EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought (2305.15021v2)

Abstract: Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understanding and execution capabilities. To achieve this, we have made the following efforts: (i) We craft a large-scale embodied planning dataset, termed EgoCOT. The dataset consists of carefully selected videos from the Ego4D dataset, along with corresponding high-quality language instructions. Specifically, we generate a sequence of sub-goals with the "Chain of Thoughts" mode for effective embodied planning. (ii) We introduce an efficient training approach to EmbodiedGPT for high-quality plan generation, by adapting a 7B LLM to the EgoCOT dataset via prefix tuning. (iii) We introduce a paradigm for extracting task-related features from LLM-generated planning queries to form a closed loop between high-level planning and low-level control. Extensive experiments show the effectiveness of EmbodiedGPT on embodied tasks, including embodied planning, embodied control, visual captioning, and visual question answering. Notably, EmbodiedGPT significantly enhances the success rate of the embodied control task by extracting more effective features. It has achieved a remarkable 1.6 times increase in success rate on the Franka Kitchen benchmark and a 1.3 times increase on the Meta-World benchmark, compared to the BLIP-2 baseline fine-tuned with the Ego4D dataset.

Overview of EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought

The paper introduces EmbodiedGPT, an end-to-end multi-modal foundation model designed to advance embodied AI, a pivotal domain in robotics focused on enabling robots to plan and execute complex sequences of actions within physical environments. This model addresses the significant need for robots to comprehend and interact with the world using multi-modal inputs such as vision and language, thus empowering them to perform tasks that require long-term planning and autonomous execution based on real-time observations.

Key Contributions

1. Introduction of EgoCOT Dataset: A large-scale embodied planning dataset named EgoCOT is constructed, consisting of egocentrically captured videos from the Ego4D dataset paired with structured language instructions. Leveraging a “Chain of Thoughts” mode, the dataset facilitates effective embodied planning by breaking tasks into sub-goals, aiding the model in acquiring generative planning capabilities.
2. Efficient Training Approach: The authors adapt a 7B LLM by employing prefix tuning to the EgoCOT dataset to enhance embodied planning capabilities. This tuning fosters the generation of high-quality planning queries from visual inputs, bridging high-level planning with low-level control.
3. Integration of Vision and LLMs: The EmbodiedGPT architecture includes frozen vision and language modules interacting through an “embodied-former.” This design facilitates seamless integration of visual recognition with language-based task reasoning, allowing the agent to execute coherent plans based on visual and textual input streams.

Experimental Validation

Extensive experiments validate EmbodiedGPT's proficiency across several embodied AI tasks: embodied planning, embodied control, video captioning, and visual question answering (VQA). Particularly noteworthy are the percentages reflecting significant performance improvements in embodied control tasks when tested against benchmarks such as the Franka Kitchen and Meta-World, where EmbodiedGPT recorded success rates 1.6 times and 1.3 times higher respectively compared to the BLIP-2 baseline.

Moreover, user studies evaluating EmbodiedGPT in image input tasks praised its object recognition accuracy and spatial relationship understanding while noting the reduction in redundant information in outputs, underscoring the model’s precision in generating executable plans.

Theoretical and Practical Implications

From a theoretical perspective, EmbodiedGPT represents a meaningful progression toward integrating advanced LLMs with embodied cognition frameworks, heralding new possibilities for sophisticated human-robot interaction. Its ability to leverage structured "Chain of Thought" reasoning to transform abstract instructions into coherent activity sequences illustrates the promising potential of bridging symbolic-robotic planning with deep learning paradigms.

Practically, this approach could lead to substantial improvements in robotic operational efficiency and adaptability to dynamic real-world environments. Applications in service robotics, autonomous navigation, and human-robot collaboration stand to benefit from the model's robust multi-modal processing capabilities, which can translate complex real-time perceptions into tactically sound actions.

Future Directions

Future work can explore enhancements in computational efficiency by unfreezing certain model parameters for joint training once more resources become available. Additionally, incorporating additional modalities like auditory inputs could extend EmbodiedGPT's applicability. The exploration of user-specific interaction models and real-world deployments will be crucial for the continuous improvement of multi-modal embodied AI systems.

Overall, EmbodiedGPT showcases valuable strides in integrating vision and language understanding within embodied AI frameworks, yielding significant practical and theoretical advancements in the field of robotics and autonomous systems.