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PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation Tasks
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PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation Tasks
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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
Forward citations
Cited by 8 Pith papers
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VLBiMan: Vision-Language Anchored One-Shot Demonstration Enables Generalizable Bimanual Robotic Manipulation
VLBiMan framework enables generalizable bimanual manipulation from single human demonstrations via vision-language anchored task decomposition and adaptation without retraining.
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JOIN: Anchor-Grasp-Conditioned Joining via Opposition, Inference, and Navigation for Bimanual Assistive Manipulation
JOIN decomposes bimanual joining into plan-drive-grasp phases and uses a VLM to let a mobile manipulator complete tasks with a pre-grasped anchor arm, achieving 19/20 success versus 14/20 for baselines on representative ADLs.
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