# Time Series Motion Generation Considering Long Short-Term Motion

arXiv: https://arxiv.org/abs/1909.03755 · HTML: https://mlsanity.com/paper/1909.03755/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [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)
- [Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learning](https://mlsanity.com/paper/2311.09555/) (2023)
- [An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications](https://mlsanity.com/paper/2203.08619/) (2022)
- [ILBiT: Imitation Learning for Robot Using Position and Torque Information based on Bilateral Control with Transformer](https://mlsanity.com/paper/2401.16653/) (2024)
- [DABI: Evaluation of Data Augmentation Methods Using Downsampling in Bilateral Control-Based Imitation Learning with Images](https://mlsanity.com/paper/2410.04370/) (2024)
- [A New Autoregressive Neural Network Model with Command Compensation for Imitation Learning Based on Bilateral Control](https://mlsanity.com/paper/2103.08879/) (2021)
- [Bi-HIL: Bilateral Control-Based Multimodal Hierarchical Imitation Learning via Subtask-Level Progress Rate and Keyframe Memory for Long-Horizon Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2603.13315/) (2026)
- [Imitation Learning Based on Bilateral Control for Human-Robot Cooperation](https://mlsanity.com/paper/1909.13018/) (2019)
- [Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot](https://mlsanity.com/paper/2103.04004/) (2021)

## Abstract (arXiv)

Various adaptive abilities are required for robots interacting with humans in daily life. It is difficult to design adaptive algorithms manually; however, by using end-to-end machine learning, labor can be saved during the design process. In our previous research, a task requiring force adjustment was achieved through imitation learning that considered position and force information using a four-channel bilateral control. Unfortunately, tasks that include long-term (slow) motion are still challenging. Furthermore, during system identification, there is a method known as the multi-decimation (MD) identification method. It separates lower and higher frequencies, and then identifies the parameters characterized at each frequency. Therefore, we proposed utilizing machine learning to take advantage of the MD method to infer short-term and long-term (high and low frequency, respectively) motion. In this paper, long-term motion tasks such as writing a letter using a pen fixed on a robot are discussed. We found differences in suitable sampling periods between position and force information. The validity of the proposed method was then experimentally verified, showing the importance of long-term inference with adequate sampling periods.