This&That: Language-Gesture Controlled Video Generation for Robot Planning
<|reference_start|>This&That: Language-Gesture Controlled Video Generation for Robot Planning: We propose a robot learning method for communicating, planning, and executing a wide range of tasks, dubbed This&That. We achieve robot planning for general tasks by leveraging the power of video generative models trained on internet-scale data containing rich physical and semantic context. In this work, we tackle three fundamental challenges in video-based planning: 1) unambiguous task communication with simple human instructions, 2) controllable video generation that respects user intents, and 3) translating visual planning into robot actions. We propose language-gesture conditioning to generate videos, which is both simpler and clearer than existing language-only methods, especially in complex and uncertain environments. We then suggest a behavioral cloning design that seamlessly incorporates the video plans. This&That demonstrates state-of-the-art effectiveness in addressing the above three challenges, and justifies the use of video generation as an intermediate representation for generalizable task planning and execution. Project website: https://cfeng16.github.io/this-and-that/.<|reference_end|>
@article{wang2024this&that:,
    title={This&That: Language-Gesture Controlled Video Generation for Robot
  Planning},
    author={Boyang Wang, Nikhil Sridhar, Chao Feng, Mark Van der Merwe, Adam
  Fishman, Nima Fazeli, Jeong Joon Park},
    journal={arXiv preprint arXiv:2407.05530},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05530},
    primaryClass={cs.RO cs.AI cs.CV}
}