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.
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SGRv2 是一种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，其中关键的归纳偏置 - 动作局部性假设认为机器人的动作主要受目标物体及其与局部环境的交互影响。在模拟和真实环境中进行的广泛实验证明了动作局部性对提高样本效率的重要性。SGRv2 在 RLBench 任务中使用仅 5 个演示并在 26 个任务中超过 RVT 基线，同时在 ManiSkill2 和 MimicGen 中的稠密控制下与 SGR 相比，成功率提高了 2.54 倍。在真实环境中，仅使用八个演示，SGRv2 可以以显著更高的成功率执行各种任务，相比基线模型。