Goal-directed robot manipulation through axiomatic scene estimation

Performing robust goal-directed manipulation tasks remains a crucial challenge for autonomous robots. In an ideal case, shared autonomous control of manipulators would allow human users to specify their intent as a goal state and have the robot reason over the actions and motions to achieve this goal. However, realizing this goal remains elusive due to the problem of perceiving the robot’s environment. We address and describe the problem of axiomatic scene estimation for robot manipulation in cluttered scenes which is the estimation of a tree-structured scene graph describing the configuration of objects observed from robot sensing. We propose generative approaches to scene inference (as the axiomatic particle filter, and the axiomatic scene estimation by Markov chain Monte Carlo based sampler) of the robot’s environment as a scene graph. The result from AxScEs estimation are axioms amenable to goal-directed manipulation through symbolic inference for task planning and collision-free motion planning and execution. We demonstrate the results for goal-directed manipulation of multi-object scenes by a PR2 robot.