# Robust Grasp Planning Over Uncertain Shape Completions

## TL;DR

This paper introduces a method for planning robust grasps on objects with uncertain shapes by sampling shape completions using dropout-enabled neural networks and evaluating grasp candidates across these samples, improving success rates.

## Contribution

The paper proposes a novel shape-uncertainty-aware grasp planning approach using Monte Carlo sampling with dropout neural networks, enhancing robustness over existing methods.

## Key findings

- Significant improvement in grasp success rate over state-of-the-art methods.
- Validated robustness on 90,000 simulated grasps and 200 real-world grasps.
- Demonstrated advantages for complex or unknown objects.

## Abstract

We present a method for planning robust grasps over uncertain shape completed objects. For shape completion, a deep neural network is trained to take a partial view of the object as input and outputs the completed shape as a voxel grid. The key part of the network is dropout layers which are enabled not only during training but also at run-time to generate a set of shape samples representing the shape uncertainty through Monte Carlo sampling. Given the set of shape completed objects, we generate grasp candidates on the mean object shape but evaluate them based on their joint performance in terms of analytical grasp metrics on all the shape candidates. We experimentally validate and benchmark our method against another state-of-the-art method with a Barrett hand on 90000 grasps in simulation and 200 grasps on a real Franka Emika Panda. All experimental results show statistically significant improvements both in terms of grasp quality metrics and grasp success rate, demonstrating that planning shape-uncertainty-aware grasps brings significant advantages over solely planning on a single shape estimate, especially when dealing with complex or unknown objects.