The authors assembled LCWM-Bench from 15 datasets covering navigation, robotics, 3D gaming and driving.
AI agents benefit from checking both plans and imagined outcomes
Across navigation, robotics, gaming and driving, the authors report complementary benefits from ranking proposed actions and evaluating their predicted visual consequences.
Big Tech
Fengyi Wu · Yifei Dong · Yetong Sha · Lingdong Kong · Qiyu Hu · Bohan Xiong · +3 more
University of Washington · National University of Singapore · Microsoft Research · Carnegie Mellon University
Research Digest··2 min read
In one line
Tiresias plans actions by generating candidate trajectories, ranking them, then imagining their visual consequences via a world model.
Before you read this
Three ideas this paper builds onworld model for agentsA predictive environment model that agents use to simulate outcomes, enabling planning, reasoning, and long-horizon task execution.111 papers →vision-language-action modelA robot policy model that maps visual observations and language instructions to actions, trained on demonstrations and often used for manipulation tasks.83 papers →context-aware planningPlanning that uses extra situational information to guide decision-making and improve outcomes.19 papers →
Wu and colleagues study how AI agents can turn a language instruction and an initial view of their surroundings into an action sequence.
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