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arxiv: 2606.11901 · v1 · pith:LVNYPIRPnew · submitted 2026-06-10 · 💻 cs.RO · cs.AI

DuoBench: A Reproducible Benchmark for Bimanual Manipulation in Simulation and the Real World

Tobias J\"ulg , Seongjin Bien , Simon Hilber , Yannik Blei , Pierre Krack , Maximilian Li , Sven Parusel , Rudolf Lioutikov
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Florian Walter Wolfram Burgard
This is my paper

Pith reviewed 2026-06-27 09:40 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords bimanual manipulationrobot benchmarkingdual-arm coordinationimitation learningsimulation to real transfervision language actionfailure analysispolicy evaluation
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The pith

DuoBench reveals that current bimanual policies struggle with early interactions, parallel arm execution, and simulation-to-reality transfer.

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

The paper introduces DuoBench as a benchmarking framework consisting of eleven tasks that fall into four coordination categories. These tasks are realized both in simulation and on physical hardware through standardized recipes and printable parts. A stage-based evaluation method breaks performance into semantic phases to identify specific failure points rather than relying on overall success rates. Testing of imitation-learning and vision-language-action policies on the benchmark shows persistent difficulties in the early phases of object contact, simultaneous arm movements, and crossing from simulation to real settings. The framework supplies teleoperated datasets to support further development of dual-arm policies.

Core claim

DuoBench comprises eleven tasks spanning four coordination categories, implemented in simulation and partially reproduced in the real world through reproducible task recipes with 3D-printable assets. It includes a stage-based evaluation scheme for fine-grained semantic failure analysis and provides human-teleoperated datasets for all tasks. Benchmarking of dual-arm imitation-learning and vision-language-action policies demonstrates that current methods remain challenged by bimanual manipulation, particularly in early interaction stages, parallel arm execution, and transfer between simulation and real-world settings.

What carries the argument

The stage-based evaluation scheme that decomposes each task into ordered phases to enable semantic diagnosis of coordination failures.

If this is right

  • Dual-arm policies require targeted improvements for the initial phases of object contact.
  • Simultaneous control of both arms must be addressed as a distinct capability.
  • Methods that reduce the performance gap between simulation and physical execution become necessary.
  • Reproducible task definitions allow consistent comparison of new algorithms across different laboratories.
  • Human teleoperation datasets can serve as direct supervision for learning coordinated behaviors.

Where Pith is reading between the lines

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

  • The stage-based scheme could be applied to single-arm or multi-robot benchmarks to expose analogous phase-specific weaknesses.
  • Failure patterns identified here may point toward policy architectures that explicitly model inter-arm dependencies.
  • Adding tasks with higher degrees of object complexity or environmental variation would test whether the observed challenges scale.
  • Combining the provided datasets with existing single-arm collections could create mixed training regimes for dual-arm learning.

Load-bearing premise

The eleven tasks and four coordination categories together capture the coordination challenges and failure modes that existing benchmarks miss.

What would settle it

A single policy that reaches high success rates in every stage of all eleven tasks in both simulation and the real world without additional coordination-specific training would indicate that the reported challenges are not general.

Figures

Figures reproduced from arXiv: 2606.11901 by Florian Walter, Maximilian Li, Pierre Krack, Rudolf Lioutikov, Seongjin Bien, Simon Hilber, Sven Parusel, Tobias J\"ulg, Wolfram Burgard, Yannik Blei.

Figure 1
Figure 1. Figure 1: Overview of DuoBench: four bimanual task categories with eleven tasks, and four repli [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FR3 Duo setup. Left shows the real-world setup with printed assets. Right shows the simulated MuJoCo scene. FR3 Duo is a novel dual-arm arrangement of FR3 robotic arms defined by the manufacturer Franka Robotics. The mounting configuration is chosen such that both robots’ ISO cubes over￾lap, ensuring strong dual-arm manipulability. Both arms have a two-finger Robotiq 2F-85 gripper attached. The setup uses … view at source ↗
Figure 3
Figure 3. Figure 3: Fraction of rollouts in simulation that failed in a given [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Average task progress over normalized time across all rollouts in simulation. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Fraction of real-world rollouts that ended in a given stage. Green means fraction of [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Visual ablation examples: original (top-left), object and texture ablation (top-middle), [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
read the original abstract

Bimanual robot systems substantially expand manipulation capabilities, but coordinating two arms introduces additional control complexity and failure modes that are not well captured by existing benchmarks. We introduce DuoBench, an extensible benchmarking framework for bimanual manipulation policies on the FR3 Duo platform. DuoBench comprises eleven tasks spanning four coordination categories, implemented in simulation and partially reproduced in the real world through reproducible task recipes with 3D-printable assets. In addition, we propose a stage-based evaluation scheme that supports fine-grained semantic failure analysis beyond binary success and provide human-teleoperated datasets for all benchmark tasks. We benchmark several dual-arm imitation-learning and vision-language-action policies in simulation and on real hardware. Our results show that current policies remain challenged by bimanual manipulation, particularly in early interaction stages, parallel arm execution, and transfer between simulation and real-world settings. DuoBench provides a reproducible testbed for diagnosing these failure modes and studying future methods for dual-arm policy learning. Code, datasets, and videos are available at https://duobench.github.io/

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 / 1 minor

Summary. The manuscript introduces DuoBench, an extensible benchmarking framework for bimanual manipulation policies on the FR3 Duo platform. It comprises eleven tasks spanning four coordination categories, implemented in simulation with partial real-world reproduction via reproducible recipes and 3D-printable assets. A stage-based evaluation scheme is proposed for fine-grained semantic failure analysis beyond binary success, human-teleoperated datasets are provided for all tasks, and several dual-arm imitation-learning and vision-language-action policies are benchmarked. The central claim is that current policies remain challenged by bimanual manipulation (particularly early interaction stages, parallel arm execution, and sim-to-real transfer) and that DuoBench supplies a reproducible testbed for diagnosing these issues.

Significance. If the tasks and stage-based scheme prove effective at exposing coordination and transfer failure modes not captured by prior benchmarks, and if the reproducibility elements (public code, datasets, and assets) function as described, the work could provide a useful standardized testbed for dual-arm policy research. The inclusion of human datasets and partial real-world validation are positive elements that support imitation learning and sim-to-real studies.

major comments (2)
  1. [Abstract] Abstract: the claim that 'our results show that current policies remain challenged' is asserted without reference to specific quantitative outcomes (e.g., success rates per task or stage, number of trials, or statistical measures), which is load-bearing for the diagnostic value of the benchmark.
  2. [Task and evaluation design] Task and evaluation design (Abstract and § on benchmark tasks): the assertion that the eleven tasks and stage-based scheme capture coordination challenges and failure modes missed by existing benchmarks is central to the contribution but lacks explicit comparative analysis or evidence demonstrating unique diagnostic power relative to prior work.
minor comments (1)
  1. [Reproducibility] Reproducibility section: confirm that all linked assets (code, datasets, 3D models) include complete setup instructions matching the claimed partial real-world reproduction to avoid ambiguity for users.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'our results show that current policies remain challenged' is asserted without reference to specific quantitative outcomes (e.g., success rates per task or stage, number of trials, or statistical measures), which is load-bearing for the diagnostic value of the benchmark.

    Authors: We agree that the abstract would benefit from explicit quantitative support for the central claim. The full manuscript contains the relevant experimental results (success rates by task, stage, and policy type, along with trial counts), but these are not summarized in the abstract. In the revised version we will add concise references to key outcomes, such as aggregate success rates for the evaluated imitation-learning and VLA policies and the number of trials per task, while preserving the abstract's length and focus. revision: yes

  2. Referee: [Task and evaluation design] Task and evaluation design (Abstract and § on benchmark tasks): the assertion that the eleven tasks and stage-based scheme capture coordination challenges and failure modes missed by existing benchmarks is central to the contribution but lacks explicit comparative analysis or evidence demonstrating unique diagnostic power relative to prior work.

    Authors: The manuscript introduces novel tasks spanning four coordination categories and a stage-based evaluation that enables finer-grained failure analysis than binary success metrics common in prior benchmarks. Our benchmarking results highlight specific issues (early-stage interaction, parallel execution, sim-to-real gaps) that arise under these tasks. However, we acknowledge that an explicit side-by-side comparison of diagnostic power versus representative prior benchmarks is not currently present. We will add a short comparative paragraph in the benchmark tasks section that directly contrasts the failure modes surfaced by DuoBench with those reported in existing single-arm or less-coordinated benchmarks, using concrete examples from our data. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is an empirical benchmark paper with no mathematical derivations, equations, fitted parameters, or predictions. The eleven tasks, four coordination categories, stage-based evaluation, and policy benchmarks are defined directly from task requirements and external policy implementations; reproducibility rests on linked code, datasets, and 3D assets rather than any self-referential construction. No self-citation load-bearing steps, ansatzes, or renamings appear. The central claims rest on external comparisons to prior benchmarks and observed policy failures, making the work self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical benchmark paper with no mathematical derivations, fitted parameters, or physical postulates; the contribution consists of defined tasks, categories, and evaluation protocols rather than any derived quantities.

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