Simple but Effective: CLIP Embeddings for Embodied AI

@article{Khandelwal2021SimpleBE,
  title={Simple but Effective: CLIP Embeddings for Embodied AI},
  author={Apoorv Khandelwal and Luca Weihs and Roozbeh Mottaghi and Aniruddha Kembhavi},
  journal={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={14809-14818}
}
Contrastive language image pretraining (CLIP) encoders have been shown to be beneficial for a range of visual tasks from classification and detection to captioning and image manipulation. We investigate the effectiveness of CLIP visual backbones for Embodied AI tasks. We build incredibly simple baselines, named EmbCLIP, with no task specific architectures, inductive biases (such as the use of semantic maps), auxiliary tasks during training, or depth maps-yet we find that our improved baselines… 

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References

SHOWING 1-10 OF 39 REFERENCES

Habitat: A Platform for Embodied AI Research

The comparison between learning and SLAM approaches from two recent works are revisited and evidence is found -- that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and the first cross-dataset generalization experiments are conducted.

Matterport3D: Learning from RGB-D Data in Indoor Environments

Matterport3D is introduced, a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400RGB-D images of 90 building-scale scenes that enable a variety of supervised and self-supervised computer vision tasks, including keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and region classification.

How Much Can CLIP Benefit Vision-and-Language Tasks?

It is shown that CLIP significantly outperforms widely-used visual encoders trained with in-domain annotated data, such as BottomUp-TopDown, and also establishes new state-of-the-art results on Visual Question Answering, Visual Entailment, and V&L Navigation tasks.

Rearrangement: A challenge for embodied

  • ai. arXiv,
  • 2020

Learning Transferable Visual Models From Natural Language Supervision

It is demonstrated that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet.

Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale

A large-scale study of imitating human demonstrations on tasks that require a virtual robot to search for objects in new environments - ObjectGoal Navigation and Pick&place - finds the IL-trained agent learns efficient object-search behavior from humans.

Continuous Scene Representations for Embodied AI

Using CSR, state-of-the-art approaches for the challenging downstream task of visual room rearrangement are outperformed, without any task specific training and the learned embeddings capture salient spatial details of the scene and show applicability to real world data.

Stubborn: A Strong Baseline for Indoor Object Navigation

A semantic-agnostic exploration strategy (called Stubborn) without any learning that surprisingly outperforms prior work on the Habitat Challenge task of navigating to a target object in indoor environments is presented.

THDA: Treasure Hunt Data Augmentation for Semantic Navigation

This paper shows that the key problem is overfitting in ObjectNav, and introduces Treasure Hunt Data Augmentation (THDA) to address overfitting.

Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI

Habitat-Matterport 3D is a large-scale dataset of 1,000 building-scale 3D reconstructions from a diverse set of real-world locations that is ‘pareto optimal’ in the following sense – agents trained to perform PointGoal navigation on HM3D achieve the highest performance regardless of whether they are evaluated onHM3D, Gibson, or MP3D.