PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation Tasks
<|reference_start|>PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation Tasks: 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 comprising 13 new tasks with 23 unique task variations, each requiring a high degree of coordination and adaptability. To kickstart the benchmark, we extended several state-of-the art methods to bimanual manipulation and also present a language-conditioned behavioral cloning agent -- PerAct2, which 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. Project website with code is available at: http://bimanual.github.io<|reference_end|>
@article{grotz2024peract2:,
    title={PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation
  Tasks},
    author={Markus Grotz, Mohit Shridhar, Tamim Asfour, Dieter Fox},
    journal={arXiv preprint arXiv:2407.00278},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00278},
    primaryClass={cs.RO cs.AI cs.CV cs.LG}
}