利用局部性提升機器人操作中的樣本效率

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

鑑於在現實世界中收集機器人數據的高成本，樣本效率一直是機器人領域中一個持續引人注目的追求。在本文中，我們介紹了SGRv2，一個透過改進視覺和動作表示來提高樣本效率的模仿學習框架。SGRv2 設計的核心是引入了一個關鍵的歸納偏差-動作局部性，這認為機器人的動作主要受目標物體以及其與局部環境的互動所影響。在模擬和真實世界環境中進行的大量實驗表明，動作局部性對於提高樣本效率至關重要。SGRv2 在 RLBench 任務中以僅使用 5 個示範來控制關鍵幀，並在 26 個任務中的 23 個中超越了 RVT 基準。此外，在 ManiSkill2 和 MimicGen 上進行密集控制評估時，SGRv2 的成功率是 SGR 的 2.54 倍。在真實環境中，僅通過八個示範，SGRv2 與基準模型相比，在各種任務上的成功率明顯更高。項目網站：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