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arXiv · 2210.05524

A Learning-Based Estimation and Control Framework for Contact-Intensive Tight-Tolerance Tasks

Bukun Son·Hyelim Choi·Jaemin Yoon·Dongjun Lee
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Abstract

We present a two-stage framework that integrates a learning-based estimator and a controller, designed to address contact-intensive tasks. The estimator leverages a Bayesian particle filter with a mixture density network (MDN) structure, effectively handling multi-modal issues arising from contact information. The controller combines a self-supervised and reinforcement learning (RL) approach, strategically dividing the low-level admittance controller's parameters into labelable and non-labelable categories, which are then trained accordingly. To further enhance accuracy and generalization performance, a transformer model is incorporated into the self-supervised learning component. The proposed framework is evaluated on the bolting task using an accurate real-time simulator and successfully transferred to an experimental environment. More visualization results are available on our project website: https://sites.google.com/view/2stagecitt

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Bukun Son, Hyelim Choi, Jaemin Yoon, Dongjun Lee. 2022-10-11. A Learning-Based Estimation and Control Framework for Contact-Intensive Tight-Tolerance Tasks. https://arxiv.org/abs/2210.05524

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