Shoaib Aslam

Object transfer through throwing is a classic dynamic manipulation task that necessitates precise control and perception capabilities. However, developing dynamic models for unstructured environments using analytical methods presents challenges. In this study, we present DartBot, a robot that integrates tactile exploration and reinforcement learning to achieve robust throwing skills for nonrigid relatively small objects under the influence of moment of inertia which cause the object to spin in the air. Unlike traditional sim-to-real transfer methods, our approach involves direct training of the agent on a real hardware robot equipped with a high-resolution tactile sensor, enabling reinforced learning in a realistic and dynamic environment. By leveraging tactile perception, we incorporate pseudo-embeddings of the physical properties of objects into the learning process through tilting actions at two distinct angles. This tactile information enables the agent to infer and adapt its throwing strategy, resulting in improved accuracy when handling various objects and targeting distant locations. Furthermore, we demonstrate that the quality of a grasp significantly impacts the success rate of the throwing task. We evaluate the effectiveness of our method through extensive experiments, demonstrating superior performance and generalization capabilities in real-world throwing scenarios. We achieved a success rate of 95% for unseen objects with a mean error of 3.15 cm from the goal. A high-resolution video demo of our work is available at https://youtu.be/KNFgDeLt-0g.

Equations 1–4 on Page 6397 in the above-named article (ibid., vol. 6, no. 4, pp. 6394–6401, Oct. 2021) were incorrectly printed. The correct form of the equations are given here. Also, the corresponding author details need correction. The correct corresponding author is Jungwon Seo (email: junseo@ust.hk).

This letter introduces a new method for scooping manipulation in constrained environments. The presented technique lends itself to relatively thin objects, with a large width-to-thickness ratio, which are commonly seen in many domestic and industrial tasks yet can cause considerable difficulty in handling. We present a novel approach to scooping based on a mobility analysis that leads to a new way to perform the operation via motion control with a two-fingered gripper. Our method thus expands the ways in which the manipulation of scooping can be performed, without necessitating underactuated control or a compliant mechanism. Our variable-length digit is suggested as a key facilitator for realizing the motion-controlled scooping maneuver. We present an extensive set of experiments showing the effectiveness of our approach in various scenarios featuring a constrained workspace, including bin picking from clutter.