📝 SummarXiv

Abstract

In-hand manipulation tasks, particularly in human-inspired robotic systems, must rely on distributed tactile sensing to achieve precise control across a wide variety of tasks. However, the optimal configuration of this network of sensors is a complex problem, and while the fingertips are a common choice for placing sensors, the contribution of tactile information from other regions of the hand is often overlooked. This work investigates the impact of tactile feedback from various regions of the fingers and palm in performing in-hand object reorientation tasks. We analyze how sensory feedback from different parts of the hand influences the robustness of deep reinforcement learning control policies and investigate the relationship between object characteristics and optimal sensor placement. We identify which tactile sensing configurations contribute to improving the efficiency and accuracy of manipulation. Our results provide valuable insights for the design and use of anthropomorphic end-effectors with enhanced manipulation capabilities.