# Non-Prehensile Manipulation in Clutter with Human-In-The-Loop

arXiv: https://arxiv.org/abs/1904.03748 · HTML: https://mlsanity.com/paper/1904.03748/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Semi-Autonomous Teleoperation via Learning Non-Prehensile Manipulation Skills](https://mlsanity.com/paper/2109.13081/) (2021)
- [Lazy Rearrangement Planning in Confined Spaces](https://mlsanity.com/paper/2203.10379/) (2022)
- [Using Memory-Based Learning to Solve Tasks with State-Action Constraints](https://mlsanity.com/paper/2303.04327/) (2023)
- [Learning Manipulation Tasks in Dynamic and Shared 3D Spaces](https://mlsanity.com/paper/2404.17673/) (2024)

## Abstract (arXiv)

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.