# Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning

arXiv: https://arxiv.org/abs/2011.06192 · HTML: https://mlsanity.com/paper/2011.06192/

## Key facts

- Cited by 11 papers in the map (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 17 of its 46 references that are in the map.
- First posted on arXiv 12 Nov 2020.

## 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, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [Sequence Level Training with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06732/) (2015)
- [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)
- [Professor Forcing: A New Algorithm for Training Recurrent Networks](https://mlsanity.com/paper/1610.09038/) (2016)
- [Learning to Poke by Poking: Experiential Learning of Intuitive Physics](https://mlsanity.com/paper/1606.07419/) (2016)
- [One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning](https://mlsanity.com/paper/1802.01557/) (2018)
- [Bridging the Gap between Training and Inference for Neural Machine Translation](https://mlsanity.com/paper/1906.02448/) (2019)
- [A Survey of Deep Learning-based Object Detection](https://mlsanity.com/paper/1907.09408/) (2019)
- [Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration](https://mlsanity.com/paper/1707.02920/) (2017)
- [Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control](https://mlsanity.com/paper/1811.03759/) (2018)
- [Cross-Database Micro-Expression Recognition: A Benchmark](https://mlsanity.com/paper/1812.07742/) (2018)
- [Rethinking Exposure Bias In Language Modeling](https://mlsanity.com/paper/1910.11235/) (2019)
- [Deep Learning Scooping Motion using Bilateral Teleoperations](https://mlsanity.com/paper/1810.10414/) (2018)

## Built on by (most cited first, 11 of 11)

- [Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformer](https://mlsanity.com/paper/2401.17698/) (2024)
- [Imitation Learning for Variable Speed Contact Motion for Operation up to Control Bandwidth](https://mlsanity.com/paper/2102.10283/) (2021)
- [Tri-Modal Motion Retrieval by Learning a Joint Embedding Space](https://mlsanity.com/paper/2403.00691/) (2024)
- [An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications](https://mlsanity.com/paper/2203.08619/) (2022)
- [Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed](https://mlsanity.com/paper/2206.10289/) (2022)
- [ILBiT: Imitation Learning for Robot Using Position and Torque Information based on Bilateral Control with Transformer](https://mlsanity.com/paper/2401.16653/) (2024)
- [A New Autoregressive Neural Network Model with Command Compensation for Imitation Learning Based on Bilateral Control](https://mlsanity.com/paper/2103.08879/) (2021)
- [Imitation learning for variable speed motion generation over multiple actions](https://mlsanity.com/paper/2103.06466/) (2021)
- [MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Clouds](https://mlsanity.com/paper/2502.18745/) (2025)
- [Temporal horizons in forecasting: a performance-learnability trade-off](https://mlsanity.com/paper/2506.03889/) (2025)
- [Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot](https://mlsanity.com/paper/2103.04004/) (2021)

## Abstract (arXiv)

Robots that can execute various tasks automatically on behalf of humans are becoming an increasingly important focus of research in the field of robotics. Imitation learning has been studied as an efficient and high-performance method, and imitation learning based on bilateral control has been proposed as a method that can realize fast motion. However, because this method cannot implement autoregressive learning, this method may not generate desirable long-term behavior. Therefore, in this paper, we propose a method of autoregressive learning for bilateral control-based imitation learning. A new neural network model for implementing autoregressive learning is proposed. In this study, three types of experiments are conducted to verify the effectiveness of the proposed method. The performance is improved compared to conventional approaches; the proposed method has the highest rate of success. Owing to the structure and autoregressive learning of the proposed model, the proposed method can generate the desirable motion for successful tasks and have a high generalization ability for environmental changes.