# Efficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge

arXiv: https://arxiv.org/abs/2002.11573 · HTML: https://mlsanity.com/paper/2002.11573/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [SoftGPT: Learn Goal-oriented Soft Object Manipulation Skills by Generative Pre-trained Heterogeneous Graph Transformer](https://mlsanity.com/paper/2306.12677/) (2023)
- [ReVoLT: Relational Reasoning and Voronoi Local Graph Planning for Target-driven Navigation](https://mlsanity.com/paper/2301.02382/) (2023)
- [Learning Goal-oriented Bimanual Dough Rolling Using Dynamic Heterogeneous Graph Based on Human Demonstration](https://mlsanity.com/paper/2410.22355/) (2024)

## Abstract (arXiv)

Compared to rigid robots that are generally studied in reinforcement learning, the physical characteristics of some sophisticated robots such as soft or continuum robots are higher complicated. Moreover, recent reinforcement learning methods are data-inefficient and can not be directly deployed to the robot without simulation. In this paper, we propose an efficient reinforcement learning method based on inexplicit prior knowledge in response to such problems. We first corroborate the method by simulation and employed directly in the real world. By using our method, we can achieve active visual tracking and distance maintenance of a tendon-driven robot which will be critical in minimally invasive procedures. Codes are available at https://github.com/Skylark0924/TendonTrack.