让双臂机器人自适应调节力度，更稳更灵巧地操作大件物体。
DA-VIL: Adaptive Dual-Arm Manipulation with Reinforcement Learning and Variable Impedance Control
- 结合强化学习与梯度优化，动态调节机械臂阻抗参数。
- 在多种质量和形状的大型物体上实现更优轨迹跟踪性能。
- 适合需要双臂协作的复杂操作场景，如装配与搬运。
双臂协同操作是机器人领域的重要研究方向，对抓取大型物体、组件装配及类人交互等复杂任务至关重要。然而，实现高效双臂操作面临协调精度、动态适应性以及力控管理等挑战。本文提出一种新框架，融合基于环境反馈的策略学习与基于梯度的优化方法，自动学习控制所需的增益参数，使机器人能根据任务需求动态调节阻抗，提升操作稳定性和灵巧性。我们在包含多种质量与几何形状的大尺寸物体上测试该方法，对比三种现有双臂控制策略，结果表明本方法在轨迹跟踪性能上表现更优。
原文摘要 · Abstract (English)
Dual-arm manipulation is an area of growing interest in the robotics community. Enabling robots to perform tasks that require the coordinated use of two arms, is essential for complex manipulation tasks such as handling large objects, assembling components, and performing human-like interactions. However, achieving effective dual-arm manipulation is challenging due to the need for precise coordination, dynamic adaptability, and the ability to manage interaction forces between the arms and the objects being manipulated. We propose a novel pipeline that combines the advantages of policy learning based on environment feedback and gradient-based optimization to learn controller gains required for the control outputs. This allows the robotic system to dynamically modulate its impedance in response to task demands, ensuring stability and dexterity in dual-arm operations. We evaluate our pipeline on a trajectory-tracking task involving a variety of large, complex objects with different masses and geometries. The performance is then compared to three other established methods for controlling dual-arm robots, demonstrating superior results.
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