Abstract
One of the largest challenges in the deployment of legged robots in the real world is deriving effective general gaits. In this paper, we present BeeTLe, which is a framework that enables terrain aware locomotion without the need for dedicated terrain sensors. BeeTLe is realised as a multi-expert policy Reinforcement Learning (RL) algorithm. This enables multiple gaits, applicable to different surface types, to be stored and shared in a single policy. Sensor free terrain awareness is incorporated using a Recurrent Neural Network (RNN) to infer surface type purely from actuator positions over time. The RNN achieves an accuracy of 94% in terrain identification out of 8 possible options. We demonstrate that BeeTLe achieves a greater performance than the baselines across a series of challenges including: the traversal of a flat plane, a tilted plane, a sequence of tilted planes and geometry modelling a natural hilly terrain. This is despite not seeing the sequence of tilted planes and the natural hilly terrain during training.The code, policy and simulated environments are available at: https://gitlab.surrey.ac.uk/rf00350/BeeTLe