  In this paper, we explore whether a robot can learn to regrasp a diverse set
of objects to achieve various desired grasp poses. Regrasping is needed
whenever a robot's current grasp pose fails to perform desired manipulation
tasks. Endowing robots with such an ability has applications in many domains
such as manufacturing or domestic services. Yet, it is a challenging task due
to the large diversity of geometry in everyday objects and the high
dimensionality of the state and action space. In this paper, we propose a
system for robots to take partial point clouds of an object and the supporting
environment as inputs and output a sequence of pick-and-place operations to
transform an initial object grasp pose to the desired object grasp poses. The
key technique includes a neural stable placement predictor and a regrasp
graph-based solution through leveraging and changing the surrounding
environment. We introduce a new and challenging synthetic dataset for learning
and evaluating the proposed approach. We demonstrate the effectiveness of our
proposed system with both simulator and real-world experiments. More videos and
visualization examples are available on our project webpage.
