Skip to main content Skip to main navigation


Deriving Rewards for Reinforcement Learning from Symbolic Behaviour Descriptions of Bipedal Walking

Daniel Harnack; Christoph Lüth; Lukas Groß; Shivesh Kumar; Frank Kirchner
In: 62nd IEEE Conference on Decision and Control (CDC). IEEE Conference on Decision and Control (CDC-2023), December 13-15, Marina Bay Sands, Singapore, TBA. 2023.


Generating physical movement behaviours from their symbolic description is a long-standing challenge in artificial intelligence (AI) and robotics, requiring insights into numerical optimization methods as well as into formalizations from symbolic AI and reasoning. In this paper, a novel approach to finding a reward function from a symbolic description is proposed. The intended system behaviour is modelled as a hybrid automaton, which reduces the system state space to allow more efficient reinforcement learning. The approach is applied to bipedal walking, by modelling the walking robot as a hybrid automaton over state space orthants, and used with the compass walker to derive a reward that incentivizes following the hybrid automaton cycle. As a result, training times of reinforcement learning controllers are reduced while final walking speed is increased. The approach can serve as a blueprint how to generate reward functions from symbolic AI and reasoning.


Weitere Links