Learning Novel Skills from Language-Generated Demonstrations
This addresses the challenge of intuitive and intelligent skill learning for robots, though it appears incremental as it builds on existing vision-language and video diffusion models.
The study tackled the problem of high labor costs and safety risks in robot skill acquisition by proposing DemoGen, a framework that generates demonstration videos from natural language instructions, enabling robots to achieve an accomplishment rate three times higher on novel tasks.
Robots are increasingly deployed across diverse domains to tackle tasks requiring novel skills. However, current robot learning algorithms for acquiring novel skills often rely on demonstration datasets or environment interactions, resulting in high labor costs and potential safety risks. To address these challenges, this study proposes DemoGen, a skill-learning framework that enables robots to acquire novel skills from natural language instructions. DemoGen leverages the vision-language model and the video diffusion model to generate demonstration videos of novel skills, which enabling robots to learn new skills effectively. Experimental evaluations in the MetaWorld simulation environments demonstrate the pipeline's capability to generate high-fidelity and reliable demonstrations. Using the generated demonstrations, various skill learning algorithms achieve an accomplishment rate three times the original on novel tasks. These results highlight a novel approach to robot learning, offering a foundation for the intuitive and intelligent acquisition of novel robotic skills. (Project website: https://aoqunjin.github.io/LNSLGD/)