Fig. 1. DexPilot enabled teleoperation across a wide variety of tasks, e.g., rectifying a Pringles can and placing it inside the red bowl (upper-left), inserting cups (upper-right), concurrently picking two cubes with four fingers (lower-left), and extracting money from a wallet (lower-right). Videos are available at https://sites.google.com/view/dex-pilot.

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This article presents a set of performance metrics, test methods, and associated artifacts to help progress the development and deployment of robotic assembly systems. The designs for three task board artifacts that replicate small part insertion and fastening operations such as threading, snap fitting, and meshing with standard screws, nuts, washers, gears, electrical connectors, belt drives, and wiring are presented. To support the evaluation of robotic assembly and disassembly operations, benchmarking protocols and performance metrics are presented that leverage these task boards. Finally, robot competitions are discussed as use cases for these task boards.

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In the human hand, high-density contact information provided by afferent neurons is essential for many human grasping and manipulation capabilities. In contrast, robotic tactile sensors, including the state-of-the-art SynTouch BioTac, are typically used to provide low-density contact information, such as contact location, center of pressure, and net force. Although useful, these data do not convey or leverage the rich information content that some tactile sensors naturally measure. This research extends robotic tactile sensing beyond reducedorder models through 1) the automated creation of a precise experimental tactile dataset for the BioTac over a diverse range of physical interactions, 2) a 3D finite element (FE) model of the BioTac, which complements the experimental dataset with high-density, distributed contact data, 3) neural-network-based mappings from raw BioTac signals to not only low-dimensional experimental data, but also high-density FE deformation fields, and 4) mappings from the FE deformation fields to the raw signals themselves. The high-density data streams can provide a far greater quantity of interpretable information for grasping and manipulation algorithms than previously accessible.