Learning Task Constraints from Demonstration for Hybrid Force/Position Control
Learning Task Constraints from Demonstration for Hybrid Force/Position Control
复制标题
从混合力/位置控制演示中学习任务约束
DOI：
10.1109/humanoids43949.2019.9035013
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Tucker Hermans
中科院分区：
文献类型：
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作者：
Adam Conkey;Tucker Hermans
我们提出了一种新的方法学习混合力/位置控制从示范。我们使用笛卡尔动态运动基元学习与所需力方向对齐的动态约束框架。与利用固定约束框架的方法相比，我们的方法很容易适应随着时间的推移快速变化的任务约束的任务。我们在任何给定时间只激活一个自由度进行力控制，确保运动始终可能与所需力的方向正交。由于我们利用演示的力来学习约束框架，因此我们能够补偿仅从演示的运动学运动学习的方法未检测到的力，例如末端执行器和接触表面之间的摩擦力。我们还提出了新的扩展的动态运动原语框架，鼓励强大的过渡，从自由空间运动到接触运动，尽管环境的不确定性。我们结合了力反馈和动态切换的目标，以减少施加到环境中的力，并在实现力控制的同时保持稳定的接触。我们的方法表现出低冲击力的接触和低稳态跟踪误差。
We present a novel method for learning hybrid force/position control from demonstration. We learn a dynamic constraint frame aligned to the direction of desired force using Cartesian Dynamic Movement Primitives. In contrast to approaches that utilize a fixed constraint frame, our approach easily accommodates tasks with rapidly changing task constraints over time. We activate only one degree of freedom for force control at any given time, ensuring motion is always possible orthogonal to the direction of desired force. Since we utilize demonstrated forces to learn the constraint frame, we are able to compensate for forces not detected by methods that learn only from demonstrated kinematic motion, such as frictional forces between the end-effector and contact surface. We additionally propose novel extensions to the Dynamic Movement Primitive framework that encourage robust transition from free-space motion to in-contact motion in spite of environment uncertainty. We incorporate force feedback and a dynamically shifting goal to reduce forces applied to the environment and retain stable contact while enabling force control. Our methods exhibit low impact forces on contact and low steady-state tracking error.