利用局部性提高机器人操作中的样本效率

Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation

June 15, 2024

作者: Tong Zhang, Yingdong Hu, Jiacheng You, Yang Gao

cs.AI

摘要

考虑到在现实世界中收集机器人数据的高成本，样本效率一直是机器人领域中一个持续引人注目的追求。在本文中，我们介绍了SGRv2，这是一个通过改进视觉和动作表示来提高样本效率的模仿学习框架。SGRv2设计的核心是引入了一个关键的归纳偏差 - 动作局部性，该偏差认为机器人的动作主要受目标物体及其与局部环境的相互作用所影响。在模拟和真实世界环境中进行的大量实验表明，动作局部性对于提高样本效率至关重要。SGRv2在RLBench任务中以关键帧控制为特色，仅使用5个演示就超越了26项任务中的23项RVT基线。此外，在ManiSkill2和MimicGen上进行密集控制评估时，SGRv2的成功率是SGR的2.54倍。在真实环境中，仅使用八个演示，SGRv2相比基线模型可以以明显更高的成功率执行各种任务。项目网站：http://sgrv2-robot.github.io

English

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