Interpretable Robotic Manipulation from Language

• URL: http://arxiv.org/abs/2405.17047v1
• Date: Mon, 27 May 2024 11:02:21 GMT
• Title: Interpretable Robotic Manipulation from Language
• Authors: Boyuan Zheng, Jianlong Zhou, Fang Chen,
• Abstract summary: We introduce an explainable behavior cloning agent, named Ex-PERACT, specifically designed for manipulation tasks. At the top level, the model is tasked with learning a discrete skill code, while at the bottom level, the policy network translates the problem into a voxelized grid and maps the discretized actions to voxel grids. We evaluate our method across eight challenging manipulation tasks utilizing the RLBench benchmark, demonstrating that Ex-PERACT not only achieves competitive policy performance but also effectively bridges the gap between human instructions and machine execution in complex environments.
• Score: 11.207620790833271
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans naturally employ linguistic instructions to convey knowledge, a process that proves significantly more complex for machines, especially within the context of multitask robotic manipulation environments. Natural language, moreover, serves as the primary medium through which humans acquire new knowledge, presenting a potentially intuitive bridge for translating concepts understandable by humans into formats that can be learned by machines. In pursuit of facilitating this integration, we introduce an explainable behavior cloning agent, named Ex-PERACT, specifically designed for manipulation tasks. This agent is distinguished by its hierarchical structure, which incorporates natural language to enhance the learning process. At the top level, the model is tasked with learning a discrete skill code, while at the bottom level, the policy network translates the problem into a voxelized grid and maps the discretized actions to voxel grids. We evaluate our method across eight challenging manipulation tasks utilizing the RLBench benchmark, demonstrating that Ex-PERACT not only achieves competitive policy performance but also effectively bridges the gap between human instructions and machine execution in complex environments.

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