New Directions in Robotic Environmental Monitoring via Machine Learning

点击翻译按钮获取中文摘要

What is preventing robotic environmental monitoring of Canadian landscapes from becoming automated? The answer is twofold. First, there are challenges in hardware robustness, such as operation in adverse environmental conditions. Second, and most crucial, robots are oblivious to the type of data that environmental scientists really need. They do not know where to direct their attention. My overarching goal is to address the latter set of challenges with little supervision from and interaction with humans.******This research will develop perception, inference, and control algorithms to address challenging robotics problems in informed visual exploration. The main application domain is the automation of environmental monitoring by robots operating outdoors, building on my past work in this area. The main users of the technology are environmental scientists. The core algorithms developed will be directly applicable to a vast array of human-robot interaction scenarios, and will span three key directions:******(D1) Data-Efficient Learning of Robot Visual Attention with User Specifications: we will develop methods that efficiently learn what visual content in an image matters to the user. Predictive models of visual attention will be learned from a small number of labeled and a large number of unlabeled images. They will enable robots to perform informed visual exploration tailored to the user's preferences. We will also develop connections with reward learning and inverse reinforcement learning over images. This is a key enabling factor for human-robot interaction, which despite years of progress in low-dimensional settings, still lacks robust practical methods in high dimensions. ******(D2) Online Visual Exploration Strategies for Robots via Uncertainty in Visual Attention: we will estimate the model (epistemic) uncertainty of visual attention models, going beyond mere learning of a single probabilistic model. This will enable online, information gathering behaviors from the robot. It will also enable active selection of only a few images that the user needs to label in (D1). Most importantly, however, it will pave the way for the development of uncertainty-aware visual exploration strategies in 3D.******(D3) Collaborative Human Multi-Robot Visual Exploration for Environmental Monitoring: we will design methods to enable a team of robots and a human to visually explore an unknown environment in tandem. This will allow robots of different capabilities to explore collaboratively, without constantly having to see where the human is, by making informed short-term and long-term predictions about his/her location, intent, and preferences. After exploring on their own, robots will need to relocate their human collaborator to redirect his/her attention towards any interesting observations.******This research is unique in jointly addressing these challenges with a human in the loop. The techniques proposed herein will be validated on ground and aerial robots operating outdoors.