DartBot: Overhand Throwing of Deformable Objects With Tactile Sensing and Reinforcement Learning

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. Note to Practitioners—The industrial demand for precise and accurate object transfer beyond the robot’s maximum kinematic range is rapidly growing, necessitating advancements in robotic throwing manipulation to efficiently utilize resources. In this context, this paper contributes to the field by presenting a method for the transfer of deformable objects through overhand throwing, addressing the challenges of achieving precise control and perception capabilities by learning the complex physics of the task using raw data. The approach offers valuable insights and techniques for enhancing throwing manipulation tasks. The integration of high-resolution tactile sensing and reinforcement learning, while considering the influence of moment of inertia, opens up new possibilities for handling deformable objects in throwing tasks and developing dynamic models for such unstructured environments. The learned throwing policy is applied to a variety of previously unseen objects, demonstrating its effectiveness and ability to generalize in real-world throwing scenarios. The presented work holds applicability in various domains such as medical rehabilitation, logistic warehouses for packaging, and handling of urban waste. It shows promise in improving the precision, accuracy and efficiency of the throwing task. In the future, we aim to expand the current overhand throwing framework by incorporating rapid grasping in cluttered environments and enabling throwing to far distances with diverse target locations.