title: Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost
    Whole-Body Teleoperation
summary: Imitation learning from human demonstrations has shown impressive performance
  in robotics. However, most results focus on table-top manipulation, lacking the
  mobility and dexterity necessary for generally useful tasks. In this work, we
  develop a system for imitating mobile manipulation tasks that are bimanual and
  require whole-body control. We first present Mobile ALOHA, a low-cost and
  whole-body teleoperation system for data collection. It augments the ALOHA
  system with a mobile base, and a whole-body teleoperation interface. Using data
  collected with Mobile ALOHA, we then perform supervised behavior cloning and
  find that co-training with existing static ALOHA datasets boosts performance on
  mobile manipulation tasks. With 50 demonstrations for each task, co-training
  can increase success rates by up to 90%, allowing Mobile ALOHA to autonomously
  complete complex mobile manipulation tasks such as sauteing and serving a piece
  of shrimp, opening a two-door wall cabinet to store heavy cooking pots, calling
  and entering an elevator, and lightly rinsing a used pan using a kitchen
  faucet. Project website: https://mobile-aloha.github.io
paper_authors._id: 65979ad547b8f1a2563a9a5f
paper_authors.name: Zipeng Fu
paper_authors._id: 65979ad547b8f1a2563a9a60
paper_authors.name: Tony Z. Zhao
paper_authors._id: 65979ad547b8f1a2563a9a61
paper_authors.name: Chelsea Finn
paper_title: Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost
    Whole-Body Teleoperation
paper_summary: Imitation learning from human demonstrations has shown impressive performance
  in robotics. However, most results focus on table-top manipulation, lacking the
  mobility and dexterity necessary for generally useful tasks. In this work, we
  develop a system for imitating mobile manipulation tasks that are bimanual and
  require whole-body control. We first present Mobile ALOHA, a low-cost and
  whole-body teleoperation system for data collection. It augments the ALOHA
  system with a mobile base, and a whole-body teleoperation interface. Using data
  collected with Mobile ALOHA, we then perform supervised behavior cloning and
  find that co-training with existing static ALOHA datasets boosts performance on
  mobile manipulation tasks. With 50 demonstrations for each task, co-training
  can increase success rates by up to 90%, allowing Mobile ALOHA to autonomously
  complete complex mobile manipulation tasks such as sauteing and serving a piece
  of shrimp, opening a two-door wall cabinet to store heavy cooking pots, calling
  and entering an elevator, and lightly rinsing a used pan using a kitchen
  faucet. Project website: https://mobile-aloha.github.io
paper_ai_summary: Co-training of supervised behavior cloning with static and mobile manipulation datasets improves the success rates of mobile manipulation tasks using a whole-body teleoperation system.
paper_ai_keywords: imitation learning human demonstrations robotics mobile manipulation bimanual tasks whole-body control behavior cloning supervised learning