Practice Makes Perfect: an iterative approach to achieve precise tracking for legged robots

TL;DR

This paper introduces an iterative learning control method enabling legged robots to improve trajectory tracking efficiently through self-learning, reducing computational load and data requirements, demonstrated on a simulated robot performing precise pronking.

Contribution

The paper presents a novel iterative learning control approach that allows legged robots to learn from their own mistakes with minimal trials, creating a torque library for efficient trajectory tracking.

Findings

Enhanced trajectory tracking accuracy in simulation

Reduced computational and data requirements

Successful pronking at various speeds

Abstract

Precise trajectory tracking for legged robots can be challenging due to their high degrees of freedom, unmodeled nonlinear dynamics, or random disturbances from the environment. A commonly adopted solution to overcome these challenges is to use optimization-based algorithms and approximate the system with a simplified, reduced-order model. Additionally, deep neural networks are becoming a more promising option for achieving agile and robust legged locomotion. These approaches, however, either require large amounts of onboard calculations or the collection of millions of data points from a single robot. To address these problems and improve tracking performance, this paper proposes a method based on iterative learning control. This method lets a robot learn from its own mistakes by exploiting the repetitive nature of legged locomotion within only a few trials. Then, a torque library is created as a lookup table so that the robot does not need to repeat calculations or learn the same skill over and over again. This process resembles how animals learn their muscle memories in nature. The proposed method is tested on the A1 robot in a simulated environment, and it allows the robot to pronk at different speeds while precisely following the reference trajectories without heavy calculations.