Fast Visuomotor Policy for Robotic Manipulation
This work addresses the need for efficient and high-precision robotic manipulation, particularly for resource-constrained systems, though it appears incremental as it builds on existing policy frameworks.
The paper tackles the problem of robotic manipulation by proposing Energy Policy, a fast policy framework that predicts multimodal actions in a single forward pass, achieving superior performance on the MimicGen benchmark with faster inference compared to state-of-the-art methods.
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.