Recasting Generic Pretrained Vision Transformers As Object-Centric Scene Encoders For Manipulation Policies
<|reference_start|>Recasting Generic Pretrained Vision Transformers As Object-Centric Scene Encoders For Manipulation Policies: Generic re-usable pre-trained image representation encoders have become a standard component of methods for many computer vision tasks. As visual representations for robots however, their utility has been limited, leading to a recent wave of efforts to pre-train robotics-specific image encoders that are better suited to robotic tasks than their generic counterparts. We propose Scene Objects From Transformers, abbreviated as SOFT, a wrapper around pre-trained vision transformer (PVT) models that bridges this gap without any further training. Rather than construct representations out of only the final layer activations, SOFT individuates and locates object-like entities from PVT attentions, and describes them with PVT activations, producing an object-centric embedding. Across standard choices of generic pre-trained vision transformers PVT, we demonstrate in each case that policies trained on SOFT(PVT) far outstrip standard PVT representations for manipulation tasks in simulated and real settings, approaching the state-of-the-art robotics-aware representations. Code, appendix and videos: https://sites.google.com/view/robot-soft/<|reference_end|>
@article{qian2024recasting,
    title={Recasting Generic Pretrained Vision Transformers As Object-Centric Scene
  Encoders For Manipulation Policies},
    author={Jianing Qian, Anastasios Panagopoulos, Dinesh Jayaraman},
    journal={arXiv preprint arXiv:2405.15916},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15916},
    primaryClass={cs.CV cs.RO}
}