WorldSketch: Sketching World Models for Zero-Shot Mobile Manipulation
Overview
This research paper proposes WorldSketch, a new paradigm for agentic robotics where a vision-language model constructs a minimal kinematic world model by actively querying perception tools. The system captures task-relevant objects, interaction geometry, and possible motions, which are then used by a geometric planner to optimize contact and whole-body motion. The world model is self-improving through execution feedback that updates uncertain hypotheses. Demonstrations on RoboCasa, LIBERO-PRO, and R2R-CE navigation show zero-shot success rates of 91%, 97%, and 76% respectively, outperforming baselines while using significantly fewer VLM input tokens.
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Reported quantity · Reported success rate: 91 percent · reported trials · Reported zero-shot result; the excerpt does not specify an evaluation protocol.
“l is self-improving: execution feedback verifies outcomes, updates uncertain hypotheses, and enables the agent to revise the model when its predictions are contradicted. We demonstrate the generality of this paradigm across RoboCasa, LIBERO-PRO, and R2R-CE navigation, achieving zero-shot success rates of 91%, 97%, and 76%, respectively. On RoboCasa, WorldSketch outperforms the next-best zero-shot agent baseline by 58”
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Reported quantity · reduction in VLM input tokens for navigation: 6 other · Basis not reported
“percentage points while using 10 times fewer VLM input tokens; on LIBERO-PRO, it exceeds CaP-X by 82 percentage points. In navigation, it matches the success rate of step-by-step VLM agents while using 6 times fewer input tokens.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalWorldSketch: Sketching World Models for Zero-Shot Mobile Manipulation
This research paper proposes WorldSketch, a new paradigm for agentic robotics where a vision-language model constructs a minimal kinematic world model by actively querying perception tools. The system captures task-relevant objects, interaction geometry, and possible motions, which are then used by a geometric planner to optimize contact and whole-body motion. The world model is self-improving through execution feedback that updates uncertain hypotheses. Demonstrations on RoboCasa, LIBERO-PRO, and R2R-CE navigation show zero-shot success rates of 91%, 97%, and 76% respectively, outperforming baselines while using significantly fewer VLM input tokens.
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