Reproduce “Neural Rearrangement Planning for Object Retrieval from Confined Spaces Perceivable by Robot's In-hand RGB-D Sensor” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Hanwen Ren, Ahmed H. Qureshi
A neural framework allows robots to retrieve target objects efficiently from unknown, cluttered, and confined spaces by actively perceiving the environment with an in-hand RGB-D sensor. This system achieves a 72% retrieval success rate in simulation and an average planning time of 0.014 seconds, demonstrating robust real-world deployment.
Key insights
- Learning-based approaches, specifically neural networks, can drastically reduce the computational time for complex rearrangement planning tasks while maintaining high success rates.
- Employing an active sensing strategy with an in-hand RGB-D camera enables robust perception and scene understanding in previously inaccessible confined and cluttered environments.
- Designing object selection and placement strategies with path homotopy awareness enhances the efficiency and success of subsequent motion planning in constrained spaces.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.