predict-human-intention-symbolic-goal-then-plan-jointly TEC-0162 · proposedPredict human intention as a symbolic goal, then plan jointl
A VLM infers the human's latent intention from third-person images plus a scene graph as symbolic goal predicates rather than an action sequence, and those goals are grounded with the robot's goal into a multi-agent PDDL problem solved by a
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
A VLM infers the human's latent intention from third-person images plus a scene graph as symbolic goal predicates rather than an action sequence, and those goals are grounded with the robot's goal into a multi-agent PDDL problem solved by a Filed by the drafting stage from HINT-Plan: Human Intention-Aware Robot Task Planning in Context-Rich Environments using Vision Language Models (2609.17771) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Joint success rate 69.71% vs 35.50% for VLM-as-planner and 34.42% for GG-LLM, with paired cluster bootstrap CIs on 48 scene-activity-task configurations; also higher robot and human goal completion and about half the functional conflict rate.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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Sources
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