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Human-Robot Collaboration

Dr. Lin
Principal Investigator: 

Dr. Lin, Department of Electrical Engineering

Project Summary:

In Human-Robot Collaboration, robots are expected to work next to human in warehouses, daily housekeeping, and other robot assistant applications safely, intelligently and friendly. To achieve this goal, the robotic system should be equipped with capacities of understanding intentions of human partners and reasoning according to the behaviors of human partners and the state of the environment. The main idea is to combine the learning-based approach with traditional high-level task planing algorithms. The first step is to build a human model using data collected from visual perception system such as stereo cameras. Based on the learned human model, robots could infer intentions of human partners using the data collected during run-time. For example, in the handover task, the robot could track the skeleton of the human, collect data from several demonstrations and then infer the human intention. Once the robots understand the human intention, they could behave collaboratively with the human according to decisions made by high-level task planning algorithms. 

Methods and Techniques: 

Cyberphysical Systems, Formal Methods, Linear Temporal Logic, Reactive Synthesis, Satisfiability Modulo Theories (SMT), Bayesian Non-parametric learning, Learning from demonstrations. 

Student's Role:

Develop algorithms to track human movements and collect data from demonstrations. Build human models using Bayesian Non-parametric learning algorithms to ensure robots could infer the human intention correctly. Develop high-level task planning algorithms to enable the robot to behave collaboratively with human partners. Implement these algorithms on the Baxter robot. Background/interest in visual perception, control algorithms, Bayesian learning. Preferred skills in MATLAB, C/C++, Linux, and Python.