Fast Perception, Planning, and Execution for a Robotic Butler: Wheeled Humanoid M-Hubo


As the aging population grows at a rapid rate, there is an ever growing need for service robot platforms that can provide daily assistance at practical speed with reliable performance. In order to assist with daily tasks such as fetching a beverage, a service robot must be able to perceive its environment and generate corresponding motion trajectories. This becomes a challenging and computationally complex problem when the environment is unknown and thus the path planner must sample numerous trajectories that often are sub-optimal, extending the execution time. To address this issue, we propose a unique strategy of integrating a 3D object detection pipeline with a kinematically optimal manipulation planner to significantly increase speed performance at runtime. In addition, we develop a new robotic butler system for a wheeled humanoid that is capable of fetching requested objects at 24% of the speed a human needs to fulfill the same task. The proposed system was evaluated and demonstrated in a real-world environment setup as well as in public exhibition.

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)
Philipp Benz
Philipp Benz
Ph.D. Candidate @ Robotics and Computer Vision Lab, KAIST

My research interest is in Deep Learning with a focus on robustness and security.