SeeAct is an open-source AI agent framework that combines large language model planning with visual scene understanding to decompose tasks into subgoals and generate action sequences. It provides modular perception, planning, and execution pipelines to build vision-language agents for navigation, manipulation, and interactive reasoning. Researchers and developers can extend components, run benchmarks on simulated environments, and customize workflows for new tasks.
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What is SeeAct?
SeeAct is designed to empower vision-language agents with a two-stage pipeline: a planning module powered by large language models generates subgoals based on observed scenes, and an execution module translates subgoals into environment-specific actions. A perception backbone extracts object and scene features from images or simulations. The modular architecture allows easy replacement of planners or perception networks and supports evaluation on AI2-THOR, Habitat, and custom environments. SeeAct accelerates research on interactive embodied AI by providing end-to-end task decomposition, grounding, and execution.
Who will use SeeAct?
- AI researchers
- Robotics developers
- NLP practitioners
- Vision-language system engineers
How to use the SeeAct?
- Step1: Clone the SeeAct GitHub repository
- Step2: Install Python and required dependencies via pip or conda
- Step3: Download or configure a supported simulation environment (e.g., AI2-THOR)
- Step4: Define perception and planner modules in the config file
- Step5: Run training or inference scripts to generate subgoals and actions
- Step6: Analyze results and fine-tune modules for custom tasks
Platform
- Web
- Linux
- Mac
- Windows
SeeAct's Core Features & Benefits
The Core Features
- LLM-based subgoal planning
- Visual perception and feature extraction
- Modular execution pipeline
- Benchmark tasks on simulated environments
- Configurable components
The Benefits
- Interpretable task decomposition
- Rapid prototyping of embodied agents
- Highly extensible architecture
- Compatibility with standard benchmarks
- Open-source and community-driven
SeeAct's Main Use Cases & Applications
- Vision-and-language navigation in AI2-THOR
- Robotic manipulation policy testing
- Interactive scene understanding demos
- Task planning in virtual environments
SeeAct's Pros & Cons
The Pros
Leverages advanced multimodal large models like GPT-4V for sophisticated web interaction.
Combines action generation and grounding to effectively perform tasks on live websites.
Exhibits strong capabilities in speculative planning, content reasoning, and self-correction.
Openly available as a Python package facilitating ease of use and further development.
Demonstrated competitive performance in online task completion with a 50% success rate.
Accepted at a major AI conference (ICML 2024), reflecting validated research contributions.
The Cons
Action grounding remains a significant challenge with a notable performance gap compared to oracle grounding.
Current grounding methods (element attributes, textual choices, image annotation) have error cases leading to failures.
Success rate on live websites is limited to about half the tasks, indicating room for improvement in robustness and generalization.
FAQs of SeeAct
SeeAct Company Information
- OSU NLP Group
SeeAct Reviews
5/5
SeeAct's Main Competitors and alternatives?
HuggingGPT
SayCan
LangChain Agents
MiniGPT-4
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