Robotics Lab
Human–machine collaboration
Designing systems where people and machines share intent, context and authority.
Overview
The most useful robotic systems are not fully autonomous islands; they are collaborators that amplify human judgment. Getting that collaboration right is a research problem spanning control, interface design and cognition.
We study how authority should transfer between human and machine, how shared context is maintained, and how trust is calibrated over time.
What this covers
Shared intent
Machines infer and confirm what a person is trying to accomplish, reducing ambiguity in joint tasks.
Authority transfer
Control passes between human and machine smoothly and predictably as conditions change.
Context preservation
Both parties keep a common picture of state so handoffs don't lose information.
Calibrated trust
Interfaces help people trust the system exactly as much as its demonstrated reliability warrants.
Low-latency feedback
People perceive what the machine is doing fast enough to stay meaningfully in the loop.
Ergonomic interfaces
Collaboration tools are designed for sustained real-world use, not lab demos.