PlaceNet: Obstacle Aware Mobile Manipulator Base Placement through Deep Learning

Abstract

In this work, we present PlaceNet: a deep learning framework for mobile manipulator base placement which provides solutions to the shortcomings common in the state-of-the-art. Our method addresses the lack of obstacle awareness of reachability methods and the limited generalization of learning methods. Using only the raw pointcloud and task pose data as input, PlaceNet learns the concepts of reachability and obstacle occlusions in an environment-independent manner, enabling its use in situations outside its training experiences. Tests comparing PlaceNet to inverse reachability and heuristic methods demonstrated state-of-the-art performance in both the In-Distribution and Out-Of-Distribution test sets, achieving as high as 98% success rate for problems with many solutions, and an 82% success rate overall. PlaceNet can be trained on grounded pointcloud data from any source without the need for dynamic simulation, marking it as an accessible alternative to similar frameworks which require expensive, high-performance GPUs for running simultaneous simulation and training or which depend on labor intensive data collection. PlaceNet is lightweight during deployment and can easily run with low latency on affordable hardware, including laptop GPUs and the NVIDIA Jetson line for embedded deployment.