In this talk, we explore advanced reinforcement learning (RL) methods for robotic systems, focusing on how to employ structure to provide faster and safer learning for robotic manipulation. We envision robotic systems to operate in unstructured environments safely and reliably while mastering a wide repertoire of skills. Traditional RL methods require vast data for effective control policies, so we delve into innovative frameworks that enhance learning efficiency, safety, and adaptability. We discuss Hybrid RL methods with reachability priors for faster task learning, Model Predictive Actor-Critic (MoPAC) to reduce model bias, and techniques for safe exploration to ensure collision-free interactions. We also examine Domain Randomization via Entropy Maximization (DORAEMON) for improved sim-to-real transfer and Deep Diffusion Policy Gradient (DDiffPG) for learning diverse behaviors. These approaches collectively advance the practicality and effectiveness of RL in real-world robotic applications.
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