The sense of touch, being the earliest sensory system to develop in a human body , plays a critical part of our daily interaction with the environment. In order to successfully complete a task, many manipulation interactions require incorporating haptic feedback. However, manually designing a feedback mechanism can be extremely challenging. In this work, we consider manipulation tasks that need to incorporate tactile sensor feedback in order to modify a provided nominal plan. To incorporate partial observation, we present a new framework that models the task as a partially observable Markov decision process (POMDP) and learns an appropriate representation of haptic feedback which can serve as the state for a POMDP model. The model, that is parametrized by deep recurrent neural networks, utilizes variational Bayes methods to optimize the approximate posterior. Finally, we build on deep Q-learning to be able to select the optimal action in each state without access to a simulator. We test our model on a PR2 robot for multiple tasks of turning a knob until it clicks.
Learning to Represent Haptic Feedback for Partially-Observable Tasks,
Jaeyong Sung, J. Kenneth Salisbury, Ashutosh Saxena. In International Conference on Robotics and Automation (ICRA), 2017.
(finalist for Best Cognitive Robotics Paper Award)
Following video shows examples of possible failure modes and result of our model on PR2 robot on "stirrer", "speaker", and "fan knob".