Decoupling Planning and Control for Instructable AI Agents
Key takeaways
- Instruct-to-Act decouples VLM planning from world-model control for AI agents.
- VLMs provide high-level plans, while controllers handle low-latency actions.
- Synthetic instruction relabeling trains controllers for language instructability.
- This approach improves performance and flexibility over integrated VLM-action systems.
Who benefits
Summary
Instruct-to-Act is a new system that combines the strengths of VLM planners for high-level instructions with world-model controllers for low-latency actions, decoupling planning and control. By training controllers with synthetic instructions, it outperforms combined VLM-action variants across multiple embodied environments, maintaining fast control and allowing flexible VLM planner integration.
Why it matters
This approach offers a more robust and flexible way to build AI agents that can understand complex instructions and execute them reliably in dynamic environments, crucial for robotics and autonomous systems.
How to implement this in your domain
- 1Evaluate current agent architectures for tasks requiring both high-level planning and low-latency control.
- 2Consider adopting a decoupled VLM-planner and world-model controller approach for new agent development.
- 3Explore methods for generating synthetic instructions to train controllers for language instructability.
- 4Test the system's performance in environments requiring fast, reactive control alongside complex task execution.
- 5Investigate the potential for swapping different VLM planners without retraining the underlying controller.
Original post by Zineng Tang, Kelsey R. Allen, Sjoerd van Steenkiste, Ishita Dasgupta, Alane Suhr
"arXiv:2608.26788v1 Announce Type: new Abstract: Recent work shows that pre-trained, instruction-tuned vision-language models (VLMs) perform well at mapping from instructions and observations to high-level plans, but struggle to realize such plans as reliable low-latency action se…"
View on XOriginally posted by Zineng Tang, Kelsey R. Allen, Sjoerd van Steenkiste, Ishita Dasgupta, Alane Suhr on X · view source
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