Adaptive Mobile Manipulation for Articulated Objects In the Open World
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/
65b33bafade89bc3faf52049
Haoyu Xiong
claimed_verified
2024-01-26T08:23:34.240Z
6388fb842a897944ea5b0346
https://cdn-avatars.huggingface.co/v1/production/uploads/1669922608346-6388fb842a897944ea5b0346.png
Haoyu Xiong
user
haoyux
65b33bafade89bc3faf5204a
Russell Mendonca
65b33bafade89bc3faf5204b
Kenneth Shaw
65b33bafade89bc3faf5204c
Deepak Pathak
Adaptive Mobile Manipulation for Articulated Objects In the Open World
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/
adaptive learning framework behavior cloning online practice articulated object operation interactive platform autonomous online adaptation mobile manipulation