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Institute for AI Industry Research
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Modern data-driven decision-making methods, such as imitation learning (IL) and reinforcement learning (RL), have achieved great success in solving many complex tasks. However, these methods often suffer from serious control instability and robustness issues when applied in real-world applications such as robotics and...
ODEWorld is constructed, a continuous-time latent world model that is both efficient and versatile and successfully reconciles planning-conducive dynamics abstraction with visual realism, excelling in both video generation and robotic control.
This work proposes PRISM, a new multi-reward RL framework built upon the idea of policy-space decomposition and composition, which alleviates the potential conflict during multi-reward policy optimization, while enabling controllability during inference by flexible policy composition.
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