Aug 2026· 2026 IEEE International Conference on Mechatronics and Automation (ICMA)· pp. 1788-1793· 0 citations· 20 references
Abstract
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.
Quantitative experiments showed that the proposed method generally outperformed the baselines in mass and CoM variations, particularly in terms of success rate, while maintaining robust performance across the evaluated conditions.
Jinseok Kim, Iksu Choi, Hunjo Lee et al.· Intelligent Service Robotics· 0 citations
This work added YOLO-based object and hand detection, stereo vision-based localization using the robot's built-in low-resolution fisheye cameras, and task-specific corrections for grasp execution to form a novel calibration-based grasping pipeline that does not require RGB-D cameras, motion capture, or external trackin...
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 is proposed, which mitigates perception latency during dynamic interception.
Yi-Dong Zhu, Zibo Dai, Tong-Ning Zhang et al.· 0 citations
This work proposes a contact-guided exploration strategy implemented within a Multi-Critic Reinforcement Learning (RL) framework, and obtains candidate interaction points from a general-purpose grasping algorithm, enabling the exploration mechanism to generalise across various object geometries.
Simone Tolomei, Mayank Mittal, F. Angelini et al.· IEEE Robotics and Automation...· 0 citations
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 propos...
Chun-Cheng Zhang, Lei Sun· 2026 IEEE 27th China Confere...· 0 citations
This work proposes a lightweight and modular framework for proactive collision avoidance, operating directly at the end-effector velocity-command level, and shows that the proposed method achieves a higher end-effector collision avoidance rate than the baseline methods.
Changhao Hu, Zeyi Liu, Song Hu et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
