Human Robot Collaboration Team

Human Robot Collaboration Team


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.

Issues Involved or Addressed

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 behave collaboratively with human partners. Implement these algorithms on the Baxter robot.

Methods and Technologies

  • Bayesian Non-parametric learning
  • Learning from demonstrations
  • Formal Methods
  • Linear Temporal Logic
  • Reactive Synthesis
  • Stochastic
  • Dynamic Programming

Academic Majors of Interest

  • Electrical Engineering
  • Computer Science and Engineering
  • Aerospace and Mechanical Engineering

Preferred Interests and Preparation