DuoBench: A Reproducible Benchmark for Bimanual Manipulation in Simulation and the Real World
arXiv:2606.11901v1 Announce Type: cross 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
The increasing sophistication of robotic hardware and AI control methods necessitates more robust and standardized evaluation benchmarks for complex manipulation tasks.
A standardized benchmark for bimanual manipulation is crucial for accelerating progress in robotics, enabling direct comparison of different policies and fostering innovation.
The introduction of DuoBench provides a common framework and set of tasks that will allow researchers to more effectively develop and assess bimanual robot capabilities in both simulation and the real world.
- · Robotics researchers
- · Automation industry
- · Robot manufacturers
- · Proprietary, non-standardized robot testing methods
More rapid development and deployment of advanced bimanual robotic systems will occur due to clearer performance metrics.
Improved bimanual manipulation capabilities will enable robots to perform more complex tasks in manufacturing, logistics, and healthcare, increasing automation adoption.
As bimanual robots become more adept and cost-effective, they could begin to displace human labor in tasks requiring dual-arm coordination, impacting employment in specific sectors.
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Read at arXiv cs.AI