# Learning Stable Robot Grasping with Transformer-based Tactile Control Policies

arXiv: https://arxiv.org/abs/2407.21172 · HTML: https://mlsanity.com/paper/2407.21172/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [Resource-Efficient Affordance Grounding with Complementary Depth and Semantic Prompts](https://mlsanity.com/paper/2503.02600/) (2025)

## Abstract (arXiv)

Measuring grasp stability is an important skill for dexterous robot manipulation tasks, which can be inferred from haptic information with a tactile sensor. Control policies have to detect rotational displacement and slippage from tactile feedback, and determine a re-grasp strategy in term of location and force. Classic stable grasp task only trains control policies to solve for re-grasp location with objects of fixed center of gravity. In this work, we propose a revamped version of stable grasp task that optimises both re-grasp location and gripping force for objects with unknown and moving center of gravity. We tackle this task with a model-free, end-to-end Transformer-based reinforcement learning framework. We show that our approach is able to solve both objectives after training in both simulation and in a real-world setup with zero-shot transfer. We also provide performance analysis of different models to understand the dynamics of optimizing two opposing objectives.