Embodied Environment

Embodied environments research focuses on integrating artificial intelligence agents with physical or simulated worlds to improve their reasoning and decision-making capabilities. Current research emphasizes using reinforcement learning and large language models, often combined with techniques like active perception and physically-based simulation, to enable agents to learn from interaction and adapt to complex, dynamic environments. This approach is proving valuable for advancing robotics, particularly in challenging domains like surgical robotics and disaster response, as well as for improving the grounding and robustness of AI models more generally. The ultimate goal is to create AI systems that can effectively learn and operate in the real world, bridging the gap between symbolic reasoning and physical interaction.