We have witnessed very impressive progress in large-scale and multi-modal foundation/generative models in recent months. We believe making use of such models in a reasonable way could really enable robots to acquire diverse skills. In the recent white paper, we discussed how we can automate the whole pipeline for robotic skill learning, from low-level asset generation, texture generation, to high-level scene, task and reward generation. Once we obtain such a diverse suite of tasks and environments, we can offload policy training to RL of trajectory optimization to solve all the generated low-level tasks, and finally distill all the learned closed-loop policy into a unified policy model. Apart from scaling up in simulation, how to use real-world data more effectively is another promising research direction. Real-world human demonstrations can be found at scale, but typically only provides spatial trajectory information and doesn’t advise how to recover from error compounding during policy rollout. Motivated by these observations and thoughts, this workshop seeks to discuss and compare the advantages and limitations of different paradigms for scaling up skill learning - scaling up simulation, leveraging generative models, exploiting unstructured passive human demonstration, scaling up structured demonstration collection in the real world, etc.
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