Conditional Variational Auto Encoder Based Dynamic Motion for Multi-task Imitation Learning

TL;DR

This paper introduces a novel framework combining DMP and CVAE to enable multi-task robotic motion learning with limited data, achieving high success rates in reaching and pushing tasks through simulation validation.

Contribution

It proposes a CVAE-based dynamic motion generation framework that improves multi-task learning and generalization in robotic tasks with limited training data.

Findings

Achieves 100% success rate on pushing and reaching tasks in simulation.

Effectively generalizes to new goal positions.

Reduces data requirements compared to existing deep learning methods.

Abstract

The dynamic motion primitive-based (DMP) method is an effective method of learning from demonstrations. However, most of the current DMP-based methods focus on learning one task with one module. Although, some deep learning-based frameworks can learn to multi-task at the same time. However, those methods require a large number of training data and have limited generalization of the learned behavior to the untrained state. In this paper, we propose a framework that combines the advantages of the traditional DMP-based method and conditional variational auto-encoder (CVAE). The encoder and decoder are made of a dynamic system and deep neural network. Deep neural networks are used to generate torque conditioned on the task ID. Then, this torque is used to create the desired trajectory in the dynamic system based on the final state. In this way, the generated tractory can adjust to the new goal position. We also propose a finetune method to guarantee the via-point constraint. Our model is trained on the handwriting number dataset and can be used to solve robotic tasks -- reaching and pushing directly. The proposed model is validated in the simulation environment. The results show that after training on the handwriting number dataset, it achieves a 100\% success rate on pushing and reaching tasks.