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/
An adaptive learning framework and hardware platform enable a robot to perform mobile manipulation of various articulated objects in unstructured environments with high success rates through online adaptation.
adaptive learning framework behavior cloning online practice mobile manipulation articulated objects online adaptation autonomous online adaptation