PaLM-E: An Embodied Multimodal Language Model (2303.03378v1)

Abstract: LLMs excel at a wide range of complex tasks. However, enabling general inference in the real world, e.g., for robotics problems, raises the challenge of grounding. We propose embodied LLMs to directly incorporate real-world continuous sensor modalities into LLMs and thereby establish the link between words and percepts. Input to our embodied LLM are multi-modal sentences that interleave visual, continuous state estimation, and textual input encodings. We train these encodings end-to-end, in conjunction with a pre-trained LLM, for multiple embodied tasks including sequential robotic manipulation planning, visual question answering, and captioning. Our evaluations show that PaLM-E, a single large embodied multimodal model, can address a variety of embodied reasoning tasks, from a variety of observation modalities, on multiple embodiments, and further, exhibits positive transfer: the model benefits from diverse joint training across internet-scale language, vision, and visual-language domains. Our largest model, PaLM-E-562B with 562B parameters, in addition to being trained on robotics tasks, is a visual-language generalist with state-of-the-art performance on OK-VQA, and retains generalist language capabilities with increasing scale.

Overview of "PaLM-E: An Embodied Multimodal LLM"

The paper introduces PaLM-E, an embodied multimodal LLM designed to directly interface LLMs with real-world continuous sensor modalities, thus enabling grounded reasoning in robotics. Traditional LLMs demonstrate various reasoning capabilities but struggle with grounding in the physical world—a critical feature for solving computer vision and robotics tasks. PaLM-E addresses this limitation by integrating multimodal data into the LLMing paradigm.

Main Contributions

1. Introduction of Embodied LLMs: The paper advances the concept of embodied LLMs that incorporate continuous sensor inputs (images, state vectors) into LLMs. This approach aims to bridge the gap between language representations and real-world percepts, allowing the LLMs to perform more grounded inferences in robotic environments.
2. Architectural Design: The introduced method encodes continuous observations into sequences of vectors within the language token embedding space. These embeddings are then processed by the attention layers of a Transformer-based LLM, enabling it to handle multimodal inputs and generate outputs suitable for both textual and physical actions.
3. Transfer Learning Across Modalities and Tasks: PaLM-E is trained on a diverse set of datasets encompassing internet-scale language, vision-language tasks, and robotics scenarios. This joint training approach demonstrates positive transfer effects, significantly enhancing the model's performance on individual tasks compared to specialized models trained in isolation.
4. Scalability: The model is scaled up to 562 billion parameters, integrating the capabilities of PaLM (540B) and ViT (22B). The largest PaLM-E model achieves state-of-the-art results in vision-language benchmarks such as OK-VQA, while retaining general language capabilities.
5. Empirical Evaluation: Robust evaluation across multiple domains, including robotic manipulation planning, visual question answering (VQA), and captioning, establishes PaLM-E's versatility. Additionally, empirical studies reveal that the use of structured neural scene representations, such as Object Scene Representation Transformer (OSRT), substantially improves the model’s performance, particularly in low-data regimes.

Key Findings

• Performance on Robotic Tasks:

PaLM-E can solve complex robotic tasks in simulated and real-world environments, significantly outperforming baseline methods like SayCan and zero-shot variants of PaLI. It demonstrates robust planning capabilities, high data efficiency, and generalization to new environments and object configurations.

• Transfer and Data Efficiency:

Through co-training on a mixture of diverse tasks, PaLM-E benefits from transfer learning, achieving higher success rates on robotics tasks with a reduced need for extensive task-specific data. This is evident in environments like TAMP and Language-Table where the model exceeds the performance of models trained solely on individual tasks.

• General Vision-Language Benchmarks:

The generalist PaLM-E model shows competitive results on standard benchmarks like VQA v2 and COCO captioning, underscoring its effectiveness as a vision-LLM. Notably, the PaLM-E-562B variant achieves the highest reported score on OK-VQA, even surpassing models specifically fine-tuned for this task.

• Language Capabilities:

PaLM-E maintains strong performance in general natural language tasks, with minimal catastrophic forgetting observed in the largest model. This demonstrates that scaling up the model size helps preserve the language capabilities during multimodal training.

Implications and Future Directions

The introduction of PaLM-E has several significant implications:

1. Enhanced Grounded Reasoning: By directly incorporating multimodal inputs, PaLM-E represents a substantial step towards more grounded AI systems capable of reasoning about and interacting with the physical world.
2. Transfer Learning: The demonstrated transfer effects indicate that multimodal and multitask training can effectively leverage large, heterogeneous datasets to improve performance across diverse domains.
3. Application in Robotics: The model's ability to plan and execute complex robotic tasks opens up new possibilities for deploying LLMs in practical robotics applications, such as automated manipulation and navigation in real-world environments.
4. Further Research: Future research can explore optimizing the integration of multimodal inputs, enhancing data efficiency further, and extending the model's capabilities to more diverse and complex tasks. Additionally, the exploration of novel architectural ideas for embedding continuous observations into LLMs can pave the way for even more robust and versatile models.

In conclusion, PaLM-E represents a substantial advance in the integration of multimodal inputs with LLMs, demonstrating the potential for significant improvements in embodied reasoning tasks and setting a new benchmark for future research in this area.