One of the most promising areas of research in human-robot collaboration is shared control. In this paradigm, a human and an intelligent autonomy “share” control of a robot by simultaneously issuing commands towards a common goal. Fusing inputs in this manner allows the human-robot team to harness the complementary advantages of both agents. Typical applications of shared control include robotic wheelchairs, surgical aids, space exploration rovers, vehicle driving assistance, and so forth. I’m passionate about this subject because of its capacity to ease control of robots, which can be challenging or result in excess workload when manually operated. This is particularly beneficial for individuals with disabilities.

But how do we develop shared control to best assist someone during a task? A popular answer to this question is to have the robot learn human “intentions”. By understanding what a person intends to accomplish in a given task, the robot can then effectively assist them; be it getting a robotic wheelchair from point ‘A’ to ‘B’, helping a surgeon guide a surgical instrument, or even steering a vehicle to avoid accidents. My prior research has explored this idea of making robots understand human intent [1].

That being said, even when a robot can infer the best strategy for assistance, it still may not behave as you would expect. This is what we term a “model misalignment”, i.e., a person’s mental model does not quite sync up with how the robot works internally.

Left: Why is my robotic wheelchair behaving weirdly?
Right: Augmented reality headset explaining why!

To help resolve this misalignment, I introduced the notion of Explainable Shared Control [2]. In this framework, an augmented reality headset was used as a way of visually exposing a robot’s inner workings. Check out the video below for a demonstration on a robotic wheelchair!

References

[1] M. Zolotas and Y. Demiris, “Disentangled sequence clustering for human intention inference”, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022.
[2] M. Zolotas and Y. Demiris, “Towards Explainable Shared Control using Augmented Reality”, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019.