Robots That Plan Long, Multi‑Step Tasks Using General AI

Teaching robots to handle real-world chores—no special training required

This research shows a new way for robots to complete long, multi-step tasks by combining off‑the‑shelf foundation models (the same kind powering today’s AI) with a constantly updated “scene graph”—a smart map of objects and their relationships.

Here’s the idea: foundation models handle what the robot sees and understands (vision and language), while a general reasoning model decides the sequence of actions. The scene graph ties it together, tracking where things are and how they change so the robot can plan reliably over many steps without forgetting context.

• Multimodal perception from existing AI models
• General-purpose reasoning for robust task sequencing
• Dynamic scene graphs for spatial awareness and consistency

Tested on tabletop manipulation, the framework highlights a path to build capable robot systems directly on top of today’s off-the-shelf AI—no domain-specific training needed.

Paper by Sushil Samuel Dinesh and Shinkyu Park. Read more: http://arxiv.org/abs/2510.27558v1

Paper: http://arxiv.org/abs/2510.27558v1

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