Retrieve to Act: Improving VLA Execution Robustness with Motion Primitive Graph Retrieval

    The authors propose Retrieve to Act (R2A), a test-time scaling framework that organizes successful demonstrations into an offline spatiotemporal motion primitive graph. At inference, a query-dependent graph neural network locates the current execution state and retrieves structurally compatible future motion primitives, which are then fused with the frozen VLA's predicted actions through dimension-wise gating.

  20. arXiv cs.RO59

    Task-Adaptive Friction Margins for Contact Maintenance in Bimanual Non-Prehensile Manipulation

    The paper proposes using signed friction distance to measure friction margin in force space, and derives a task-adaptive margin from the force correction needed to balance pivot moment deviations during bimanual tilting. The method embeds contact margins into a sequence of second-order cone programs (SOCPs), each yielding a globally optimal contact force solution at a given tilt angle, keeping robot contact forces and pivot contact forces away from the nominal friction boundary. Real-world experiments were executed under variations in object properties and model mismatch, and comparisons against fixed-margin and no-margin settings show the importance of adjusting the margin to task demand.

  22. arXiv cs.RO62

    Torch-Hand: A 21-DOF Linkage-Driven Humanoid Dexterous Hand

    The research team proposed Torch-Hand, a humanoid dexterous hand featuring an all-linkage transmission architecture with 21 degrees of freedom, including 5 DOF in the thumb and 4 DOF in each of the other four fingers. The hand integrates its actuators into the palm in a serial-robot-like arrangement, so that motor motion corresponds directly to joint kinematics while balancing grasp force and payload capacity. Experiments evaluated the reachable workspace, fingertip coordinated trajectories, thumb opposition, coverage of 33 grasp categories, grasp force, payload, and task-level manipulation.

  23. arXiv cs.RO56

    Study: Effects of Non-Rigid Bases on Mobile Manipulators in Sustained Contact Tasks

    Using a sanding task as a case study, the paper combines simulation with experiments on two lightweight, unactuated wheeled testbeds to characterize non-rigid base effects in mobile manipulators. Under low base-ground resistance, only 54-70% of a 100 N command was realized, with tool-center-point errors reaching 100 mm; increasing base-ground resistance during contact improved force realization and arrested further tool displacement, while base vibration and force-tracking error showed no consistent relationship under the tested conditions. The study was conducted by Zongyuan Zhang et al. and has been accepted to ACRA 2026.

  24. arXiv cs.RO67

    TOPS: Reshaping Flow Source and Transport for Multi-Task Robot Manipulation

    TOPS proposes a task-oriented prior and scheduling method that adapts the flow-matching source prior and interpolation schedule for each task without modifying the network architecture. On LIBERO-10, single-step inference raises the success rate from 53.44% for standard flow matching with ten steps to 69.67%, with inference time about one-sixth of the baseline. The method improves performance on LIBERO, LIBERO-Plus, and CALVIN, and when transferred to the π0.5 policy, and on a real robot it raises the success rate on an upper-drawer-opening task from 20% to 80%.

  25. arXiv cs.RO60

    RoboSTD: A Bimanual Learning Method for Zero-Shot Single-to-Dual Transfer via Sagittal Mirroring

    RoboSTD is a zero-shot single-to-dual data construction method for bimanual robot learning. It applies sagittal-plane mirroring to observations, proprioception, and actions to generate executable contralateral pseudo-demonstrations, then uses an LLM to generate structured spatio-temporal coordination constraints that guide the reconstruction of pseudo-bimanual supervision across workspace regions and task stages. The study evaluates RoboSTD on the AgileX ALOHA platform in RoboTwin simulation and real-world settings. Without additional bimanual demonstrations, it achieves an average of 2.11 times the number of cups collected by the single-arm baseline in the cup-collection task.

  26. arXiv cs.RO71

    OpenJaws: An open-source hardware UMI framework for generalist manipulation, using stereo localization to improve demonstration accuracy

    OpenJaws is a fully open-source sensorized UMI hardware and software framework that pairs a modular 3D-printed gripper with a low-cost stereo camera to recover metric camera trajectories, improving action-label localization accuracy in handheld demonstrations. The authors also propose UMI-Loc-Bench-v1, a benchmark for quantifying the real-world localization accuracy of handheld data-collection devices, and show that stereo localization clearly outperforms monocular alternatives. Policies trained on data collected with OpenJaws have been deployed on real robots across multiple tasks, and the hardware, software, and data are released under a commercially friendly license.

  27. arXiv cs.RO64

    CARD: A Contact-Anchored Retargeting Framework for Dexterous Manipulation

    CARD is a simulator- and object-model-free framework that retargets human demonstrations into robot actions by explicitly preserving contact force targets, and it handles video, motion capture, and tactile glove data in a unified way. On video, glove, and motion-capture data and the GRAB dataset, CARD maintains contact on 43%–78% of contact frames, whereas purely kinematic and object-aware retargeting methods stay below 10% and 22.5%, respectively.

  28. arXiv cs.RO69

    RoboAuto: Autonomous Robot Program Search through Multi-Level Physical Understanding

    RoboAuto proposes a robot program search framework that organizes physical understanding at three levels—object, scene, and interaction—to guide LLM agents in generating and revising manipulation programs. The method achieves better performance across five benchmark suites, reaching an 89.1% success rate on short-horizon LIBERO-PRO tasks and averaging more than twice the success rate of ASPIRE on LIBERO-PRO Long and LIBERO-Plus.

  29. arXiv cs.RO62

    HIDRA: A Hierarchical Dual-Routing Attention Framework for Replay-Free Lifelong Imitation Learning

    The authors propose HIDRA, a hierarchical dual-attention routing framework for replay-free, single-pass lifelong imitation learning that decouples instruction-level expert retrieval from context-dependent refinement. The method introduces key-level geometric regularization that constrains within-task alignment and between-task separation to mitigate routing interference under representation drift. On several LIBERO benchmark suites, including variants with heterogeneous task sequences and paraphrased language instructions, HIDRA outperforms replay-free and replay baselines on AUC and forward transfer while maintaining low forgetting, and the code is publicly available.

  30. arXiv cs.RO65

    Learning Multi-Phase Robot Loco-Manipulation via Phase-Conditioned Policy Optimization

    The study proposes extending PPO into phase-conditioned policy optimization, using a phase-conditioned critic, phase-gated rewards, and a multi-phase reset curriculum to train a single policy for long-horizon loco-manipulation tasks. The method adopts a decoupled hierarchical control architecture that separates low-level balancing dynamics from high-level phase execution, and validates multi-stage skills on real hardware on the Unitree G1 and a Boston Dynamics Spot equipped with a manipulator arm, while testing design choices through ablation experiments.

  31. arXiv cs.RO67

    cuArms: A GPU-Accelerated Conflict-Based Planner for Multi-Arm Manipulation

    cuArms is a conflict-based planner co-designed with GPU parallelism. It decomposes the search space using reusable multi-modal roadmaps and resolves conflicts and deadlocks through lazy search. It integrates with a high-level scheduler to jointly optimize task assignment, scheduling, motion, and manipulation skills. Experiments report real-time initial solutions and anytime improvements in motion quality and scheduling, without domain training for learning-based methods. The authors say it provides demo videos on both simulated and real industrial tasks.

  32. OFweek Robotics58

    Do humanoid robots entering factories really need legs? LimX Dynamics mounts the TRON 2 upper body on a six-axis industrial arm

    LimX Dynamics and ZINOVA mounted the torso module of the TRON 2 dual-arm embodied robot on the end of a large six-axis industrial robotic arm, completing formwork frame assembly and rebar tying tasks at a heavy-industry construction site. The article notes that bipedal robots have a single-charge battery life of only one to two hours, with most of the power used to maintain balance, and that their small contact area makes it difficult to withstand the recoil of tools such as nail guns. In this approach, the industrial arm bears the load and absorbs impact, while the TRON 2 dual arms use vision to recognize an off-the-shelf industrial nail gun and plan gripping and trigger actions; force sensors perceive contact vibration and resistance, and two robots can work together to align lumber and fire.

  33. New Strategy Robotics40

    Damon Technology GPC 2026 Global Partner Conference preview: AI and embodied intelligence logistics solutions deployed

    Damon Technology will hold the GPC 2026 Global Partner Conference on November 6-7 at its Damon Science and Innovation headquarters in Huzhou. Live on-site demonstrations will show four warehouse workstation solutions validated through internal POC, including intelligent parcel feeding for cross-belt sorters, feeding for the Flying Shuttle intelligent sorting robot, production line-side handling, and warehouse depalletizing and palletizing handling, with humanoid robots working in coordination with sorting hosts, conveyors, and shelf pallets.

  35. 荭杏科技18

    Hongxing Technology's Dazhuang-Z6 and Two Other Robots Explore Real-World Embodied Intelligence Applications

    Hongxing Technology's industrial robot Dazhuang-Z6 can perform tasks such as box handling and item picking, exploring the practical application of embodied intelligence in industrial operations. Its guide service robot Xiaomei-M9 provides conversation, guidance, and route directions, while the desktop robot Lumi offers daily companionship through conversation and movement interaction. The three products target industrial operations, commercial services, and daily interaction scenarios, respectively.

  36. Reddit r/robotics30

    Training Franka Panda to complete pick-and-place with PPO in PyBullet

    A simulated Franka Panda learns in PyBullet to pick up blocks and place them on a target pad using PPO from Stable-Baselines3; the project aims to understand PyBullet and the full reinforcement-learning robot training pipeline. Future plans include adding text or voice prompts so users can choose which block to pick up and where to place it; the code has been released on GitHub.

Oct 27Tue
  1. 机器人全球资讯 · 微信公众号74

    AgiBot launches heavy-duty embodied robot Genie G2 Max with 40kg dual-arm rated payload

    AgiBot has launched the safety-certified heavy-duty embodied robot Genie G2 Max, targeting industrial scenarios such as depalletizing and palletizing, material transfer, and production line feeding. It has a 20kg single-arm payload, a 40kg dual-arm rated payload, a 50kg peak payload, and arm repeat positioning accuracy of ≤±0.5mm. The product is already running on multiple production lines, with a task success rate above 99% in e-commerce warehousing scenarios, depalletizing 80 boxes and palletizing 75 boxes per hour. In July this year at WAIC, JD Logistics and AgiBot jointly launched the product and announced it would be deployed in JD Logistics' ZhiLang warehouse.

  3. 机器人前瞻 · 微信公众号71

    AgiBot releases heavy-duty embodied industrial robot Genie G2 Max with 40 kg dual-arm payload

    AgiBot has released the safety-certified heavy-duty embodied industrial robot Genie G2 Max, featuring a 20 kg single-arm payload and 40 kg dual-arm payload, with dual-arm peak payload up to 50 kg and repeat positioning accuracy of ≤±0.5 mm, targeting depalletizing and palletizing, material transfer, and production line feeding.