- The paper presents a novel method for mapping 3D spatial coordinates to semantic embeddings using weak supervision.
- It integrates multi-resolution hash encoding and contrastive learning with models like CLIP, Detic, and Sentence-BERT for effective scene representation.
- Experiments on the HM3D dataset show improved few-shot instance identification and semantic segmentation, enhancing robotic navigation.
CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory
In the presented paper, the authors introduce CLIP-Fields, a novel approach for robotic semantic memory construction that leverages weakly supervised learning from large-scale web-trained models. CLIP-Fields are designed to facilitate various robotic tasks such as segmentation, instance identification, semantic search, and view localization without relying on direct human annotations. This is achieved by integrating state-of-the-art web-scale vision and LLMs including CLIP, Detic, and Sentence-BERT.
Methodology and Contributions
The core innovation of CLIP-Fields lies in its ability to generate an implicit scene model via a mapping from spatial coordinates to semantic embeddings. This approach builds on the recent progress in neural implicit representations of 3D spaces and the capabilities of weakly-supervised models. Utilizing models trained on vast datasets, such as CLIP, allows CLIP-Fields to establish such mappings with minimal directly labeled data, addressing a significant bottleneck in deploying semantic memory systems in robotics.
Training CLIP-Fields involves creating a dataset where points in 3D space correspond to semantic and visual features extracted from web models, applying contrastive learning objectives. The architecture encodes an implicit function with multi-resolution hash encoding (MHE) for efficient representation, coupled with objective-specific neural network heads that capture different aspects of the environment, such as language and visual context.
Evaluation and Results
The authors demonstrate the efficacy of CLIP-Fields in a series of experiments, notably few-shot instance identification and semantic segmentation on the challenging HM3D dataset. CLIP-Fields outperformed traditional fine-tuned models like Mask-RCNN, particularly in scenarios with limited data, showcasing its capability to generalize semantic understanding from sparse examples.
Moreover, the system effectively utilized CLIP-Fields as a semantic scene memory for robotic navigation tasks in real-world settings. The ability to execute commands based on semantic queries without predefined labels validates its potential for practical applications.
Implications and Future Directions
The introduction of CLIP-Fields marks a significant step toward leveraging pretrained models in robotics, offering scalability and versatility in semantic environmental understanding. By sidestepping extensive manual annotation and the constraints of fixed class labels, CLIP-Fields provides a more flexible framework for deploying robots in dynamic and complex real-world environments.
Moving forward, refining CLIP-Fields to handle dynamic scenes and incorporating multi-view and multi-sensory inputs could further broaden their application scope. Exploring parameter sharing across different scenes and embedding more abstract features such as affordances for tasks like manipulation and planning offers exciting avenues for future research. Additionally, as foundational models like CLIP continue to improve, we can expect corresponding enhancements in the capabilities and performance of CLIP-Fields, presenting a compelling case for their integration into robotic systems.
Overall, CLIP-Fields exemplifies the synergy between semantic representation learning and neural implicit fields, poised to advance the field of robotics by providing a means to navigate and interact with environments in an informed manner.