FORGE: Towards Functional Tool-Use Generalization via Keypoint Trajectory Reasoning

2026-07-07 · Source: Artificial Intelligence · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

FunctiOnal Reasoning and Grounded Execution (FORGE) is a two-stage policy designed to achieve functional tool-use generalization in robots. This system addresses the challenge where robots, unlike humans, fail to transfer tool functions to novel objects despite visual similarities, due to differing action space requirements. FORGE utilizes 2D keypoint trajectories as an intermediate representation, effectively balancing functional expressiveness with action groundability. The policy operates by first predicting generalizable keypoint trajectories from action-free data, then grounding these trajectories into robot actions using limited demonstrations. On a seven-tool hitting-function benchmark, FORGE consistently outperformed state-of-the-art methods on unseen tools, achieving over 2X improvement in average success rate in both simulation and real-world environments.

Key takeaway

For robotics engineers developing systems for generalizable tool use, FORGE offers a robust approach to overcome the challenge of transferring functions to novel tools. You should consider implementing a two-stage policy that leverages 2D keypoint trajectories to separate functional reasoning from action execution. This method significantly improves success rates on unseen tools, enabling more adaptable and versatile robotic applications.

Key insights

Functional tool-use generalization in robots requires decoupling functional reasoning from action execution via keypoint trajectories.

Principles

• Functional generalization requires bridging perceptual and action space gaps.
• Keypoint trajectories offer a balanced intermediate representation.
• Decoupling reasoning from execution enhances tool-use transfer.

Method

FORGE is a two-stage policy: first, predict generalizable keypoint trajectories from action-free data; then, ground these trajectories into robot actions using limited demonstrations.

In practice

• Implement 2D keypoint trajectories for robot tool-use.
• Decouple functional reasoning from action execution.

Topics

• Robotics
• Functional Generalization
• Tool Use
• Keypoint Trajectories
• Robot Learning
• Policy Learning

Best for: Research Scientist, AI Scientist, Robotics Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.