Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation
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
6720b560b5cac389a99f616a
Tong Zhang
6720b560b5cac389a99f616b
Yingdong Hu
6720b560b5cac389a99f616c
Jiacheng You
6720b560b5cac389a99f616d
Yang Gao
Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation
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
SGRv2, an imitation learning framework with an action locality bias, enhances sample efficiency in robotics by improving visual and action representations and outperforms baselines in simulated and real-world tasks.
imitation learning sample efficiency action locality visual representations action representations RLBench keyframe control ManiSkill2 MimicGen dense control success rate