BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic Manipulation

• URL: http://arxiv.org/abs/2406.10093v2
• Date: Wed, 4 Sep 2024 08:20:40 GMT
• Title: BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic Manipulation
• Authors: Dongjie Yu, Hang Xu, Yizhou Chen, Yi Ren, Jia Pan,
• Abstract summary: We introduce a novel keypose-conditioned consistency policy tailored for bimanual manipulation. It is a hierarchical imitation learning framework that consists of a high-level keypose predictor and a low-level trajectory generator. Simulated and real-world experimental results demonstrate that the proposed approach surpasses baseline methods in terms of success rate and operational efficiency.
• Score: 48.08416841005715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bimanual manipulation tasks typically involve multiple stages which require efficient interactions between two arms, posing step-wise and stage-wise challenges for imitation learning systems. Specifically, failure and delay of one step will broadcast through time, hinder success and efficiency of each sub-stage task, and thereby overall task performance. Although recent works have made strides in addressing certain challenges, few approaches explicitly consider the multi-stage nature of bimanual tasks while simultaneously emphasizing the importance of inference speed. In this paper, we introduce a novel keypose-conditioned consistency policy tailored for bimanual manipulation. It is a hierarchical imitation learning framework that consists of a high-level keypose predictor and a low-level trajectory generator. The predicted keyposes provide guidance for trajectory generation and also mark the completion of one sub-stage task. The trajectory generator is designed as a consistency model trained from scratch without distillation, which generates action sequences conditioning on current observations and predicted keyposes with fast inference speed. Simulated and real-world experimental results demonstrate that the proposed approach surpasses baseline methods in terms of success rate and operational efficiency. Codes are available at https://github.com/ManUtdMoon/BiKC.

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