Interpreting contact interactions to overcome failure in robot assembly tasks
Abstract/Contents
- Abstract
- A key challenge towards the goal of multi-part assembly tasks is finding methods for robust sensorimotor control in the presence of uncertainty. In contrast to previous works that rely on a priori knowledge on whether two parts match, we propose a method to learn this through physical interaction. This method involves a hierarchical approach that enables a robot to autonomously assemble parts while being uncertain about part types and positions. In particular, the method's probabilistic approach learns a set of differentiable filters that leverage the tactile sensorimotor trace from failed assembly attempts to update a robot's belief about part position and type. This enables the robot to overcome assembly failure. Through experiments, we demonstrate the effectiveness of the proposed approach on a set of object fitting tasks. The experimental results indicate that the proposed approach achieves higher precision in object position and type estimation, and accomplishes object fitting tasks faster than baselines.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Zachares, Peter Anastasi |
|---|---|
| Degree committee member | Bohg, Jeannette, 1981- |
| Thesis advisor | Bohg, Jeannette, 1981- |
| Associated with | Stanford University, Department of Mechanical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Peter Anastasi Zachares. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Engineering Stanford University 2020. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Peter Anastasi Zachares
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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