arXiv:2605.21429v1 Announce Type: cross Abstract: 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 (\texttt{roto 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, w
The proliferation of advanced robotics and AI necessitates standardized benchmarks for developing more capable, commercially viable robotic systems capable of operating in unstructured environments.
This benchmark represents a significant step towards enabling 'blind' end-to-end manipulation, which is crucial for robots performing complex tasks without reliance on external visual state information, accelerating advancements in tactile-based reinforcement learning.
The introduction of roto 2.0 provides a standardized, GPU-parallelized benchmark for tactile-based reinforcement learning across diverse robotic morphologies, focusing on 'blind' manipulation, which could lead to a performance leap in robotic dexterity and autonomy.
- · Robotics research institutions
- · AI/ML developers in robotics
- · Robotics hardware manufacturers
- · Industrial automation sector
- · Companies reliant on primitive robotic manipulation
- · Research groups using fragmented, non-standardized benchmarks
Standardized benchmarks accelerate the development of tactile-sensing and manipulation capabilities in diverse robotic platforms.
Improved tactile intelligence reduces reliance on visual sensing, making robots more robust in challenging environments and expanding their application domains.
Advanced tactile-enabled robots could accelerate the commercial viability and widespread adoption of general-purpose humanoid robots and autonomous industrial systems.
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Read at arXiv cs.LG