  The accurate modeling of real-world systems and physical interactions is a
common challenge towards the resolution of robotics tasks. Machine learning
approaches have demonstrated significant results in the modeling of complex
systems (e.g., articulated robot structures, cable stretch, fluid dynamics), or
to learn robotics tasks (e.g., grasping, reaching) from raw sensor measurements
without explicit programming, using reinforcement learning. However, a common
bottleneck in machine learning techniques resides in the availability of
suitable data. While many vision-based datasets have been released in the
recent years, ones involving physical interactions, of particular interest for
the robotic community, have been scarcer. In this paper, we present a public
dataset on peg-in-hole insertion tasks containing force-torque and pose
information for multiple variations of convex-shaped pegs. We demonstrate how
this dataset can be used to train a robot to insert polyhedral pegs into holes
using only 6-axis force/torque sensor measurements as inputs, as well as other
tasks involving contact such as shape recognition.
