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