PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation
(2312.02531)Abstract
The study addresses the foundational and challenging task of peg-in-hole assembly in robotics, where misalignments caused by sensor inaccuracies and mechanical errors often result in insertion failures or jamming. This research introduces PolyFit, representing a paradigm shift by transitioning from a reinforcement learning approach to a supervised learning methodology. PolyFit is a Force/Torque (F/T)-based supervised learning framework designed for 5-DoF peg-in-hole assembly. It utilizes F/T data for accurate extrinsic pose estimation and adjusts the peg pose to rectify misalignments. Extensive training in a simulated environment involves a dataset encompassing a diverse range of peg-hole shapes, extrinsic poses, and their corresponding contact F/T readings. To enhance extrinsic pose estimation, a multi-point contact strategy is integrated into the model input, recognizing that identical F/T readings can indicate different poses. The study proposes a sim-to-real adaptation method for real-world application, using a sim-real paired dataset to enable effective generalization to complex and unseen polygon shapes. PolyFit achieves impressive peg-in-hole success rates of 97.3% and 96.3% for seen and unseen shapes in simulations, respectively. Real-world evaluations further demonstrate substantial success rates of 86.7% and 85.0%, highlighting the robustness and adaptability of the proposed method.
Overview
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PolyFit is a new framework for peg-in-hole robotic assembly tasks using Force/Torque data to improve precision.
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Instead of reinforcement learning, PolyFit utilizes supervised learning and does not rely on vision-based systems.
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A multi-point contact strategy is employed to better estimate the peg's position relative to the hole.
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Sim-to-real adaptation is used to bridge the gap between simulations and real-world conditions, ensuring model generalization.
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Empirical tests show high success rates in simulations and real-world scenarios, paving the way for more advanced robotic assembly applications.
Introduction
The topic explored in this paper is the peg-in-hole assembly task in robotics. This task is pivotal in manufacturing and often suffers from the issue of misalignments, which can lead to failed insertions or damage. The paper introduces PolyFit, a novel framework using Force/Torque (F/T) data for facilitating precise peg-in-hole assemblies.
PolyFit Framework
PolyFit stands out by leveraging supervised learning rather than the traditional reinforcement learning approach for the peg-in-hole task. Importantly, it operates without requiring vision-based systems, which are susceptible to environmental challenges like lighting or occlusions. Extensive simulations create a rich dataset of various peg-hole configurations paired with their F/T responses, which allows the model to accurately learn and predict misalignments.
To enhance the accuracy of pose estimation in scenarios where identical F/T readings may correspond to different poses, PolyFit uses a multi-point contact strategy. This technique helps improve the model's performance when estimating the relative position, i.e., the extrinsic pose, between the peg and the hole.
Sim-to-Real Adaptation
The transition from simulated to real environments presents notable challenges due to discrepancies in dynamics and other unmodeled factors. To address this issue, PolyFit introduces a sim-to-real adaptation technique that employs a paired dataset, combining simulated and real-world data to improve the generalization of the model. This technique has been validated by the framework's high success rates in simulation (over 96% for both seen and unseen shapes) and tangible success in real-world applications (over 85% on unseen shapes).
Performance and Impact
PolyFit has demonstrated its effectiveness and adaptability through rigorous testing in both simulated and real-world environments. This research significantly advances the field by providing a framework that performs reliably across various unseen polygonal shapes. This method reduces data collection efforts and safety concerns in real-world settings, as a dynamic simulator facilitates learning.
Future Work
The groundwork laid by PolyFit encourages continued development towards closed-loop systems capable of handling more diverse tasks, such as connecting cables or other complex components. The ultimate goal is to streamline robotic assembly methods to be more efficient, safe, and versatile, contributing to advances in automated manufacturing.
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