The main hypothesis was that a lightweight task-specific diffusion policy, explicitly grounded by a visual bounding-box prompt, could outperform text-only VLA prompting for fine-grained target selection, and that offline RL could further improve robustness beyond imitation learning (IL).
Open-vocabulary grasping on a quadruped manipulator requires more than recognizing the target object. The robot must also select a grasp pose that is both consistent with the task semantics and reliable to execute under body motion and viewpoint changes. In this paper, we present VLEG, an embodied vision-language grasping framework for quadruped manipulators that explicitly incorporates body motion into grasp decision making. Our method guides the robot to continuously adjust its body pose during approach and optimize local observations before grasping, thereby improving perception quality. For grasp decision making, instead of using a coarse single-stage filtering strategy, we design a multi-stage and multi-criteria grasp selection mechanism based on geometric grasp candidates. This mechanism jointly considers physical feasibility and task consistency. We implement the complete system on an onboard Jetson platform and conduct extensive real-world experiments on a quadruped robot equipped with a manipulator, covering tabletop, low-platform, ground-level, and outdoor raised-platform scenes. The results validate the deployability of VLEG in real-world quadruped manipulation scenarios, as well as its robust grasping ability and task-aware decision-making capability across the tested object categories.
Yu-Xing Ji, Fei Meng, Zishang Ji et al.· Journal of Physics, Conferen...· 0 citations
The Global User Intent Dual-phase Estimation for Robots (GUIDER) is deployed on data collected from a robotic arm to test the manipulation phase across various assistance scenarios, including making tea and fetching medicine.
Nicholas Kenny, Cesar Alan Contreras, B. Ouedraogo et al.· 0 citations
Dexterous manipulation with multi-fingered robot hands promises human-level dexterity, but collecting large-scale dexterous robot hand data remains difficult. Learning from human demonstrations has emerged as a scalable alternative to robot teleoperation, providing strong priors on object interaction and contact strategies. Recent sim-to-real RL methods incorporate such priors, but often (i) omit rewards that explicitly incentivize precise contact, yielding weak real-world performance, and/or (ii) generalize poorly to unseen object instances. We propose DemoMimic (Dexterous Motion Mimic), a policy that manipulates objects by focusing on their geometry local to the contact points. Its contact-centric rewards encourage precise contact and improve sim-to-real consistency, yielding a single real-world policy that transfers across objects of varying shape, scale, mass, and friction wherever local contact structure is preserved. Real-world ablations show that DemoMimic achieves 71% success across 16 objects, four tasks, and two robot-hand embodiments, with the smallest sim-to-real drop compared to baselines.
Satvik Sharma, Samrat Sahoo, Huang Huang et al.· 0 citations
Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We present Facet-0, a robotic foundation model that predicts and values the contact consequences of its actions. Facet-0 unifies multimodal representation learning and reinforcement learning (RL) post-training around a joint action-wrench proposal: a causal wrench history is aligned with vision-language semantics and kinematic state, and flow matching generates each action chunk together with the future wrist-wrench profile it is expected to induce. Deployment rollouts train a distributional Action-Wrench Critic to distinguish motions with similar task progress but different contact outcomes, while phase-aware rewards and contact-selective credit concentrate policy improvement on decisive interactions. To accommodate part-specific dynamics, a lightweight bounded actor reuses the frozen representation for on-robot adaptation; RL remains defined over executable Cartesian actions, while an auxiliary wrench head preserves predictive, non-commanded action-contact coupling. Trained on ManuFacet-1K, a 1,000-hour force-synchronized corpus spanning three embodiments and multiple manufacturing cells, the bounded task-adapted system reaches 82% mean success on five sub-millimeter computer-assembly tasks, compared with 15% for the strongest baseline, with 0.5 mm placement accuracy and 50 ms command latency.
Haoyuan Deng, Haichao Liu, Wenkai Guo et al.· 0 citations
A reinforcement learning-based framework for robotic grasp refinement, integrating keypoint-based object representations with a Deep Q-Network (DQN), is proposed, offering a scalable and adaptable solution for contact-rich manipulation tasks.
Amir Arsalan Nematollahi, Shayan Ahmadi, M. T. Masouleh et al.· 0 citations
Learned manipulation policies increasingly predict motions for abstract “hands” and are attractive in practice because they rely on easily collected demonstrations and transfer across robot platforms. Executing these trajectories on multi-arm robots, however, is not trivial. Multi-hand policy outputs must be assigned to physical arms, each arm must realize a configuration-space motion that tracks its prescribed end-effector trajectory, and all arms must respect kinematic limits and avoid collisions. In the absence of algorithms that directly address this problem, practitioners typically extend single-arm inverse-kinematics (IK) pipelines in an ad hoc way, with no guarantees of feasibility or safety. In this work, we close this execution gap with a search-based framework that is theoretically complete for grounding policy-generated multi-hand trajectories onto physical multi-arm systems. Building on Conflict-Based Search, our method explicitly searches over both the discrete assignment of trajectories to arms and the continuous Jacobian null spaces of redundant manipulators, using redundancy to avoid inter-arm collisions while tracking the prescribed motions. This unified treatment of assignment and null-space motion yields a practically efficient planner that safely realizes coordinated manipulation-policy outputs on multi-arm robots. See omcbsa.github.io for more.
Yorai Shaoul, Jiao-Yang Li, Maxim Likhachev· Proceedings of the Internati...· 0 citations
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