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Generalized Multi-Level Replanning TAMP Framework for Dynamic Environment
Task and Motion Planning (TAMP) algorithms can generate plans that combine
logic and motion aspects for robots. However, these plans are sensitive to
interference and control errors. To make TAMP more applicable in real-world, we
propose the generalized multi-level replanning TAMP framework(GMRF), blending
the probabilistic completeness of sampling-based TAMP algorithm with the
robustness of reactive replanning. GMRF generates an nominal plan from the
initial state, then dynamically reconstructs this nominal plan in real-time,
reorders robot manipulations. Following the logic-level adjustment, GMRF will
try to replan a new motion path to ensure the updated plan is feasible at the
motion level. Finally, we conducted real-world experiments involving stack and
rearrange task domains. The result demonstrate GMRF's ability to swiftly
complete tasks in scenarios with varying degrees of interference
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