PaLM-E: An Embodied Multimodal Language Model
Abstract: Large language models 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 language models to directly incorporate real-world continuous sensor modalities into language models and thereby establish the link between words and percepts. Input to our embodied language model 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 large language model, 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.
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Synopsis
Overview
- Keywords: Embodied Language Models, Multimodal Learning, Robotics, Vision-Language Models, Transfer Learning
- Objective: Develop an embodied multimodal language model that integrates continuous sensor modalities for improved reasoning in robotics and visual tasks.
- Hypothesis: A generalist language model can effectively incorporate multimodal inputs to enhance decision-making and reasoning capabilities in embodied tasks.
Background
Preliminary Theories:
- Large Language Models (LLMs): Models trained on vast textual data, capable of performing complex reasoning tasks but often lack grounding in real-world contexts.
- Vision-Language Models (VLMs): Models that combine visual and textual understanding, facilitating tasks like visual question answering and image captioning.
- Embodied AI: The concept of integrating physical actions and sensory feedback into AI systems, enabling them to interact with and understand their environments.
- Transfer Learning: A technique where knowledge gained while solving one problem is applied to a different but related problem, enhancing model performance with limited data.
Prior Research:
- 2018: Introduction of BERT, which revolutionized natural language understanding through pre-training and fine-tuning.
- 2020: Development of GPT-3, showcasing the capabilities of large-scale language models in diverse tasks.
- 2021: Emergence of models like CLIP, integrating vision and language for better understanding of multimodal data.
- 2022: Introduction of Frozen, a model that optimizes vision encoder parameters through a frozen LLM, setting the stage for further exploration in embodied reasoning.
Methodology
Key Ideas:
- Multimodal Sentence Representation: Inputs are structured as sequences of tokens that include visual, state estimation, and textual encodings, allowing for flexible processing.
- Neural Scene Representations: Incorporation of 3D-aware object representations to enhance the model's understanding of spatial relationships.
- End-to-End Training: The model is trained in conjunction with a pre-trained LLM, allowing for seamless integration of multimodal inputs.
- Co-training Across Tasks: Training on a diverse set of tasks simultaneously to improve generalization and performance in specific tasks.
Experiments:
- Robotic Manipulation Tasks: Evaluated on various robotic tasks, including mobile manipulation and sequential planning, using both simulated and real robots.
- Visual Question Answering (VQA): Tested on benchmarks like OK-VQA to assess performance in visual-language tasks.
- Data Efficiency Studies: Investigated the model's ability to learn from limited examples, demonstrating effective transfer learning across domains.
Implications: The design allows for improved data efficiency and the retention of language capabilities during multimodal training, indicating a promising direction for future embodied AI systems.
Findings
Outcomes:
- Performance Improvement: PaLM-E significantly outperformed existing models in both embodied reasoning and visual-language tasks, showcasing its versatility.
- Zero-shot and Few-shot Learning: The model demonstrated the ability to generalize from minimal training examples, achieving high success rates in novel tasks.
- Robustness in Real-world Applications: Successfully guided real robots through complex tasks, adapting to dynamic environments and unexpected challenges.
Significance: This research challenges the notion that LLMs require extensive task-specific fine-tuning, highlighting the potential of generalist models to excel across diverse applications.
Future Work: Exploration of further integration of sensory modalities, enhancement of real-time decision-making capabilities, and scaling the model for even broader applications in robotics and AI.
Potential Impact: Advancements in embodied AI could lead to more capable and adaptable robotic systems, enhancing their utility in real-world applications such as home assistance, manufacturing, and exploration.