Interpreting Contact Interactions to Overcome Failure in Robot Assembly Tasks

by   Peter A. Zachares, et al.

A key challenge towards the goal of multi-part assembly tasks is finding robust sensorimotor control methods in the presence of uncertainty. In contrast to previous works that rely on a priori knowledge on whether two parts match, we aim to learn this through physical interaction. We propose a hierachical approach that enables a robot to autonomously assemble parts while being uncertain about part types and positions. In particular, our probabilistic approach learns a set of differentiable filters that leverage the tactile sensorimotor trace from failed assembly attempts to update its belief about part position and type. This enables a robot to overcome assembly failure. We demonstrate the effectiveness of our approach on a set of object fitting tasks. The experimental results indicate that our proposed approach achieves higher precision in object position and type estimation, and accomplishes object fitting tasks faster than baselines.


page 1

page 6


RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment

Part assembly is a typical but challenging task in robotics, where robot...

Lessons Learned Developing an Assembly System for WRS 2020 Assembly Challenge

The World Robot Summit (WRS) 2020 Assembly Challenge is designed to allo...

Assembly of randomly placed parts realized by using only one robot arm with a general parallel-jaw gripper

In industry assembly lines, parts feeding machines are widely employed a...

Unsupervised Co-part Segmentation through Assembly

Co-part segmentation is an important problem in computer vision for its ...

InsertionNet – A Scalable Solution for Insertion

Complicated assembly processes can be described as a sequence of two mai...

3D Part Assembly Generation with Instance Encoded Transformer

It is desirable to enable robots capable of automatic assembly. Structur...