Stability-Aware Closed-Loop Recovery for Robotic Grasping in Complex Simulation Environments

Object-Motion-Aware Grasp Pose Refinement for Stable and Collision-Free Real-Robot Grasping

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.

Grasp Execution Without a Planner: Configuration-Space Grasp Distance Fields with Certified Safety&Guaranteed Quality

GDFs, smooth softmin distances to finite sets of arm-hand grasp configurations to handle changes in contact topology are presented, and a hysteretic contact-mode transition with a wrench-quality CBF that limits degradation of the realized force-closure margin relative to hold onset is combined.

Spatiotemporal Agility: Time-Constrained Reinforcement Learning for Vision-Guided Dynamic Quadrupedal Interception

Legged robots require robust agility to perceive and interact with complex and dynamic environments within a constrained time. However, most existing quadruped locomotion works rely on velocity-tracking policy, which struggle to reach precise targets within strict temporal constraints. Moreover, integrating real-time perception with agile locomotion for highly dynamic targets remains challenging due to sensor latency and processing delays. To concretely study and benchmark such agility in dynamic settings, we introduce a challenging ball-catching task for legged robots. This paper proposes an integrated framework that combines a vision module for landing point and time prediction with a direct position and time conditioned RL locomotion policy, instead of intermediate velocity commands. Beyond the method design, this work presents a system-level contribution that completes real-time robotic interception system that integrates multi-camera perception, online trajectory prediction, low-latency target communication, and sim-to-real locomotion control into a closed-loop deployment pipeline. By explicitly predicting the future spatial-temporal target, our approach mitigates perception latency during dynamic interception. We conducted extensive ball-catching experiments for the legged robot. Through comparative experiments against a velocity-tracking baseline, our direct target-conditioned approach achieves a higher success rate in catching balls with predicted landing spots within 2 meters and flight times between 0.8 and 1.2 seconds. This shows that the robot has successfully completed the dynamic ball-catching task under our tested setup. Furthermore, our policy exhibits a smaller performance gap after deployment, suggesting improved sim-to-real behavior in these trials.

Vision-Based Predictive Control for Dual-Arm Nonprehensile Transportation

Dual-arm robots often encounter difficulties when handling easily deformable or structurally complex objects using traditional grasping-based manipulation. In addition, grasping and releasing operations introduce significant time overhead. To address these limitations, this paper proposes a vision-based predictive control framework for dual-arm nonprehensile transportation. The proposed method employs a hybrid end effector design that integrates an elastic tether with a tray, enabling flexible and stable transportation without direct grasping. A predictive control strategy is adopted to optimize dual-arm motion trajectories on the move under kinematic and safety constraints. To further enhance coordination accuracy, a direct visual servoing scheme is incorporated to dynamically regulate the arm velocities, minimizing relative motion between the end effectors and the object. This effectively suppresses oscillations induced by the elastic tether. Both simulation and experimental results demonstrate that the proposed approach ensures convergence to desired states and achieves continuous, stable, and safe object transportation, even in the presence of disturbances.

PECHC: Robust Tactile Grasping Stabilization in Vision-Denied Peripersonal Space

GUIDER: Evaluating Goal-Free Human Intent Inference for Teleoperated Manipulation on Real-Robot Data

This paper presents an evaluation of a goal-free probabilistic framework for human intent inference during robotic manipulation. We deploy the Global User Intent Dual-phase Estimation for Robots (GUIDER) on data collected from a robotic arm to test the manipulation phase across various assistance scenarios, including making tea and fetching medicine. To support operation, we add online probability updates, workspace limits, support-plane filtering, and a grasping mode that prioritizes feasible grasp regions, all of which are tested on the recorded data while preserving its original temporal conditions. Across 20 manipulation steps in three scenarios, GUIDER estimated human intent within the correct grasp-candidate set in all cases and achieved a time to confident prediction of 3.7 s, a remaining time before first grasp of 49.6 s, a prediction stability of 96.4%, and a runtime of 4.857/4.474 s (mean/median) per perceptual phase of intent.