Active Admittance Control with Iterative Learning for General-Purpose Contact-Rich Manipulation
CoRR(2024)
摘要
Force interaction is inevitable when robots face multiple operation
scenarios. How to make the robot competent in force control for generalized
operations such as multi-tasks still remains a challenging problem. Aiming at
the reproducibility of interaction tasks and the lack of a generalized force
control framework for multi-task scenarios, this paper proposes a novel hybrid
control framework based on active admittance control with iterative learning
parameters-tunning mechanism. The method adopts admittance control as the
underlying algorithm to ensure flexibility, and iterative learning as the
high-level algorithm to regulate the parameters of the admittance model. The
whole algorithm has flexibility and learning ability, which is capable of
achieving the goal of excellent versatility. Four representative interactive
robot manipulation tasks are chosen to investigate the consistency and
generalisability of the proposed method. Experiments are designed to verify the
effectiveness of the whole framework, and an average of 98.21
improvement of RMSE is obtained relative to the traditional admittance control
as well as the model-free adaptive control, respectively.
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