Real-time Coordinated Motion Generation: A Hierarchical Deep Predictive Learning Model for Bimanual Tasks

Abstract

Robots that autonomously operate in human living environments require the ability to adapt to unpredictable changes and flexibly handle a variety of tasks. Particularly, coordinated bimanual motions are essential for enabling tasks that are difficult with just one hand, such as grasping bulky objects, transporting heavy loads, and precision work. Traditional methods of generating robot motions typically involve executing pre-programmed motions, making it challenging to adapt to complex and unpredictable environmental changes. To address this issue, our research focuses on generating diverse motions that can flexibly adapt to environmental changes based on Deep Predictive Learning from a small amount of real-world data. Previous Deep Predictive Learning models have generated the motions of a robot’s left and right arms by a single LSTM, making it difficult to operate them independently. Therefore, we propose a new Hierarchical Deep Predictive Learning model specialized for generating coordinated bimanual motions. This model comprises three components: a Left-LSTM, which learns the body and visual information on the robot’s left side, a Right-LSTM that performs a similar function for the right side, and a Union-LSTM which integrates this information at a higher level. To verify the effectiveness of the proposed model, we conducted bimanual grasping experiments with multiple different objects using two different robots. The experimental results showed that independent of hardware, our model demonstrated a higher success rate compared to the traditional approach, indicating its enhanced capability in coordinating bimanual motions.