# 5620 / Step-Climbing Motion Acquisition Of Tracked Robot With Flippers Without Using Environment Information By Reinforcement Learning ## Authors Ryosuke Eto, Hayatake Sato, and Junya Yamakawa {% hint style="info" %} Paper presented at ISTVS 2024 | 21st International and 12th Asia-Pacific Regional Conference of the ISTVS\ Keywords: tracked robot; flipper; step-climbing; reinforcement learning\ {% endhint %} ## Abstract Tracked robots, which have flippers on the front, back, left, and right sides, are expected to be used for disaster investigation because of their high performance to overcome obstacles such as debris and bumps. However, it is difficult for the operator to control the robot because of its high degree of freedom. Therefore, the system that automatically controls the flippers and crawlers is required. In this study, we examined the acquired motion of a tracked robot with flippers to climb a step without using environment information by reinforcement learning. The learned motions are targeted to climb a step efficiently with a small amount of motion and to prevent the large impact when landing on a step. In order to reduce the amount of information required for the decision of the motion, only the information obtained from the internal sensors is used without the information of the surrounding environment. The agent Learned in a simulation environment using multi-body dynamics. First, the robot was trained to climb a step from the front, and the effectiveness of the acquired motion was confirmed from the results of a step climbing simulation and experiments using the trained agent. Then, the motion of the robot for climbing a step from an angle acquired by randomly changing the robot's initial orientation was clarified. *** Full paper purchase: \ ISTVS members receive three complimentary papers per year: --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://2024.istvs.org/submissions/papers/5620.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.