Courteous Anticipation: Improving Long-Lived Task Planning in Persistent Shared Environments
We consider a task planning scenario in which robots sharing a persistent environment are assigned tasks one at a time from a held-out sequence. Standard task planners, lacking foresight of future tasks and inconsiderate of others' constraints, solve each task in isolation, leaving terminal states that increase future cost for all, side effects that compound over lengthy task sequences. To reduce cost over the sequence, a robot must anticipate how its actions now may impact performance on future tasks for all robots sharing the environment. Therefore, we present courteous anticipatory planning
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- PossiblePossibly related (embedding) · 49%Agentic AI for Robot Teams →
- PossiblePossibly related (embedding) · 48%Exploring continual learning without replay buffers: Our findings using dynamic task-similarity routing [P] →
- LinkedLinked via arxiv author · 85%Md Ridwan Hossain Talukder →
“Courteous Anticipation: Improving Long-Lived Task Planning in Persistent Shared Environments”
- LinkedLinked via arxiv author · 85%Roshan Dhakal →
“Courteous Anticipation: Improving Long-Lived Task Planning in Persistent Shared Environments”
- LinkedLinked via arxiv author · 85%Elizabeth Phillips →
“Courteous Anticipation: Improving Long-Lived Task Planning in Persistent Shared Environments”
- LinkedLinked via arxiv author · 85%Gregory J. Stein →
“Courteous Anticipation: Improving Long-Lived Task Planning in Persistent Shared Environments”