PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation Tasks
Keywords: Bimanual Benchmarking, Imitation Learning, Bimanual Manipulation
TL;DR: A novel benchmark focusing on bimanual manipulation with large task diversity for systematically studying bimanual capabilities across a wide range of tabletop tasks.
Abstract: Bimanual manipulation is challenging due to precise spatial and temporal coordination required between two arms.
While there exist several real-world bimanual systems, there is a lack of simulated benchmarks with a large task diversity for systematically studying bimanual capabilities across a wide range of tabletop tasks.
This paper addresses the gap by extending RLBench to bimanual manipulation.
We open-source our code and benchmark, which comprises 18 new tasks with 41 unique task variations, each requiring a high degree of coordination and adaptability.
To initiate the benchmark, we extended several state-of-the-art methods to bimanual manipulation and also present a language-conditioned behavioral cloning agent PerAct2, an extension of the PerAct framework. This method enables the learning and execution of bimanual 6-DoF manipulation tasks. Our novel network architecture efficiently integrates language processing with action prediction, allowing robots to understand and perform complex bimanual tasks in response to user-specified goals.
Submission Number: 29
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