Neural-network-based grasp detection has achieved remarkable success in robotic manipulation due to its efficiency and generalization ability. However, detected poses are often not optimized, leading to undesired object motion or collisions during physical execution. This paper proposes a motion-aware refinement framework that minimizes estimated object motion while enforcing collision avoidance. The seven-dimensional pose is decomposed into approach direction, engagement depth, planar projection, and gripper opening width, enabling efficient and interpretable optimization in lower-dimensional subspaces. To evaluate grasp stability beyond conventional success metrics, we introduce the observed success rate (OSR) together with quantitative motion measurements including translation, rotation, and tilt. Real-robot experiments show that, for high-profile objects, the full pipeline improves the measured success rate (MSR) from 93.33% to 100% and OSR from 83.33% to 97.78%. It also reduces the mean translation from <inline-formula> <tex-math notation="LaTeX">$6.099{\,}mm$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$2.684{\,}mm$ </tex-math></inline-formula>, rotation from 3.732° to 1.344°, and tilt from <inline-formula> <tex-math notation="LaTeX">$4.417{\,}mm$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$1.313{\,}mm$ </tex-math></inline-formula>, while requiring <inline-formula> <tex-math notation="LaTeX">$0.82\pm 0.42{\,}s$ </tex-math></inline-formula> on average. For low-profile objects that cannot be detected by the baseline point-cloud-based planner, the full pipeline achieves 100% MSR and OSR.
Language-guided robotic grasping has made significant progress in semantic understanding, but existing methods often rely on open-loop execution strategies and struggle to handle physical disturbances such as object collisions, target displacement, and transportation slippage. To address this problem, this paper proposes a stability-aware dynamic recovery mechanism, named SADR. Based on a multi-threaded decoupled architecture, SADR decouples semantic planning, target tracking, and execution control, and constructs a two-stage stability criterion through pre-closure displacement checking and post-closure force/current feedback verification. When target instability, missed grasping, or slippage is detected, the system performs local trajectory correction and re-grasping based on real-time tracking results, without restarting global semantic planning. Experiments in PyBullet show that SADR significantly improves the grasping success rate under high-density disturbance scenarios while reducing the average task completion time. This study provides an effective closed-loop recovery solution for improving the reliability of robotic grasping tasks in complex simulation environments.
Chuncheng Zhang, Lei Sun· 2026 IEEE 27th China Confere...· 0 citations
Mainstream plan-then-track approaches to multifingered grasp execution entail selecting a grasp, planning a collision-free trajectory, and tracking the resulting trajectory via a feedback controller. Pose-estimation error during execution or scene motion can invalidate this open-loop commitment and trigger replanning. We thus present Grasp Distance Fields (GDFs), smooth softmin distances to finite sets of arm-hand grasp configurations. Using their negative gradients as feedback, we jointly select and execute grasps without planning a trajectory. A CBF-CLF quadratic program (QP) enforces self-collision, workspace, object, and obstacle-clearance constraints, while its CLF slack quantifies obstruction of task progress. We bound the softmin approximation error by $\log N/\rho$ and prove forward invariance of the filtered safe set. To handle changes in contact topology, we combine a hysteretic contact-mode transition with a wrench-quality CBF that limits degradation of the realized force-closure margin relative to hold onset. Using our method, a fixed-base manipulator and a Unitree G1 equipped with the same underactuated hand grasp and lift 46 of 50 test objects amid clutter and moving obstacles. The realized grasps also retain a median 94% of their synthesized quality margin, and each QP solve requires 0.09 ms within a 20 ms control interval. Project page: www.clintonenwerem.com/gdf.
Clinton Enwerem, John S. Baras, C. Belta· 0 citations
A deep learning-based grasp estimation model designed to enable robotic manipulation with articulated objects that incorporates the attention-based semantic and geometric feature fusion (ASGF) module improved the grasp success rate in the evaluated setting.
Dongwoo Lee, Yeongmin Kim, Seong-Bo Jo et al.· IEEE Access· 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
Dexterous grasp generation across robot hands is challenging because hands differ in kinematic topology, actuation dimensions, and native command spaces. We introduce GraspGraphNet, a topology-aware grasp generation framework that represents each hand as a URDF-derived kinematic graph and directly generates executable palm poses and joint configurations. GraspGraphNet combines hierarchical object surface encoding, differentiable forward kinematics, and dynamic world-edge message passing to model evolving robot-object interactions. It applies conditional flow matching directly in executable palm-pose and joint-state space, avoiding post-processing optimization, inverse kinematics, and retargeting. Using a shared model trained on Barrett Hand, Allegro Hand, and Shadow Hand, GraspGraphNet achieves an average success rate of 83.48% with 40ms inference time per grasp on a 40-object benchmark. Without retraining, the same model achieves 72.70% success on controlled finger-removal variants, demonstrating robustness to hand-topology variations. These results suggest that graph-structured hand representations can effectively support dexterous grasp generation across robot hands with different kinematic structures. Project: https://lysees.github.io/graspgraphnet-page
Y. Lee, Taeyeop Lee, Hyosup Shin et al.· 0 citations