We propose a new benchmarking protocol to evaluate algorithms for bimanual robotic manipulation semi-deformable objects. The benchmark is inspired from two real-world applications: (a) watchmaking craftsmanship, and (b) belt assembly in automobile engines. We provide two setups that try to highlight the following challenges: (a) manipulating objects via a tool, (b) placing irregularly shaped objects in the correct groove, (c) handling semideformable objects, and (d) bimanual coordination. We provide CAD drawings of the task pieces that can be easily 3D printed to ensure ease of reproduction, and detailed description of tasks and protocol for successful reproduction, as well as meaningful metrics for comparison. We propose four categories of submission in an attempt to make the benchmark accessible to a wide range of related fields spanning from adaptive control, motion planning to learning the tasks through trial-and-error learning. Index Terms-Performance evaluation and benchmarking, dual arm manipulation, model learning for control, dexterous manipulation. I. INTRODUCTION A VARIETY of industrial tasks are still performed by humans today, as they require high-level precision and dexterity not yet available in robots. These tasks require the use of prehensile instruments, such as screwdrivers or tweezers, to grasp, insert, and manipulate tiny and deformable objects. Examples of such tasks are common in watchmaking craftsmanship, where both assembling and screwing are the core actions in the whole process, and in pharmaceutical industry, to handle pipettes and vials. There is interest to automatize parts of these tasks [1]. Such precise manipulation can also be

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