High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization
This addresses the need for versatile and efficient control in robotics, enabling real-time applications like path following and time-optimal control, though it appears incremental by building on prior learning-based methods.
The paper tackles the problem of accurate high-speed robot control by introducing Optim-FKD, which uses a learned forward kinodynamic model and non-linear least squares optimization to handle various control tasks without pre-computed trajectories. Results on a scale autonomous car show it follows trajectories more accurately and finds better solutions to optimal control problems than baselines.
Accurate control of robots at high speeds requires a control system that can take into account the kinodynamic interactions of the robot with the environment. Prior works on learning inverse kinodynamic (IKD) models of robots have shown success in capturing the complex kinodynamic effects. However, the types of control problems these approaches can be applied to are limited only to that of following pre-computed kinodynamically feasible trajectories. In this paper we present Optim-FKD, a new formulation for accurate, high-speed robot control that makes use of a learned forward kinodynamic (FKD) model and non-linear least squares optimization. Optim-FKD can be used for accurate, high speed control on any control task specifiable by a non-linear least squares objective. Optim-FKD can solve for control objectives such as path following and time-optimal control in real time, without needing access to pre-computed kinodynamically feasible trajectories. We empirically demonstrate these abilities of our approach through experiments on a scale one-tenth autonomous car. Our results show that Optim-FKD can follow desired trajectories more accurately and can find better solutions to optimal control problems than baseline approaches.