# TACTFUL: Tactile-Driven Exploration For Object Localization and Identification in Confined Environments

arXiv: https://arxiv.org/abs/2606.24712 · HTML: https://mlsanity.com/paper/2606.24712/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Builds on (most cited first, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [Sonata: Self-Supervised Learning of Reliable Point Representations](https://mlsanity.com/paper/2503.16429/) (2025)
- [3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing](https://mlsanity.com/paper/2410.24091/) (2024)
- [ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning](https://mlsanity.com/paper/2503.21860/) (2025)
- [Tactile Mapping and Localization from High-Resolution Tactile Imprints](https://mlsanity.com/paper/1904.10944/) (2019)
- [AcTExplore: Active Tactile Exploration of Unknown Objects](https://mlsanity.com/paper/2310.08745/) (2023)
- [DexTouch: Learning to Seek and Manipulate Objects with Tactile Dexterity](https://mlsanity.com/paper/2401.12496/) (2024)
- [TANDEM3D: Active Tactile Exploration for 3D Object Recognition](https://mlsanity.com/paper/2209.08772/) (2022)
- [DexSkills: Skill Segmentation Using Haptic Data for Learning Autonomous Long-Horizon Robotic Manipulation Tasks](https://mlsanity.com/paper/2405.03476/) (2024)
- [A Short Survey On Memory Based Reinforcement Learning](https://mlsanity.com/paper/1904.06736/) (2019)
- [Touch if it's transparent! ACTOR: Active Tactile-based Category-Level Transparent Object Reconstruction](https://mlsanity.com/paper/2307.16254/) (2023)
- [Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models](https://mlsanity.com/paper/2408.06145/) (2024)
- [Learning efficient haptic shape exploration with a rigid tactile sensor array](https://mlsanity.com/paper/1902.07501/) (2019)
- [TactoFind: A Tactile Only System for Object Retrieval](https://mlsanity.com/paper/2303.13482/) (2023)
- [simPLE: a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects](https://mlsanity.com/paper/2307.13133/) (2023)
- [ViTa-Zero: Zero-shot Visuotactile Object 6D Pose Estimation](https://mlsanity.com/paper/2504.13179/) (2025)
- [Apple: Toward General Active Perception via Reinforcement Learning](https://mlsanity.com/paper/2505.06182/) (2025)

## Abstract (arXiv)

Humans effortlessly locate and identify objects by touch alone, even without vision. In contrast, robotic systems rely heavily on vision and struggle with autonomous tactile exploration and object identification. We present TACTFUL, a vision-free tactile exploration framework that enables a multi-fingered robot to autonomously explore confined workspaces, discover objects through contact, and identify them via tactile reconstruction. Trained entirely on real hardware without simulation, our system learns a single policy that balances global workspace exploration with local surface refinement through a dynamic reward schedule. Our results demonstrate that tactile sensing, when paired with structured learning, can serve as an effective primary modality for object-level reasoning, achieving 77% success with 0.015 m average reconstruction error and outperforming baseline approaches on real-world objects.