4 accepted papers
Optimal Control for legged robots has gone through a paradigm shift from position-based to torque-based control, owing to the latter’s compliant and robust nature. In parallel to this shift, the community has also turned to Deep Reinforcement Learning (DRL) as a promising approach to directly learn…
Efficient and scalable adaptive traffic signal control is crucial in reducing congestion, maximizing through-put, and improving mobility experience in ever-expanding cities. Recent advances in multi-agent reinforcement learning (MARL) with parameter sharing have significantly improved the adaptive o…
Animals possess a remarkable ability to navigate challenging terrains, achieved through the interplay of various pathways between the brain, central pattern generators (CPGs) in the spinal cord, and musculoskeletal system. Traditional bioinspired control frameworks often rely on a singular control p
The emergence of neural networks has revolutionized the field of motion synthesis. Yet, learning to unconditionally synthesize motions from a given distribution remains challenging, especially when the motions are highly diverse. In this work, we present MoDi -- a generative model trained in an unsu…