deep reinforcement learning

The advances in wheeled-legged robots in recent years have led to their proliferation in human environments. However, these robots still face significant challenges regarding non-flat terrain. Stair climbing is particularly difficult for these robots and restricts their functionality in human facilities. We implemented a goal conditioned deep reinforcement learning algorithm to develop a position-based controller that allows Wheely, a wheeled-legged quadrupedal robot, to blindly climb stairs up to a record breaking 10 cm in height or 33% of Wheely’s maximum body height. By training the reinforcement learning algorithm on a single environment each time, we create specialized policies that excel at their own specific terrain type. Through the independent training, we create a controller with a mixture of experts architecture that does not need to compromise between different environments, leading to a more optimal policy for each scenario. To maintain robustness, a selector neural network selects the policy based on past observations, allowing the robot to function independently in dynamic environments. 

Published in: 2024 International Conference on Advanced Robotics and Intelligent Systems (ARIS)

Date of Conference: 22-24 August 2024

Conference Location: Taipei, Taiwan

Publisher: IEEE