Improved M4M: Faster and Richer Planning for Manipulation Among Movable Objects in Cluttered 3D Workspaces

We are interested in enabling robots to solve difficult pick-and-place manipulation tasks in cluttered and constrained environments. If the robot does not have collision-free access to the object-of-interest (OoI) which it intends to grasp and extract from the workspace, it must reason about which movable objects to rearrange, where to move them, and how it may do so. In recent work [1] we introduced E-M4M, a graph search-based solver for solving such Manipulation tasks Among Movable Objects (MAMO). In this paper we make several improvements to E-M4M – we introduce the use of prehensile or pick-and-place rearrangement actions in addition to pushes; we show that by running it as a depth-first search improves performance; we show how the search can be run "eagerly lazily" to only simulate actions in a physics-based simulator when necessary; finally we relax the assumption that we require perfect knowledge of the physical properties of objects (mass and coefficient of friction in particular). The improved version of E-M4M presented in this paper, I-M4M, is a faster and more versatile MAMO solver with a rich action space. We discuss the impact of the improvements we make in an extensive simulation study and show previously unachievable results on a real-world PR2 robot.