Robotics: Science and Systems XIX

CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory

Nur Muhammad (Mahi)Shafiullah, Chris Paxton, Lerrel Pinto, Soumith Chintala, Arthur Szlam


We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks, such as segmentation, instance identification, semantic search over space, and view localization. CLIP-Fields learns a mapping from spatial locations to semantic embedding vectors. Importantly, we show that this mapping can be trained with supervision coming only from web-image and web-text trained models such as CLIP, Detic, and Sentence-BERT; and thus uses no direct human supervision. When compared to baselines like Mask-RCNN, our method outperforms on few-shot instance identification or semantic segmentation on the HM3D dataset with only a fraction of the examples. Finally, we show that using CLIP-Fields as a scene memory, robots can perform semantic navigation in real-world environments. Our code and demonstration videos are available here:



    AUTHOR    = {Nur Muhammad (Mahi)Shafiullah AND Chris Paxton AND Lerrel Pinto AND Soumith Chintala AND Arthur Szlam}, 
    TITLE     = {{CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2023}, 
    ADDRESS   = {Daegu, Republic of Korea}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2023.XIX.074}