EnergyAction: 从单臂到双臂操作的能量模型框架
EnergyAction: Unimanual to Bimanual Composition with Energy-Based Models
AI总结：
本文提出EnergyAction框架，通过能量模型将单臂操作策略迁移至双臂任务，解决双臂操作中数据不足和动作协调难题，实验显示其在模拟和真实任务中表现优异。
摘要：
近期单臂操作策略在多样机器人任务中取得显著成功，但扩展至双臂操作面临双臂示范数据不足和双臂动作协调复杂性的挑战。现有方法或依赖大量双臂数据，或无法有效利用预训练单臂策略。为此，我们提出EnergyAction，一种通过能量模型（EBMs）将单臂策略组合到双臂任务的新型框架。具体而言，我们的方法包含三个关键创新：首先，将单臂策略建模为EBMs，并利用其组合性质来组合左右臂动作，使单臂策略融合为双臂策略；其次，引入基于能量约束的时间-空间协调机制，确保生成的双臂动作在时间上一致且空间上可行；第三，提出两种不同的能量感知去噪策略，根据动作质量评估动态调整去噪步骤。这些策略确保生成高质量的动作，同时保持优于固定步长去噪方法的计算效率。实验结果表明，EnergyAction有效将单臂知识迁移至双臂任务，在模拟和真实任务中均取得优异表现，且在少量双臂数据下即可实现。
英文摘要：
Recent advances in unimanual manipulation policies have achieved remarkable success across diverse robotic tasks through abundant training data and well-established model architectures. However, extending these capabilities to bimanual manipulation remains challenging due to the lack of bimanual demonstration data and the complexity of coordinating dual-arm actions. Existing approaches either rely on extensive bimanual datasets or fail to effectively leverage pre-trained unimanual policies. To address this limitation, we propose \textbf{EnergyAction}, a novel framework that compositionally transfers unimanual manipulation policies to bimanual tasks through the Energy-Based Models (EBMs). Specifically, our method incorporates three key innovations. First, we model individual unimanual policies as EBMs and leverage their compositional properties to compose left and right arm actions, enabling the fusion of unimanual policies into a bimanual policy. Second, we introduce an energy-based temporal-spatial coordination mechanism through energy constraints, ensuring the generated bimanual actions are both temporal coherence and spatial feasibility. Third, we propose two different energy-aware denoising strategies that dynamically adapt denoising steps based on action quality assessment. These strategies ensure the generation of high-quality actions while maintaining superior computational efficiency compared to fixed-step denoising approaches. Experimental results demonstrate that EnergyAction effectively transfers unimanual knowledge to bimanual tasks, achieving superior performance on both simulated and real-world tasks with minimal bimanual data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
