# ManiFeel: Benchmarking and Understanding Visuotactile Manipulation Policy Learning

arXiv: https://arxiv.org/abs/2505.18472 · HTML: https://mlsanity.com/paper/2505.18472/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2025.

## Built on by (most cited first, 13 of 13)

- [World Action Models: The Next Frontier in Embodied AI](https://mlsanity.com/paper/2605.12090/) (2026)
- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
- [Tactile-WAM: Touch-Aware World Action Model with Tactile Asymmetric Attention](https://mlsanity.com/paper/2606.26663/) (2026)
- [ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation](https://mlsanity.com/paper/2606.13877/) (2026)
- [SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects (Early Version)](https://mlsanity.com/paper/2607.04234/) (2026)
- [Agile-WAM: An Agile Tactile World Action Model for Contact-Rich Robot Control](https://mlsanity.com/paper/2609.20761/) (2026)
- [UNIC: Learning Unified Multimodal Extrinsic Contact Estimation](https://mlsanity.com/paper/2601.04356/) (2026)
- [Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception](https://mlsanity.com/paper/2605.05053/) (2026)
- [Dense Force Estimation with an Event-based Optical Tactile Sensor](https://mlsanity.com/paper/2606.09451/) (2026)
- [VT-MUSE: Multimodal Unified Sequential Visuotactile Representation Learning for Manipulation](https://mlsanity.com/paper/2608.21290/) (2026)
- [ME-Dex 1.0: Bringing Heterogeneous Tactile Sensing into World Action Modeling](https://mlsanity.com/paper/2609.21449/) (2026)
- [Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation](https://mlsanity.com/paper/2609.35450/) (2026)
- [Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation](https://mlsanity.com/paper/2610.03333/) (2026)

## Abstract (arXiv)

Supervised visuomotor policies have shown strong performance in robotic manipulation but often struggle in tasks with limited visual inputs, such as operations in confined spaces and dimly lit environments, or tasks requiring precise perception of object properties and environmental interactions. In such cases, tactile feedback becomes essential for manipulation. While the rapid progress of supervised visuomotor policies has benefited greatly from high-quality, reproducible simulation benchmarks in visual imitation, the visuotactile domain still lacks a similarly comprehensive and reliable benchmark for large-scale and rigorous evaluation. To address this, we introduce ManiFeel, a reproducible and scalable simulation benchmark designed to systematically study supervised visuotactile policy learning. ManiFeel offers a diverse suite of contact-rich and visually challenging manipulation tasks, a modular evaluation pipeline spanning sensing modalities, tactile representations, and policy architectures, as well as real-world validation. Through extensive experiments, ManiFeel demonstrates how tactile sensing enhances policy performance across diverse manipulation scenarios, ranging from precise contact-driven operations to visually constrained settings. In addition, the results reveal task-dependent strengths of different tactile modalities and identify key design principles and open challenges for robust visuotactile policy learning. Real-world evaluations further confirm that ManiFeel provides a reliable and meaningful foundation for benchmarking and future visuotactile policy development. To foster reproducibility and future research, we will release our codebase, datasets, training logs, and pretrained checkpoints, aiming to accelerate progress toward generalizable visuotactile policy learning and manipulation.