Simple but Effective: CLIP Embeddings for Embodied AI (2111.09888v2)

Abstract: 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 perform very well across a range of tasks and simulators. EmbCLIP tops the RoboTHOR ObjectNav leaderboard by a huge margin of 20 pts (Success Rate). It tops the iTHOR 1-Phase Rearrangement leaderboard, beating the next best submission, which employs Active Neural Mapping, and more than doubling the % Fixed Strict metric (0.08 to 0.17). It also beats the winners of the 2021 Habitat ObjectNav Challenge, which employ auxiliary tasks, depth maps, and human demonstrations, and those of the 2019 Habitat PointNav Challenge. We evaluate the ability of CLIP's visual representations at capturing semantic information about input observations -- primitives that are useful for navigation-heavy embodied tasks -- and find that CLIP's representations encode these primitives more effectively than ImageNet-pretrained backbones. Finally, we extend one of our baselines, producing an agent capable of zero-shot object navigation that can navigate to objects that were not used as targets during training. Our code and models are available at https://github.com/allenai/embodied-clip

An Analysis of CLIP Embeddings in Embodied AI Tasks

The paper "Simple but Effective: CLIP Embeddings for Embodied AI" explores the impact of utilizing CLIP (Contrastive Language-Image Pretraining) visual encoders in the domain of Embodied AI. This approach has produced significant results across various embodied tasks, with the authors implementing a series of experiments to evaluate the performance of CLIP-based models against traditional baselines. These baselines, referred to as EmbCLIP, are characterized by their simplicity: they eschew specialized architectures and auxiliary tasks yet achieve top-tier performance across multiple benchmarks in embodied AI.

Methodological Insights

The authors propose EmbCLIP, which adopts CLIP's visual backbones for a variety of embodied tasks including ObjectNav in RoboTHOR and Habitat, and Room Rearrangement in iTHOR. Notably, EmbCLIP does not require task-specific adaptations such as depth maps or human annotations, which are prevalent in other contemporary models. This minimalist approach relies solely on RGB inputs and employs CLIP's ResNet-50 visual encoder with frozen weights, thereby simplifying the architecture while maintaining performance.

In a series of experiments, EmbCLIP outperformed or matched existing state-of-the-art models that utilize more comprehensive architectures involving semantic maps and additional data like depth. For instance, the EmbCLIP surpassed the RoboTHOR ObjectNav leaderboard by a margin of 20 points in Success Rate. In the iTHOR Room Rearrangement, it more than doubled the effectiveness metric compared to other models employing Active Neural Mapping.

Empirical Findings

The paper provides strong empirical evidence of the efficacy of CLIP representations for navigation-heavy tasks. Notably, they demonstrated that CLIP-based models could encode visual primitives related to object presence, reachability, and free space more effectively than traditional ImageNet-pretrained models. This was validated through a series of probing tasks which assessed the ability of these models to recognize and localize objects and estimate walkable surfaces.

These probing experiments revealed that CLIP's visual encoders improved classification metrics, providing up to a 16% relative improvement over ImageNet baselines in object localization tasks. This indicates that CLIP's model architecture and training regime allow it to capture semantic and geometrical information more effectively, which is crucial for the performance in embodied AI tasks.

Implications and Speculations

The results suggest that the power of generic visual representations from CLIP in behind its application to embodied AI tasks potentially stems from its training on diverse, large-scale image and text data. This opens new avenues for leveraging large-scale pretraining as an alternative to specialized architectures in embodied AI.

Moreover, the paper reveals that ImageNet accuracy is not a reliable predictor of success in embodied tasks, emphasizing the need to evaluate model suitability based on task-relevant semantic and geometric information encoded in the representations.

An exciting development explored in the paper is the potential for zero-shot object navigation, where EmbCLIP achieved roughly half the Success Rate on unseen objects compared to seen ones. This illustrates the potential of using CLIP's language-grounded representations to aid in the generalization to novel tasks and highlights further research directions in zero-shot learning paradigms within embodied AI.

Conclusion

The utilization of CLIP embeddings significantly enhances the capabilities of embodied AI agents in navigation-heavy tasks, rivaling or surpassing the performance of more complex models. This research underscores the power of large-scale contrastive language-image pretraining, offering insights into the effective encoding of essential visual semantics and geometries. Future AI research can build on these results by exploring further simplifications in architecture and enhancements in zero-shot learning, potentially leading to more generalized and efficient embodied agents.