面向机器人操作的高速视觉运动策略
Fast Visuomotor Policy for Robotic Manipulation
AI总结：
提出Energy Policy框架，通过能量分数和能量MLP实现单次前向传播预测多模态动作，在模拟和真实任务中匹配或超越最先进方法，并显著降低计算开销。
摘要：
我们提出了一种快速且有效的机器人操作策略框架，名为Energy Policy，专为高频机器人任务和资源受限系统设计。与现有机器人策略不同，Energy Policy在单次前向传播中原生预测多模态动作，从而在高速下实现高精度操作。该框架基于两个核心组件构建。首先，我们采用能量分数作为学习目标，以促进多模态动作建模。其次，我们引入了一个能量MLP来实现所提出的目标，同时保持架构简单高效。我们在模拟环境和真实世界机器人任务中进行了全面实验，以评估Energy Policy的有效性。结果表明，Energy Policy在显著降低计算开销的同时，匹配或超越了最先进的操作方法的性能。值得注意的是，在MimicGen基准上，Energy Policy以更快的推理速度实现了优于现有方法的性能。
英文摘要：
We present a fast and effective policy framework for robotic manipulation, named Energy Policy, designed for high-frequency robotic tasks and resource-constrained systems. Unlike existing robotic policies, Energy Policy natively predicts multimodal actions in a single forward pass, enabling high-precision manipulation at high speed. The framework is built upon two core components. First, we adopt the energy score as the learning objective to facilitate multimodal action modeling. Second, we introduce an energy MLP to implement the proposed objective while keeping the architecture simple and efficient. We conduct comprehensive experiments in both simulated environments and real-world robotic tasks to evaluate the effectiveness of Energy Policy. The results show that Energy Policy matches or surpasses the performance of state-of-the-art manipulation methods while significantly reducing computational overhead. Notably, on the MimicGen benchmark, Energy Policy achieves superior performance with at a faster inference compared to existing approaches.
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