A Comparison of Imitation Learning Algorithms for Bimanual Manipulation

• URL: http://arxiv.org/abs/2408.06536v2
• Date: Sat, 24 Aug 2024 19:01:16 GMT
• Title: A Comparison of Imitation Learning Algorithms for Bimanual Manipulation
• Authors: Michael Drolet, Simon Stepputtis, Siva Kailas, Ajinkya Jain, Jan Peters, Stefan Schaal, Heni Ben Amor,
• Abstract summary: In this work, we demonstrate the limitations and benefits of prominent imitation learning approaches. We evaluate each algorithm on a complex bimanual manipulation task involving an over-constrained dynamics system. We find that imitation learning is well suited to solve such complex tasks, but not all algorithms are equal in terms of handling perturbations, training requirements, performance, and ease of use.
• Score: 22.531439806919547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Amidst the wide popularity of imitation learning algorithms in robotics, their properties regarding hyperparameter sensitivity, ease of training, data efficiency, and performance have not been well-studied in high-precision industry-inspired environments. In this work, we demonstrate the limitations and benefits of prominent imitation learning approaches and analyze their capabilities regarding these properties. We evaluate each algorithm on a complex bimanual manipulation task involving an over-constrained dynamics system in a setting involving multiple contacts between the manipulated object and the environment. While we find that imitation learning is well suited to solve such complex tasks, not all algorithms are equal in terms of handling environmental and hyperparameter perturbations, training requirements, performance, and ease of use. We investigate the empirical influence of these key characteristics by employing a carefully designed experimental procedure and learning environment. Paper website: https://bimanual-imitation.github.io/

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