Human-Centered Robot Learning

Improving accessibility of robots for novice users

Robots are foreseen to be used in a variety of home contexts and interacting with novice users, such as assisting the dependent elderly in daily housework (i.e., caring and unpacking groceries, fetching, and pouring a glass of water, etc). Enabling robots to adapt to their environment through learning context specific tasks would be necessary for them to be used adequately by non-programming users.

In education, a good instructor maintains a mental model of the learner’s state (what has been learned and what needs clarification). This helps the teacher to appropriately structure the upcoming learning tasks with timely feedback and guidance. The learner can help the instructor by expressing their internal state via communicative acts that reveal their understanding, confusion, and attention. Robot’s learning parameters, however, can be overwhelming for a novice user and may increase the human workload (by increasing inaccurate feedbacks, and hence decreasing the robot’s learning). The challenge lies on training humans to be efficient trainers and enabling them to plan, assess and manage the robot’s learning.

Another noticeable issue is the disengagement of humans during the training task. Teaching procedural skills to a robot learner can be time consuming and repetitive. This often results in increased noise in human feedback making their input less reliable. Some researchers have imagined several strategies for the robot to cope with this, such as detecting inconsistencies and asking for additional feedback. This project proposes to investigate how collaborative and competitive games could enable better quality feedback when robots are learning from humans. Inspired by instructional design, we will study how building teaching tools for human teachers can effectively improve the robot’s learning. We will also aim to engage the trainer longer by identifying and integrating a gamification element in the training.

This project is a 3-year endeavour funded by the Australian Research Council starting mid-2021. Under this umbrella, we are looking at hiring two PhD students in the Faculty of Engineering at the School of Computer Science. This project will make an extensive use of the National Facility for Human Robot Interaction Research.

Caption photos easily. On the left, a road goes through a tunnel. Middle, leaves artistically fall in a hipster photoshoot. Right, in another hipster photoshoot, a lumberjack grasps a handful of pine needles.
This image can also have a caption. It's like magic.

(Phaijit et al., 2023).

(Phaijit et al., 2022).

<a class="citation" href="#phaijit2022taxonomy">(Phaijit et al., 2022)</a>.
You can also have artistically styled 2/3 + 1/3 images, like these.



  1. User Interface Interventions for Improving Robot Learning from Demonstration
    Ornnalin Phaijit , Claude Sammut , and Wafa Johal
    In Proceedings of the 11th International Conference on Human-Agent Interaction , 2023


  1. Let’s Compete! The Influence of Human-Agent Competition and Collaboration on Agent Learning and Human Perception
    Ornnalin Phaijit , Claude Sammut , and Wafa Johal
    In Proceedings of the 10th International Conference on Human-Agent Interaction , 2022
  2. A Taxonomy of Functional Augmented Reality for Human-Robot Interaction
    Ornnalin Phaijit , Mohammad Obaid , Claude Sammut , and 1 more author
    In 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI) , 2022