# Workspace Aware Online Grasp Planning

arXiv: https://arxiv.org/abs/1806.11402 · HTML: https://mlsanity.com/paper/1806.11402/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2018.

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

- [Collision-Aware Target-Driven Object Grasping in Constrained Environments](https://mlsanity.com/paper/2104.00776/) (2021)
- [Model Predictive Control for Fluid Human-to-Robot Handovers](https://mlsanity.com/paper/2204.00134/) (2022)
- [Learning suction graspability considering grasp quality and robot reachability for bin-picking](https://mlsanity.com/paper/2111.02571/) (2021)
- [ContactHandover: Contact-Guided Robot-to-Human Object Handover](https://mlsanity.com/paper/2404.01402/) (2024)

## Abstract (arXiv)

This work provides a framework for a workspace aware online grasp planner. This framework greatly improves the performance of standard online grasp planning algorithms by incorporating a notion of reachability into the online grasp planning process. Offline, a database of hundreds of thousands of unique end-effector poses were queried for feasability. At runtime, our grasp planner uses this database to bias the hand towards reachable end-effector configurations. The bias keeps the grasp planner in accessible regions of the planning scene so that the resulting grasps are tailored to the situation at hand. This results in a higher percentage of reachable grasps, a higher percentage of successful grasp executions, and a reduced planning time. We also present experimental results using simulated and real environments.