Non-Prehensile Manipulation in Clutter with Human-In-The-Loop
This work addresses manipulation in cluttered environments for robotics, offering an incremental improvement by integrating human input to enhance existing planning methods.
The paper tackles the challenge of slow planning times and low success rates in non-prehensile manipulation in clutter by introducing a human-in-the-loop approach where operators provide high-level plans, resulting in faster planning and higher success rates compared to fully autonomous methods.
We propose a human-operator guided planning approach to pushing-based manipulation in clutter. Most recent approaches to manipulation in clutter employs randomized planning. The problem, however, 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. In our framework, the human operator supplies a high-level plan, in the form of an ordered sequence of objects and their approximate goal positions. We present experiments in simulation and on a real robotic setup, where we compare the success rate and planning times of our human-in-the-loop approach with fully autonomous sampling-based planners. We show that with a minimal amount of human input, the low-level planner can solve the problem faster and with higher success rates.