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Tools for Data-driven Modeling of Within-Hand Manipulation with Underactuated Adaptive Hands

Precise in-hand manipulation is an important skill for a robot to perform tasks in human environments. Practical robotic hands must be low-cost, easy to control and capable. 3D-printed underactuated adaptive hands provide such properties as they are cheap to fabricate and adapt to objects of uncertain geometry with stable grasps. Challenges still remain, however, before such hands can attain human-like performance due to complex dynamics and contacts. In particular, useful models for planning, control or model-based reinforcement learning are still lacking. Recently, data-driven approaches for such models have shown promise. This work provides the first large public dataset of real within-hand manipulation that facilitates building such models, along with baseline data-driven modeling results. Furthermore, it contributes ROS-based physics-engine model of such hands for independent data collection, experimentation and sim-to-reality transfer work.

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