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Under review as a conference paper at ICLR 2027

Bench2Dex: A Bimanual Manipulation Benchmark across Dexterous Hands with Simulated Fingertip Visuo-Tactile Support

Abstract

Bimanual dexterous manipulation requires coordinated control of two hands in multi-stage, contact-rich tasks. We introduce Bench2Dex, a simulation benchmark for bimanual manipulation supporting 12 dexterous hands. The benchmark includes 26 tasks involving tool use, interaction with articulated objects, and multi-stage coordination, together with approximately 1.3K human-teleoperated demonstrations. It integrates demonstration collection, synchronized multimodal data, and automated metrics for task completion and intermediate progress within a common framework. Taking a deliberate step beyond vision-only sensing, we equip every supported hand model with simulated vision-based tactile sensing at the fingertips, encoding local contact geometry as image-like tactile observations. Through a unified visuo-tactile interface, we make these observations available alongside external vision and proprioception to support research on tactile representations and multimodal policy learning. We establish reference results using ACT, Diffusion Policy, , and GR00T N1.5 with visual and proprioceptive inputs. We assess robustness across four evaluation channels—None, Equivariance-only, Invariance-only, and Full—covering an unperturbed baseline and controlled combinations of seven scene perturbation types. We further conduct an exploratory GR00T N1.5 comparison with and without tactile input. Averaged across the four evaluation channels, the reported stable-success rates are higher with tactile input on 13 of the 26 tasks and lower on the remaining 13, with task-averaged rates of 27.3% with tactile input versus 28.3% without. By combining a bimanual manipulation benchmark across dexterous hands with simulated visuo-tactile support and initial tactile-conditioned policy evaluations, Bench2Dex provides the community with a common starting point for investigating when tactile information is useful and how manipulation policies can effectively incorporate it.

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