Anticipatory Task and Motion Planning: Improved Rearrangement in Persistent Continuous-Space Environments
Roshan Dhakal, Duc Nguyen, Tom Silver, Xuesu Xiao, Gregory Stein
AI summary
Problem
Existing myopic task and motion planning strategies ignore future tasks, causing side effects that impede subsequent rearrangement tasks in persistent continuous-space environments.
Approach
A learned graph neural network estimates expected future costs, which are combined with an off-the-shelf TAMP planner to select plans that minimize both immediate and anticipated future costs.
Key results
- 32.7% average per-task cost reduction in navigation domains
- 16.7% average per-task cost reduction in cabinet-loading tasks
- Up to 83.1% cost improvement with advance environment preparation
- Successful real-world deployment on a Fetch mobile manipulator
Why it matters
Enables long-lived robots to operate more efficiently in dynamic, persistent environments by proactively avoiding planning side effects.
Abstract
We consider a sequential task and motion planning (TAMP) setting in which a robot is assigned continuous-space rearrangement-style tasks one-at-a-time in an environment that persists between each. Lacking advance knowledge of future tasks, existing (myopic) planning strategies unwittingly introduce side effects that impede completion of subsequent tasks: e.g., by block- ing future access or manipulation. We present anticipatory task and motion planning, in which estimates of expected future cost from a learned model inform selection of plans generated by a model-based TAMP planner so as to avoid such side effects, choosing configurations of the environment that both complete the task and reduce overall cost. Simulated many-task deploy- ments innavigation-among-movable-obstaclesandcabinet-loading domains yield improvements of 32.7% and 16.7% average per- task cost respectively. When given time in advance to prepare the environment, our learning-augmented planning approach yields improvements of 83.1% and 22.3%. Finally, we also demonstrate anticipatory TAMP on a real-world Fetch mobile manipulator.