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You can download the CAD files (STEP files) of the watch parts here. Choosing 3D printing technologies depends on users preference and potential however, the only constraint is that the technology must have precision up to 1mm for watchface and 0.5mm for other components. We expect the potential users of the benchmark to 3D print them using technologies similar to FDM for watchface (black material in figure) and SLA for other components (white material in figure). Furthermore, to increase reproducibility, we provide the CAD files of the parts in the original and the two scaled-up versions. These ratios are computed such that standard components like screws are available for the scaled-up watch. To this end, we have devised two different scaled-up versions: (a) around 3.5×, and (b)5.8× bigger than the actual one. Hence, to avoid any additional challenge to an already difficult problem, we propose to use scaled-up versions of the parts. While, in the long run, many commercial solutions will be offered to robotic arms precise enough to manipulate such small objects, it is not realistic to expect many labs to have such highly precise robots today. The real watch from which this task is inspired is quite small with a diameter of 37mm. This requires orienting the piece correctly and then bending its leg to stay in place. At onset, the plate is already held stiffly in the tweezers (i.e., the robot does not have to pick it up).The arms must insert the plate in the correct location/groove on the watch face. One arm holds/supports the tweezers at its end-effector,while the other arm holds/supports the stick (or similar tool)at its end-effector. The task should be performed by a pair of robotic arms as follows (see figure below): The tools that are used in the actual setup are (shown in the following figure): the tweezers, a screwdriver and a wooden tool. Additionally, its small size requires the usage of a second tool to keep the object stable. The plate is semi-deformable, and it necessitates bending in order to put it in place.
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.Īuthors: Konstantinos Chatzilygeroudis, Bernardo Fichera, Ilaria Lauzana, Fanjun Bu, Kunpeng Yao, Farshad Khadivar, and Aude Billard Tasks 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 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 semi-deformable objects, and (d) bimanual coordination. The benchmark is inspired from two real-world applications: (a) watchmaking craftsmanship, and (b) belt assembly in automobile engines.
We propose a new benchmarking protocol to evaluate algorithms for bimanual robotic manipulation semi-deformable objects. Here we provide information and data required to replicate the benchmark setup and evaluation protocol.
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