Deep Predictive Learning: Motion Learning Concept inspired by Cognitive Robotics

TL;DR

This paper introduces Deep Predictive Learning, a concept inspired by cognitive robotics that enables robots to predict and adapt to sensorimotor dynamics in real time, reducing the need for extensive data collection and trial-and-error.

Contribution

It proposes a novel motion learning framework based on predictive coding, allowing robots to tolerate prediction errors and perform diverse tasks with limited data.

Findings

Successful real robot applications demonstrated adaptability

Reduced data collection and trial-and-error in robot training

Enhanced task versatility through embedded motion dynamics

Abstract

Bridging the gap between motion models and reality is crucial by using limited data to deploy robots in the real world. Deep learning is expected to be generalized to diverse situations while reducing feature design costs through end-to-end learning for environmental recognition and motion generation. However, data collection for model training is costly, and time and human resources are essential for robot trial-and-error with physical contact. We propose "Deep Predictive Learning," a motion learning concept that predicts the robot's sensorimotor dynamics, assuming imperfections in the prediction model. The predictive coding theory inspires this concept to solve the above problems. It is based on the fundamental strategy of predicting the near-future sensorimotor states of robots and online minimization of the prediction error between the real world and the model. Based on the acquired sensor information, the robot can adjust its behavior in real time, thereby tolerating the difference between the learning experience and reality. Additionally, the robot was expected to perform a wide range of tasks by combining the motion dynamics embedded in the model. This paper describes the proposed concept, its implementation, and examples of its applications in real robots. The code and documents are available at: https://ogata-lab.github.io/eipl-docs