A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots

Abstract

Traditional dynamic models of continuum robots are in general computationally expensive and not suitable for real-time control. Recent approaches using learning-based methods to approximate the dynamic model of continuum robots for control have been promising, although real data hungry-which may cause potential damage to robots and be time consuming-and getting poorer performance when trained with simulation data only. This paper presents a modelbased learning framework for continuum robot closed-loop control that, by combining simulation and real data, shows to require only 100 real data to outperform a real-data-only controller trained using up to 10000 points. The introduced data-efficient framework with three control policies has utilized a Gaussian process regression (GPR) and a recurrent neural network (RNN). Control policy A uses a GPR model and a RNN trained in simulation to optimize control outputs for simulated targets; control policy B retrains the RNN in policy A with data generated from the GPR model to adapt to real robot physics; control policy C utilizes policy A and B to form a hybrid policy. Using a continuum robot with soft spines, we show that our approach provides an efficient framework to bridge the sim-toreal gap in model-based learning for continuum robots.

Control of soft robots with inertial dynamics

“…These models tend to be valid only in a neighborhood around the equilibrium point where the system has been linearized (14). Controllers based on ROM models have been applied to soft robots in the past, but these have yet to achieve the real-time control of fast, inertial motions (17)(18)(19).…”

“…In the ML modeling of soft robot dynamical behaviors, many neural net-based approaches exist. Most of this work focuses on the development of predictors using neural nets such as long short-term memory (20,21) or recurrent neural networks (17,19). These methodologies generate highly accurate predictors of the dynamics.…”