RishiHazra/saycanpay

Official code release of AAAI 2024 paper SayCanPay.

32
/ 100
Emerging

SayCanPay helps researchers and developers in robotics and AI to create better plans for autonomous agents in simulated environments. It takes detailed descriptions of tasks and simulated world states, then outputs optimal action sequences for the agent. This tool is ideal for those developing and evaluating advanced planning algorithms for robots or virtual assistants.

Use this if you are a researcher or developer working on robotic manipulation, virtual agent control, or grounded language learning, and need to generate efficient action plans for complex tasks within simulated environments.

Not ideal if you are looking for a ready-to-use, off-the-shelf solution for controlling physical robots or real-world systems without significant development work.

robotics research AI planning agent behavior simulated environments language grounding
No License No Package No Dependents
Maintenance 6 / 25
Adoption 8 / 25
Maturity 8 / 25
Community 10 / 25

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Stars

54

Forks

5

Language

Python

License

Last pushed

Oct 22, 2025

Commits (30d)

0

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