# roto 2.0: The Robot Tactile Olympiad

arXiv: https://arxiv.org/abs/2605.21429 · HTML: https://mlsanity.com/paper/2605.21429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 18 of its 20 references that are in the map.
- First posted on arXiv 20 May 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [A System for General In-Hand Object Re-Orientation](https://mlsanity.com/paper/2111.03043/) (2021)
- [In-Hand Object Rotation via Rapid Motor Adaptation](https://mlsanity.com/paper/2210.04887/) (2022)
- [HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation](https://mlsanity.com/paper/2403.10506/) (2024)
- [General In-Hand Object Rotation with Vision and Touch](https://mlsanity.com/paper/2309.09979/) (2023)
- [Rotating without Seeing: Towards In-hand Dexterity through Touch](https://mlsanity.com/paper/2303.10880/) (2023)
- [Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing](https://mlsanity.com/paper/2312.01853/) (2023)
- [skrl: Modular and Flexible Library for Reinforcement Learning](https://mlsanity.com/paper/2202.03825/) (2022)
- [Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation](https://mlsanity.com/paper/2312.13469/) (2023)
- [Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning](https://mlsanity.com/paper/2505.11164/) (2025)
- [AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch](https://mlsanity.com/paper/2405.07391/) (2024)
- [Learning Purely Tactile In-Hand Manipulation with a Torque-Controlled Hand](https://mlsanity.com/paper/2204.03698/) (2022)
- [Tactile Gym 2.0: Sim-to-real Deep Reinforcement Learning for Comparing Low-cost High-Resolution Robot Touch](https://mlsanity.com/paper/2207.10763/) (2022)
- [TacGNN:Learning Tactile-based In-hand Manipulation with a Blind Robot](https://mlsanity.com/paper/2304.00736/) (2023)
- [Estimator-Coupled Reinforcement Learning for Robust Purely Tactile In-Hand Manipulation](https://mlsanity.com/paper/2311.04060/) (2023)
- [Enhancing Tactile-based Reinforcement Learning for Robotic Control](https://mlsanity.com/paper/2510.21609/) (2025)

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

- [Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands](https://mlsanity.com/paper/2609.15726/) (2026)

## Abstract (arXiv)

Tactile-based reinforcement learning (RL) is currently hindered by fragmented research and a focus on over-saturated orientation tasks. We introduce v2 of the Robot Tactile Olympiad (textttroto 2.0), a GPU-parallelised benchmark designed to standardise tactile-based RL across four distinct robotic morphologies (16-DOF to 24-DOF). Unlike prior benchmarks, roto focuses on end-to-end "blind" manipulation, utilising only proprioception and tactile sensing without state information or distillation. We demonstrate a significant performance leap, with our blind agents achieving 13 Baoding ball rotations in 10 seconds, an order of magnitude faster than current state-of-the-art speeds. By open-sourcing our environments and robustly tuned baselines, we reduce the barrier to entry and enable researchers to prioritise fundamental algorithmic challenges over tedious RL tuning. Website: https://elle-miller.github.io/roto/