Human-Guided Planner for Non-Prehensile Manipulation
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We present a human-guided planner for non-prehensile manipulation in clutter. Most recent approaches to manipulation in clutter employs randomized planning, however, the problem remains a challenging one where the planning times are still in the order of tens of seconds or minutes, and the success rates are low for difficult instances of the problem. We build on these control-based randomized planning approaches, but we investigate using them in conjunction with human-operator input. We show that with a minimal amount of human input, the low-level planner can solve the problem faster and with higher success rates.
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