提出一种高效机器人操作策略，单次推理即可生成多模式动作。
Fast Visuomotor Policy for Robotic Manipulation
- 用能量评分作为学习目标，实现多模式动作预测
- 在模拟与真实场景中均达到领先性能，推理速度更快
- 适合高频率、资源受限的机器人系统应用
我们提出一种名为 Energy Policy 的快速高效机器人操作策略框架，适用于高频任务和资源受限系统。与现有方法不同，该框架在单次前向传播中即可原生预测多模态动作，实现高速高精度操作。其核心由两部分构成：首先采用能量评分作为学习目标，以支持多模态动作建模；其次引入能量MLP实现该目标，同时保持架构简洁高效。我们在模拟环境和真实机器人任务中进行了全面实验。结果表明，Energy Policy 在性能上匹配或超越当前最优方法，同时显著降低计算开销。尤其在 MimicGen 基准测试中，其推理速度优于现有方法且表现更优。
原文摘要 · Abstract (English)
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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