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HiRe-MoMa introduces a hierarchical residual policy learning framework to improve stability and sample efficiency in fine-tuning complex mobile manipulation systems in real-world settings. It addresses challenges from high-dimensional action spaces and module coupling with sparse human guidance.
HiRe-MoMa introduces a hierarchical residual policy learning framework to improve stability and sample efficiency in fine-tuning complex mobile manipulation systems in real-world settings. It addresses challenges from high-dimensional action spaces and module coupling with sparse human guidance.
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Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and intricate coupling between heterogeneous modules. In this paper, we propose HiRe-MoMa (Hierarchical Residual Mobile Manipulation), a...
Real-world fine-tuning of mobile manipulation robots is difficult due to complex control and integration demands. HiRe-MoMa's approach could accelerate deployment and adaptability of autonomous robotic systems in practical environments.
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