Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 28·5 min read
Decoupling Planning and Control for Instructable Agents
TL;DR
Instruct-to-Act combines vision-language models (VLMs) and world-model controllers to enhance autonomous action execution from high-level instructions. This decoupled approach outperforms traditional methods in various environments, maintaining fast control and adaptability.
✦ Why It Matters
Consider implementing Instruct-to-Act for improved task execution in your AI systems today.
Key Takeaways
- Explore integrating VLMs with world-model controllers for better task execution.
- Utilize synthetic instructions to enhance controller training.
- Evaluate performance across diverse environments to ensure robustness.
- Consider the adaptability of your AI systems with different VLM planners.
How It Works
Instruct-to-Act operates by training a world-model controller to respond to high-level instructions from a VLM planner. The controller is trained using a combination of behavior cloning, which mimics expert actions, and reward maximization, which encourages successful task completion.
By relabeling segments of the controller's policy rollouts with synthetic instructions, the system learns to interpret and act on sparse, high-level commands effectively.