# Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot

arXiv: https://arxiv.org/abs/2103.04004 · HTML: https://mlsanity.com/paper/2103.04004/

## 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 10 of its 41 references that are in the map.
- First posted on arXiv 6 Mar 2021.

## 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, 10 of 10)

- [Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning](https://mlsanity.com/paper/1910.11956/) (2019)
- [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)
- [Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control](https://mlsanity.com/paper/1811.03759/) (2018)
- [Highway Long Short-Term Memory RNNs for Distant Speech Recognition](https://mlsanity.com/paper/1510.08983/) (2015)
- [Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning](https://mlsanity.com/paper/2011.06192/) (2020)
- [Time Series Motion Generation Considering Long Short-Term Motion](https://mlsanity.com/paper/1909.03755/) (2019)
- [A Geometric Perspective on Visual Imitation Learning](https://mlsanity.com/paper/2003.02768/) (2020)
- [Deep Learning Scooping Motion using Bilateral Teleoperations](https://mlsanity.com/paper/1810.10414/) (2018)
- [Interactive Trajectory Adaptation through Force-guided Bayesian Optimization](https://mlsanity.com/paper/1908.07263/) (2019)

## Abstract (arXiv)

Machine learning is now playing important role in robotic object manipulation. In addition, force control is necessary for manipulating various objects to achieve robustness against perturbations of configurations and stiffness. The author's group revealed that fast and dynamic object manipulation with force control can be obtained by bilateral control-based imitation learning. However, the method is applicable only in robots that can control torque, while it is not applicable in robots that can only follow position or velocity commands like many commercially available robots. Then, in this research, a way to implement bilateral control-based imitation learning to velocity-controlled robots is proposed. The validity of the proposed method is experimentally verified by a mopping task.