Reproduce “GOMP: Grasp-Optimized Motion Planning for Bin Picking” 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
Jeffrey Ichnowski, Michael Danielczuk, Jingyi Xu, Vishal Satish, Ken Goldberg
A framework from UC Berkeley's AUTOLAB introduces GOMP, a time-optimized motion planning algorithm that integrates grasp orientation and robot mechanical limits for bin picking. This system reduces robot pick-and-place motion execution times by an average of 9.2 times compared to a baseline, enhancing overall warehouse automation efficiency.
Key insights
- Allowing a rotational degree of freedom around the grasp axis during motion planning can yield substantially faster trajectories by enabling the robot to find an optimal, less constrained approach angle.
- An iterative time-minimization strategy that progressively reduces the trajectory horizon within an SQP framework, while explicitly modeling robot dynamics, effectively converges to minimum-time feasible motions.
- Integrating advanced grasp analysis outputs with a dynamics-aware, optimizing motion planner can effectively address the motion planning bottleneck in the end-to-end robotic bin-picking pipeline.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.