使用状态扩散模型和逆动力学模型学习协调的双臂操作策略
Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models
AI总结：
本文提出将人类操作策略的预测特性引入机器人模仿学习，通过解耦任务相关状态转换与逆动力学建模，利用扩散模型预测未来状态并结合逆动力学模型计算动作，有效解决了双臂协调操作问题，在多模态目标和真实世界设置中均优于现有方法。
摘要：
在执行洗衣等任务时，人类会自然地协调双手来操作物体，并预判其动作将如何改变衣物的状态。然而，在机器人技术中实现这种协调仍然具有挑战性，因为需要对物体运动进行建模、预测未来状态并生成精确的双臂动作。在这项工作中，我们通过将人类操作策略的预测特性引入机器人模仿学习来解决这些挑战。具体而言，我们将与任务相关的状态转换与特定于智能体的逆动力学建模解耦，以实现有效的双臂协调。使用演示数据集，我们训练了一个扩散模型，根据历史观测预测未来状态，设想场景如何演变。然后，我们使用逆动力学模型来计算实现预测状态的机器人动作。我们的核心见解是，对物体运动进行建模有助于学习双臂协调操作任务的策略。我们在多种模拟和真实世界的操作设置中评估了我们的框架，包括多模态目标配置、双臂操作、可变形物体和多物体设置，我们发现它始终优于最先进的从状态到动作的映射策略。我们的方法展现了非凡的能力，能够导航多模态目标配置和动作分布，在不同控制模式下保持稳定性，并合成比演示数据集中存在的更广泛的行为。
英文摘要：
When performing tasks like laundry, humans naturally coordinate both hands to manipulate objects and anticipate how their actions will change the state of the clothes. However, achieving such coordination in robotics remains challenging due to the need to model object movement, predict future states, and generate precise bimanual actions. In this work, we address these challenges by infusing the predictive nature of human manipulation strategies into robot imitation learning. Specifically, we disentangle task-related state transitions from agent-specific inverse dynamics modeling to enable effective bimanual coordination. Using a demonstration dataset, we train a diffusion model to predict future states given historical observations, envisioning how the scene evolves. Then, we use an inverse dynamics model to compute robot actions that achieve the predicted states. Our key insight is that modeling object movement can help learning policies for bimanual coordination manipulation tasks. Evaluating our framework across diverse simulation and real-world manipulation setups, including multimodal goal configurations, bimanual manipulation, deformable objects, and multi-object setups, we find that it consistently outperforms state-of-the-art state-to-action mapping policies. Our method demonstrates a remarkable capacity to navigate multimodal goal configurations and action distributions, maintain stability across different control modes, and synthesize a broader range of behaviors than those present in the demonstration dataset.
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。
