Embodied-AI with large models:research and challenges
Embodied artificial intelligence(AI)driven by large-scale models is a cross-disciplinary field covering AI,robotics,and cognitive science,focusing on how to combine the perception,reasoning,and logical thinking abilities of large-scale models with embodied AI to improve the data efficiency and generalization ability of existing embodied AI frameworks such as imitation learning,reinforcement learning,and model predictive control.In recent years,with the continuous improvement of the capabilities of large-scale models and the continuous improvement of expert datasets,simulation platforms,and task sets in embodied robots,the combination of large-scale models and embodied AI will become the next wave of AI and is expected to become an important breakthrough for AI to move towards physical robots.This article focuses on the research field of embodied AI driven by large-scale foundation models(LFM),conducting systematic research,analysis,and prospects.Firstly,we review the relevant technical backgrounds of large models and embodied intelligence,as well as the existing learning frameworks of embodied intelligence.Secondly,according to how large models empower embodied intelligence,we divide the existing research into five paradigms:LFM-driven environmental perception,LFM-driven task planning,LFM-driven basic strategy,LFM-driven reward function,and LFM-driven data generation.Finally,we summarize the challenges in existing research,look forward to feasible technical routes,provide references for researchers,and further promote the national AI development strategy.
万方数据
