# Bayesian optimization for robust robotic grasping using a sensorized compliant hand

arXiv: https://arxiv.org/abs/2410.18237 · HTML: https://mlsanity.com/paper/2410.18237/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [Robotic Grasping using Deep Reinforcement Learning](https://mlsanity.com/paper/2007.04499/) (2020)
- [Robotic Tactile Perception of Object Properties: A Review](https://mlsanity.com/paper/1711.03810/) (2017)
- [BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits](https://mlsanity.com/paper/1405.7430/) (2014)
- [Unscented Bayesian Optimization for Safe Robot Grasping](https://mlsanity.com/paper/1603.02038/) (2016)
- [Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing](https://mlsanity.com/paper/2307.14272/) (2023)
- [Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects](https://mlsanity.com/paper/2103.00655/) (2021)
- [Finding safe 3D robot grasps through efficient haptic exploration with unscented Bayesian optimization and collision penalty](https://mlsanity.com/paper/2402.07024/) (2024)

## Built on by (most cited first, 2 of 2)

- [ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization](https://mlsanity.com/paper/2508.11551/) (2025)
- [Maximally Robust Satisficing Bayesian Optimization](https://mlsanity.com/paper/2607.13652/) (2026)

## Abstract (arXiv)

One of the first tasks we learn as children is to grasp objects based on our tactile perception. Incorporating such skill in robots will enable multiple applications, such as increasing flexibility in industrial processes or providing assistance to people with physical disabilities. However, the difficulty lies in adapting the grasping strategies to a large variety of tasks and objects, which can often be unknown. The brute-force solution is to learn new grasps by trial and error, which is inefficient and ineffective. In contrast, Bayesian optimization applies active learning by adding information to the approximation of an optimal grasp. This paper proposes the use of Bayesian optimization techniques to safely perform robotic grasping. We analyze different grasp metrics to provide realistic grasp optimization in a real system including tactile sensors. An experimental evaluation in the robotic system shows the usefulness of the method for performing unknown object grasping even in the presence of noise and uncertainty inherent to a real-world environment.