On Feasibility of Learning Finger-gaiting In-hand Manipulation using Intrinsic Sensing

IEEE International Conference on Robotics and Automation (ICRA), 2022

In this work, we use model-free reinforcement learning (RL) to learn finger-gaiting only via precision grasps and demonstrate finger-gaiting for rotation about an axis purely using on-board proprioceptive and tactile feedback. To tackle the inherent instability of precision grasping, we propose the use of initial state distributions that enable effective exploration of the state space.