[2312.06639] Harmonic Mobile Manipulation
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Computer Science > Robotics

arXiv:2312.06639 (cs)
[Submitted on 11 Dec 2023 (v1), last revised 5 Dec 2024 (this version, v3)]

Title:Harmonic Mobile Manipulation

Authors:Ruihan Yang, Yejin Kim, Rose Hendrix, Aniruddha Kembhavi, Xiaolong Wang, Kiana Ehsani
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Abstract:Recent advancements in robotics have enabled robots to navigate complex scenes or manipulate diverse objects independently. However, robots are still impotent in many household tasks requiring coordinated behaviors such as opening doors. The factorization of navigation and manipulation, while effective for some tasks, fails in scenarios requiring coordinated actions. To address this challenge, we introduce, HarmonicMM, an end-to-end learning method that optimizes both navigation and manipulation, showing notable improvement over existing techniques in everyday tasks. This approach is validated in simulated and real-world environments and adapts to novel unseen settings without additional tuning. Our contributions include a new benchmark for mobile manipulation and the successful deployment with only RGB visual observation in a real unseen apartment, demonstrating the potential for practical indoor robot deployment in daily life. More results are on our project site: this https URL
Comments: More results are on our project site: this https URL
Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2312.06639 [cs.RO]
  (or arXiv:2312.06639v3 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2312.06639

Submission history

From: Ruihan Yang [view email]
[v1] Mon, 11 Dec 2023 18:54:42 UTC (22,272 KB)
[v2] Tue, 15 Oct 2024 03:40:18 UTC (22,326 KB)
[v3] Thu, 5 Dec 2024 20:36:32 UTC (22,326 KB)
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