SOURCE-LINKED INTELLIGENCE
ManiSkillFormer: Demonstration-Free Compositional Manipulation via Task-Conditioned Geometric Contracts
Adapting robotic manipulation to new objects and tasks often requires additional demonstrations, policy fine-tuning, or manual engineering. Reusable manipulation skills can reduce this effort, but connecting their execution requirements to scene-specific geometry remains challenging. We present ManiSkillFormer, a framework for demonstration-free and compositional manipulation that connects perception and action through explicit geometric contracts. Building on reusable skill schemas, LLM agents generate contracts specifying the geometry primitives required by each skill, together with correspo
Read original source ↗ Open in workspace
- recordType
- paper
- region
- Global
Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T20:45:53.000Z
First collected: 2026-09-20T09:01:24.920Z. This is not the publication date.