  In robots, nonprehensile manipulation operations such as pushing are a useful
way of moving large, heavy or unwieldy objects, moving multiple objects at
once, or reducing uncertainty in the location or pose of objects. In this
study, we propose a reactive and adaptive method for robotic pushing that uses
rich feedback from a high-resolution optical tactile sensor to control push
movements instead of relying on analytical or data-driven models of push
interactions. Specifically, we use goal-driven tactile exploration to actively
search for stable pushing configurations that cause the object to maintain its
pose relative to the pusher while incrementally moving the pusher and object
towards the target. We evaluate our method by pushing objects across planar and
curved surfaces. For planar surfaces, we show that the method is accurate and
robust to variations in initial contact position/angle, object shape and start
position; for curved surfaces, the performance is degraded slightly. An
immediate consequence of our work is that it shows that explicit models of push
interactions might be sufficient but are not necessary for this type of task.
It also raises the interesting question of which aspects of the system should
be modelled to achieve the best performance and generalization across a wide
range of scenarios. Finally, it highlights the importance of testing on
non-planar surfaces and in other more complex environments when developing new
methods for robotic pushing.
