Robust and Adaptive Door Operation with a Mobile Robot

Abstract

The ability to deal with articulated objects is very important for robots assisting humans. In this work, a framework to robustly and adaptively operate common doors, using an autonomous mobile manipulator, is proposed. To push forward the state-of-the-art in robustness and speed performance, we devise a novel algorithm that fuses a convolutional neural network with efficient point cloud processing. This advancement enables real-time grasping pose estimation for multiple handles from RGB-D images, providing a speed up improvement for assistive human-centered applications. In addition, we propose a versatile Bayesian framework that endows the robot with the ability to infer the door kinematic model from observations of its motion and learn from previous experiences or human demonstrations. Combining these algorithms with a Task Space Region motion planner, we achieve an efficient door operation regardless of the kinematic model. We validate our framework with real-world experiments using the Toyota Human Support Robot.

15 pages, 15 figures

References in corpus (2)

• A Probabilistic Framework for Learning Kinematic Models of Articulated Objects
• Model-Based Generalization Under Parameter Uncertainty Using Path Integral Control

Cited by in corpus (6)

• Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation
• Versatile Multi-Contact Planning and Control for Legged Loco-Manipulation
• N${}^2$M${}^2$: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments
• A Versatile Door Opening System with Mobile Manipulator through Adaptive Position-Force Control and Reinforcement Learning
• A Helping (Human) Hand in Kinematic Structure Estimation
• Deep Measurement Updates for Bayes Filters