Benchmarking Egocentric Embodied Planning with Multimodal Large Language Models

מושגי ליבה

Multimodal Large Language Models have the potential to serve as embodied task planners, but current models struggle to effectively plan actions in real-world scenarios with complex visual inputs.

תקציר

The paper introduces EgoPlan-Bench, a benchmark for evaluating the Egocentric Embodied Planning capabilities of Multimodal Large Language Models (MLLMs). The benchmark is derived from realistic egocentric videos and features:

Realistic Tasks: The tasks are extrapolated from authentic real-world videos, offering a closer reflection of daily human needs and showcasing greater variety than artificially constructed tasks.

Diverse Actions: The benchmark involves a diverse set of actions, requiring interaction with hundreds of different objects and extending beyond basic manipulation skills.

Intricate Visual Observations: The visual observations come across various real-world scenes, where objects vary in appearance, state, and placement. The visual inputs can also span extensive periods, making it difficult for models to monitor task progression and detect critical changes in object states.

The authors evaluate a wide range of MLLMs on the benchmark and find that current models struggle to effectively plan actions in these real-world scenarios. They further construct an instruction-tuning dataset, EgoPlan-IT, to enhance the Egocentric Embodied Planning capabilities of MLLMs. The model tuned on EgoPlan-IT demonstrates significant performance gains on the benchmark and the potential to act as a task planner for guiding embodied agents in simulated environments.

סטטיסטיקה

"The tasks are extrapolated from authentic real-world videos, offering a closer reflection of daily human needs and showcasing greater variety than artificially constructed tasks."
"The benchmark involves a diverse set of actions, requiring interaction with hundreds of different objects and extending beyond basic manipulation skills."
"The visual observations come across various real-world scenes, where objects vary in appearance, state, and placement. The visual inputs can also span extensive periods, making it difficult for models to monitor task progression and detect critical changes in object states."

ציטוטים

"Multimodal Large Language Models (MLLMs) inherently have the potential to serve as embodied task planners, which are expected to predict feasible actions given a specific task goal, real-time task progress and visual observations."
"However, embodied planning in real-world scenarios presents significant challenges, as it requires a comprehensive understanding of the dynamic and complicated visual environment and the identification of the key information relevant to the tasks."

תובנות מפתח מזוקקות מ:

EgoPlan-Bench: Benchmarking Egocentric Embodied Planning with Multimodal Large Language Models

by Yi Chen,Yuyi... ב- arxiv.org 04-18-2024

https://arxiv.org/pdf/2312.06722.pdf

שאלות מעמיקות

How can MLLMs be further improved to better handle the challenges of Egocentric Embodied Planning, such as detecting subtle state changes and maintaining a comprehensive understanding of the dynamic visual environment?

To enhance the performance of MLLMs in Egocentric Embodied Planning, several strategies can be implemented:

Fine-tuning with Diverse Data: Incorporating a more diverse range of egocentric videos with varying complexities and scenarios can help MLLMs better understand and adapt to different real-world situations. This exposure to diverse data can improve the model's ability to detect subtle state changes and understand complex visual environments.

Multi-Modal Fusion Techniques: Implementing advanced multi-modal fusion techniques can help MLLMs effectively integrate information from different modalities such as text, images, and videos. Techniques like cross-modal attention mechanisms can enable the model to focus on relevant visual cues while processing language instructions, improving its understanding of the task at hand.

Incremental Learning: Implementing incremental learning strategies can allow MLLMs to continuously update their knowledge and adapt to new information. By incrementally updating the model with new data and experiences, it can stay relevant and accurate in handling dynamic visual environments and subtle state changes.

Contextual Reasoning: Enhancing the model's contextual reasoning capabilities can help MLLMs better understand the relationships between different elements in the visual environment. By improving the model's ability to reason contextually, it can make more informed decisions and predictions regarding task progression and action planning.

How can the insights and findings from this work on Egocentric Embodied Planning be applied to other areas of embodied AI, such as robotic control or interactive virtual assistants?

The insights and findings from Egocentric Embodied Planning can be applied to various areas of embodied AI in the following ways:

Robotic Control: The techniques and methodologies developed for Egocentric Embodied Planning can be leveraged in robotic control systems to enable robots to perform complex tasks in real-world environments. By integrating MLLMs with visual perception capabilities, robots can better understand their surroundings, plan actions, and execute tasks autonomously.

Interactive Virtual Assistants: The advancements in Egocentric Embodied Planning can enhance the capabilities of interactive virtual assistants by enabling them to understand and respond to user instructions in a more contextually relevant manner. Virtual assistants can utilize MLLMs to interpret multi-modal inputs, such as text and images, to provide more personalized and effective assistance to users.

Autonomous Vehicles: The principles of Egocentric Embodied Planning can also be applied to autonomous vehicles to improve their decision-making processes in dynamic driving environments. By integrating MLLMs with real-time sensor data, vehicles can better interpret their surroundings, anticipate potential obstacles, and make informed driving decisions to ensure safety and efficiency.

By transferring the knowledge and techniques developed in Egocentric Embodied Planning to these areas, we can enhance the capabilities of embodied AI systems across various domains, leading to more intelligent and adaptive autonomous agents.