# Imitation Learning Based on Bilateral Control for Human-Robot Cooperation

arXiv: https://arxiv.org/abs/1909.13018 · HTML: https://mlsanity.com/paper/1909.13018/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 28 Sep 2019.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning](https://mlsanity.com/paper/1802.01557/) (2018)
- [Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration](https://mlsanity.com/paper/1707.02920/) (2017)
- [Real-Time Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1412.3128/) (2014)
- [Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control](https://mlsanity.com/paper/1811.03759/) (2018)
- [Time Series Motion Generation Considering Long Short-Term Motion](https://mlsanity.com/paper/1909.03755/) (2019)
- [Improving dual-arm assembly by master-slave compliance](https://mlsanity.com/paper/1902.07007/) (2019)

## Abstract (arXiv)

Robots are required to autonomously respond to changing situations. Imitation learning is a promising candidate for achieving generalization performance, and extensive results have been demonstrated in object manipulation. However, cooperative work between humans and robots is still a challenging issue because robots must control dynamic interactions among themselves, humans, and objects. Furthermore, it is difficult to follow subtle perturbations that may occur among coworkers. In this study, we find that cooperative work can be accomplished by imitation learning using bilateral control. Thanks to bilateral control, which can extract response values and command values independently, human skills to control dynamic interactions can be extracted. Then, the task of serving food is considered. The experimental results clearly demonstrate the importance of force control, and the dynamic interactions can be controlled by the inferred action force.