Two-level RRT Planning for Robotic Push Manipulation

Pushing operations are encountered frequently in robotics, but have received comparatively little attention in the research comunity. In one sense, pushing is perhaps the most primitive kind of manipulation, but the relationship between applied pushes and the resulting workpiece motions are complex and hard to predict and control. Push contacts are also important to more complex tasks such as grasping.

This work presents an algorithm for planning sequences of pushes, by which a robotic arm equipped with a single rigid finger can move a manipulated object (or manipulandum) towards a desired goal pose. Pushing is perhaps the most basic kind of manipulation, however it presents difficult challenges for planning, because of the complex relationship between manipulative pushing actions and resulting manipulandum motions. The motion planning literature has well developed paradigms for solving e.g. the piano-mover's problem, where the search occurs directly in the configuration space of the manipulandum object being moved. In contrast, in pushing manipulation, a plan must be built in the action space of the robot, which is only indirectly linked to the motion space of the manipulandum through a complex interaction for which inverse models may not be known. In this work, we present a two stage approach to planning pushing operations. A global RRT path planner is used to explore the space of possible manipulandum configurations, while a local push planner makes use of predictive models of pushing interactions, to plan sequences of pushes to move the manipulandum from one RRT node to the next. The effectiveness of the algorithm is demonstrated in simulation experiments in which a robot must move a rigid body through complex 3D transformations by applying only a sequence of simple single finger pushes.

#Robotics #Pushing #ModelPredictor #Planning #RRT

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