论文标题
深层模型预测性变量阻抗控制
Deep Model Predictive Variable Impedance Control
论文作者
论文摘要
通过改变肌肉僵硬来适应符合性的能力对于人类灵巧的操纵技巧至关重要。在机器人电机控制中合并的合规性对于执行具有人级灵活性的现实力量相互作用任务至关重要。这项工作为合规的机器人操作提供了深层的模型预测性变量阻抗控制器，该阻抗控制将可变阻抗控制与模型预测控制（MPC）相结合。使用探索策略最大化信息增益的探索策略，可以学习一个通用机器人操纵器的笛卡尔阻抗模型。该模型在MPC框架内使用，以适应低级变量阻抗控制器的阻抗参数，以实现针对不同操纵任务的所需合规性行为，而无需进行任何重新培训或填充。使用Franka Emika Panda机器人操纵器在模拟和实际实验中运行的操作，使用Franka Emika Panda机器人操纵器评估深层模型预测性变量阻抗控制方法。将所提出的方法与无模型和基于模型的强化方法进行了比较，以可变阻抗控制，以进行任务和性能之间的可传递性。
The capability to adapt compliance by varying muscle stiffness is crucial for dexterous manipulation skills in humans. Incorporating compliance in robot motor control is crucial to performing real-world force interaction tasks with human-level dexterity. This work presents a Deep Model Predictive Variable Impedance Controller for compliant robotic manipulation which combines Variable Impedance Control with Model Predictive Control (MPC). A generalized Cartesian impedance model of a robot manipulator is learned using an exploration strategy maximizing the information gain. This model is used within an MPC framework to adapt the impedance parameters of a low-level variable impedance controller to achieve the desired compliance behavior for different manipulation tasks without any retraining or finetuning. The deep Model Predictive Variable Impedance Control approach is evaluated using a Franka Emika Panda robotic manipulator operating on different manipulation tasks in simulations and real experiments. The proposed approach was compared with model-free and model-based reinforcement approaches in variable impedance control for transferability between tasks and performance.
