Deep Inverse Reinforcement Learning for Informative Path Planning(3/3)

Project Details

Description

The AI community has been paying more attention to the concept of informative path planning (IPP). The difference between path planning and IPP is that IPP is to maximize information gathering instead of avoiding obstacles. There are different applications depending on the definition of information (e.g., detection of infected plants, search for structural failure, mountain rescue and search, illegal logging, monitor of pollutions and 3D mapping). However, finding optimal solutions for these problems are NP-hard, so finding approximate solutions is a feasible way. To make a breakthrough of the IPP research status, this research proposed a deep inverse reinforcement learning approach to improve IPP performance of robots through analyzing how humans solve IPP problems in daily lives. The project will take three years. The focus of the first year is to explore the reward functions of that humans solve IPP problems via deep inverse reinforcement learning. The focus of the second year is to analyze the transfer learning of that humans solve different IPP problems. The focus of the third year is to explore human-robot cooperative IPP problems. The goal of this research is to explore three issues of IPP:(1) IPP is learnable? If it is learnable, how much data robots need? (2) How do humans transfer their knowledge for different IPP problems? (3) What’s the difference and respective strengths of humans and robots?
StatusActive
Effective start/end date1/08/2131/07/22

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 12 - Responsible Consumption and Production
  • SDG 17 - Partnerships for the Goals

Keywords

  • Informative path planning
  • deep inverse reinforcement learning
  • transfer learning
  • submodularity
  • compressed sensing

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