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arxiv: 2605.21429 · v1 · pith:RATVIA4Hnew · submitted 2026-05-20 · 💻 cs.RO · cs.LG

roto 2.0: The Robot Tactile Olympiad

Elle Miller , Jayaram Reddy , Ayush Deshmukh , Trevor McInroe , David Abel , Oisin Mac Aodha , Sethu Vijayakumar This is my paper

Pith reviewed 2026-05-21 03:28 UTC · model grok-4.3

classification 💻 cs.RO cs.LG
keywords tactile reinforcement learningblind manipulationrobot benchmarkBaoding ballsproprioceptiontactile sensingGPU parallelizationrobotic morphologies
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The pith

roto 2.0 provides a GPU-parallelized benchmark for blind tactile reinforcement learning across four robot morphologies, where agents reach 13 Baoding ball rotations in 10 seconds using only proprioception and touch.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces roto 2.0 as a standardized benchmark to address fragmentation in tactile-based reinforcement learning research. It supplies environments for four robotic morphologies between 16 and 24 degrees of freedom and restricts agents to proprioceptive and tactile inputs alone. This setup removes reliance on external state information or knowledge distillation. The reported result shows agents completing 13 ball rotations in 10 seconds, an order of magnitude improvement over prior speeds. Open-sourcing the environments and tuned baselines is intended to let researchers concentrate on algorithmic advances rather than repeated tuning work.

Core claim

roto 2.0 is a benchmark for end-to-end blind tactile manipulation that runs in parallel on GPUs and covers four distinct robotic morphologies. It uses only proprioceptive and tactile inputs to control robots in a Baoding ball task, without providing state information or using distillation techniques. Agents trained in this setup achieve 13 rotations in 10 seconds, representing a substantial increase over previous state-of-the-art performance levels.

What carries the argument

The GPU-parallelised roto 2.0 environments supporting blind manipulation on 16-DOF to 24-DOF robot morphologies using only proprioception and tactile sensing.

If this is right

  • Standardized comparison of tactile RL methods becomes possible across different robot designs.
  • Researchers can prioritize algorithmic innovations over environment-specific tuning.
  • Performance in blind manipulation tasks can reach significantly higher levels than before.
  • Lower barrier to entry allows more researchers to work on core tactile RL problems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Methods that succeed here may generalize better to varied real-world tactile challenges with uncertainty.
  • The benchmark could be extended to include additional morphologies or tasks to test broader applicability.
  • Similar blind setups might be adapted for other sensing modalities to explore cross-modal learning.

Load-bearing premise

The four chosen morphologies and the Baoding ball task sufficiently represent the space of real-world blind tactile manipulation challenges.

What would settle it

A demonstration that agents trained on roto 2.0 fail to improve performance or transfer to a different tactile task or robot morphology outside the benchmark would challenge the claim of standardization benefit.

Figures

Figures reproduced from arXiv: 2605.21429 by Ayush Deshmukh, David Abel, Elle Miller, Jayaram Reddy, Oisin Mac Aodha, Sethu Vijayakumar, Trevor McInroe.

Figure 1
Figure 1. Figure 1: The roto 2.0 Benchmark Suite: A standardised RL framework across four distinct dexterous morphologies (from L to R): ORCA Hand, Shadow Lite, Allegro Hand, and the Shadow Dexterous Hand. The suite facilitates “blind" tactile manipulation tasks, such as Baoding ball rotation and ball bouncing. Abstract—Tactile-based reinforcement learning (RL) is cur￾rently hindered by fragmented research and a focus on over… view at source ↗
Figure 2
Figure 2. Figure 2: Mean evaluation returns across 5 seeds for state-based and blind [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
read the original 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, 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/

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces roto 2.0, a GPU-parallelized benchmark for tactile-based RL across four robotic morphologies (16-DOF to 24-DOF). It emphasizes end-to-end blind manipulation using only proprioception and tactile sensing without state information or distillation. The central empirical claim is that blind agents achieve 13 Baoding ball rotations in 10 seconds, asserted to be an order of magnitude faster than current state-of-the-art speeds, with open-sourced environments and tuned baselines provided to lower barriers for research.

Significance. If the performance claims hold under verified identical blind conditions and the chosen morphologies/tasks prove representative, the benchmark could standardize evaluation in tactile RL, shifting focus from environment tuning to algorithmic challenges. The open-sourcing of environments and baselines is a concrete strength supporting reproducibility.

major comments (2)
  1. [Abstract and results section] Abstract and results section: The headline claim of 13 Baoding ball rotations in 10 seconds as an order-of-magnitude improvement is load-bearing for the paper's contribution, yet the manuscript does not quote exact prior SOTA speeds, confirm identical blind observation spaces (proprioception + tactile only, no privileged information), or verify equivalent rotation-counting metrics and morphologies. If prior work used vision, distillation, or different conventions, the leap does not hold.
  2. [Methods/experimental setup] Methods/experimental setup: No details are supplied on training procedures, baseline implementations, statistical significance testing, or verification methods for the reported performance numbers, preventing assessment of whether the data actually supports the central performance claim.
minor comments (2)
  1. [Abstract] The abstract would be clearer if it briefly listed the four specific morphologies and DOF counts rather than the range alone.
  2. [Figures] Figure captions and axis labels in any performance plots should explicitly state the observation space and whether results are blind or privileged to aid direct comparison.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We address each major comment below and outline the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract and results section] Abstract and results section: The headline claim of 13 Baoding ball rotations in 10 seconds as an order-of-magnitude improvement is load-bearing for the paper's contribution, yet the manuscript does not quote exact prior SOTA speeds, confirm identical blind observation spaces (proprioception + tactile only, no privileged information), or verify equivalent rotation-counting metrics and morphologies. If prior work used vision, distillation, or different conventions, the leap does not hold.

    Authors: We agree that explicit, side-by-side comparisons are required to support the central claim. The manuscript already cites relevant prior tactile RL works, but we will add a new comparison table in the results section that lists exact reported speeds, observation spaces (explicitly noting which use vision, privileged state, or distillation), morphologies, and rotation-counting conventions from the original papers. We will also insert a clarifying sentence in the abstract and results stating that our agents operate under strictly blind conditions using only proprioception and tactile sensing. These additions will allow direct verification of the claimed improvement. revision: yes

  2. Referee: [Methods/experimental setup] Methods/experimental setup: No details are supplied on training procedures, baseline implementations, statistical significance testing, or verification methods for the reported performance numbers, preventing assessment of whether the data actually supports the central performance claim.

    Authors: We accept that the current manuscript is insufficiently detailed on these points. In the revised version we will expand the experimental setup and appendix to include: (i) complete training hyperparameters and network architectures, (ii) descriptions of how each baseline was implemented and tuned, (iii) performance statistics (mean and standard deviation) across multiple random seeds, and (iv) explicit verification procedures such as automated rotation logging and episode rollout checks. Full code and configuration files are already open-sourced; we will add direct pointers and a reproducibility checklist. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark without derivations

full rationale

The paper introduces roto 2.0 as a GPU-parallelised benchmark for blind tactile RL across morphologies and reports experimental results (13 Baoding ball rotations in 10 s). No equations, first-principles derivations, fitted parameters renamed as predictions, or self-referential definitions appear in the provided text. Performance claims rest on direct simulation outcomes rather than any chain that reduces to its own inputs by construction. Self-citations, if present in the full manuscript, are not load-bearing for any derivation because none exists. The contribution is self-contained as an open benchmark with reported baselines.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, the paper introduces no new mathematical axioms, free parameters, or invented physical entities; it describes an empirical benchmark and associated RL training results.

pith-pipeline@v0.9.0 · 5707 in / 1035 out tokens · 44509 ms · 2026-05-21T03:28:08.716200+00:00 · methodology

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Reference graph

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