From LLM-Generated Specifications to Learned Quadruped Locomotion
Quadruped robot locomotion policies are often trained using reinforcement learning, which in turn relies heavily on hand-crafted reward functions. Designing reward functions requires substantial manual engineering, and it is often unclear which local rewards will induce the desired global behavior. Shaped rewards from...
Merve Atasever, Keyan Azbijari, Cagan Bakirci et al.
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