# Robust Grasp Planning Over Uncertain Shape Completions

arXiv: https://arxiv.org/abs/1903.00645 · HTML: https://mlsanity.com/paper/1903.00645/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2019.

## Built on by (most cited first, 16 of 16)

- [Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations](https://mlsanity.com/paper/2104.01542/) (2021)
- [RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images](https://mlsanity.com/paper/2103.02184/) (2021)
- [DDGC: Generative Deep Dexterous Grasping in Clutter](https://mlsanity.com/paper/2103.04783/) (2021)
- [3DSGrasp: 3D Shape-Completion for Robotic Grasp](https://mlsanity.com/paper/2301.00866/) (2023)
- [Active Visuo-Haptic Object Shape Completion](https://mlsanity.com/paper/2203.09149/) (2022)
- [Uncertainty-aware 3D Object-Level Mapping with Deep Shape Priors](https://mlsanity.com/paper/2309.09118/) (2023)
- [Shape Completion with Prediction of Uncertain Regions](https://mlsanity.com/paper/2308.00377/) (2023)
- [Fine-Tuning Generative Models as an Inference Method for Robotic Tasks](https://mlsanity.com/paper/2310.12862/) (2023)
- [FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference](https://mlsanity.com/paper/2407.15161/) (2024)
- [Towards Confidence-guided Shape Completion for Robotic Applications](https://mlsanity.com/paper/2209.04300/) (2022)
- [You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects](https://mlsanity.com/paper/2404.03462/) (2024)
- [Local Occupancy-Enhanced Object Grasping with Multiple Triplanar Projection](https://mlsanity.com/paper/2407.15771/) (2024)
- [PCF-Grasp: Converting Point Completion to Geometry Feature to Enhance 6-DoF Grasp](https://mlsanity.com/paper/2504.16320/) (2025)
- [Fusing RGBD Tracking and Segmentation Tree Sampling for Multi-Hypothesis Volumetric Segmentation](https://mlsanity.com/paper/2104.00205/) (2021)
- [Robust Bayesian Scene Reconstruction with Retrieval-Augmented Priors for Precise Grasping and Planning](https://mlsanity.com/paper/2411.19461/) (2024)
- [Object Pose and Shape Estimation for Grasping: Does it Work?](https://mlsanity.com/paper/2605.26944/) (2026)

## Abstract (arXiv)

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.