title: Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation
summary: Given the high cost of collecting robotic data in the real world, sample
  efficiency is a consistently compelling pursuit in robotics. In this paper, we
  introduce SGRv2, an imitation learning framework that enhances sample
  efficiency through improved visual and action representations. Central to the
  design of SGRv2 is the incorporation of a critical inductive bias-action
  locality, which posits that robot's actions are predominantly influenced by the
  target object and its interactions with the local environment. Extensive
  experiments in both simulated and real-world settings demonstrate that action
  locality is essential for boosting sample efficiency. SGRv2 excels in RLBench
  tasks with keyframe control using merely 5 demonstrations and surpasses the RVT
  baseline in 23 of 26 tasks. Furthermore, when evaluated on ManiSkill2 and
  MimicGen using dense control, SGRv2's success rate is 2.54 times that of SGR.
  In real-world environments, with only eight demonstrations, SGRv2 can perform a
  variety of tasks at a markedly higher success rate compared to baseline models.
  Project website: http://sgrv2-robot.github.io
paper_authors._id: 6720b560b5cac389a99f616a
paper_authors.name: Tong Zhang
paper_authors._id: 6720b560b5cac389a99f616b
paper_authors.name: Yingdong Hu
paper_authors._id: 6720b560b5cac389a99f616c
paper_authors.name: Jiacheng You
paper_authors._id: 6720b560b5cac389a99f616d
paper_authors.name: Yang Gao
paper_title: Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation
paper_summary: Given the high cost of collecting robotic data in the real world, sample
  efficiency is a consistently compelling pursuit in robotics. In this paper, we
  introduce SGRv2, an imitation learning framework that enhances sample
  efficiency through improved visual and action representations. Central to the
  design of SGRv2 is the incorporation of a critical inductive bias-action
  locality, which posits that robot's actions are predominantly influenced by the
  target object and its interactions with the local environment. Extensive
  experiments in both simulated and real-world settings demonstrate that action
  locality is essential for boosting sample efficiency. SGRv2 excels in RLBench
  tasks with keyframe control using merely 5 demonstrations and surpasses the RVT
  baseline in 23 of 26 tasks. Furthermore, when evaluated on ManiSkill2 and
  MimicGen using dense control, SGRv2's success rate is 2.54 times that of SGR.
  In real-world environments, with only eight demonstrations, SGRv2 can perform a
  variety of tasks at a markedly higher success rate compared to baseline models.
  Project website: http://sgrv2-robot.github.io
paper_ai_keywords: imitation learning sample efficiency inductive bias-action locality RLBench tasks keyframe control ManiSkill2 MimicGen dense control success rate