Adaptive Mobile Manipulation for Articulated Objects In the Open World

• URL: http://arxiv.org/abs/2401.14403v2
• Date: Sun, 28 Jan 2024 18:58:29 GMT
• Title: Adaptive Mobile Manipulation for Articulated Objects In the Open World
• Authors: Haoyu Xiong, Russell Mendonca, Kenneth Shaw, Deepak Pathak
• Abstract summary: We introduce Open-World Mobile Manipulation System to tackle realistic articulated object operation. The system is able to increase success rate from 50% of BC pre-training to 95% using online adaptation.
• Score: 37.34288363863099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings, and prior mobile manipulation work is restricted to pick-move-place, which is arguably just the tip of the iceberg in this area. In this paper, we introduce Open-World Mobile Manipulation System, a full-stack approach to tackle realistic articulated object operation, e.g. real-world doors, cabinets, drawers, and refrigerators in open-ended unstructured environments. The robot utilizes an adaptive learning framework to initially learns from a small set of data through behavior cloning, followed by learning from online practice on novel objects that fall outside the training distribution. We also develop a low-cost mobile manipulation hardware platform capable of safe and autonomous online adaptation in unstructured environments with a cost of around 20,000 USD. In our experiments we utilize 20 articulate objects across 4 buildings in the CMU campus. With less than an hour of online learning for each object, the system is able to increase success rate from 50% of BC pre-training to 95% using online adaptation. Video results at https://open-world-mobilemanip.github.io/

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