  In robotics and artificial intelligence, the integration of tactile
processing is becoming increasingly pivotal, especially in learning to execute
intricate tasks like alignment and insertion. However, existing works focusing
on tactile methods for insertion tasks predominantly rely on robot
teleoperation data and reinforcement learning, which do not utilize the rich
insights provided by human's control strategy guided by tactile feedback. For
utilizing human sensations, methodologies related to learning from humans
predominantly leverage visual feedback, often overlooking the invaluable
tactile feedback that humans inherently employ to finish complex manipulations.
Addressing this gap, we introduce "MimicTouch", a novel framework that mimics
human's tactile-guided control strategy. In this framework, we initially
collect multi-modal tactile datasets from human demonstrators, incorporating
human tactile-guided control strategies for task completion. The subsequent
step involves instructing robots through imitation learning using multi-modal
sensor data and retargeted human motions. To further mitigate the embodiment
gap between humans and robots, we employ online residual reinforcement learning
on the physical robot. Through comprehensive experiments, we validate the
safety of MimicTouch in transferring a latent policy learned through imitation
learning from human to robot. This ongoing work will pave the way for a broader
spectrum of tactile-guided robotic applications.
