Human-Guided Planner for Non-Prehensile Manipulation
This addresses a domain-specific problem in robotics manipulation, offering an incremental improvement over existing randomized planning methods.
The paper tackles the challenge of slow planning times and low success rates in non-prehensile manipulation in clutter by incorporating minimal human operator input into control-based randomized planning, resulting in faster problem-solving and higher success rates.
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.