Name
Abstract | ||
|---|---|---|
The sense of touch, being the earliest sensory system to develop in a human body [1], 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICRA.2017.7989326 | ICRA |
DocType | Volume | Issue |
Conference | abs/1705.06243 | 1 |
Citations | PageRank | References |
4 | 0.40 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaeyong Sung | 1 | 395 | 14.51 |
| John Kenneth Salisbury Jr. | 2 | 1403 | 200.30 |
| Ashutosh Saxena | 3 | 4575 | 227.88 |