A behavioural transformer for effective collaboration between a robot
  and a non-stationary human
Ruaridh Mon-Williams Theodoros Stouraitis Sethu Vijayakumar
A key challenge in human-robot collaboration is the non-stationarity created
by humans due to changes in their behaviour. This alters environmental
transitions and hinders human-robot collaboration. We propose a principled
meta-learning framework to explore how robots could better predict human
behaviour, and thereby deal with issues of non-stationarity. On the basis of
this framework, we developed Behaviour-Transform (BeTrans). BeTrans is a
conditional transformer that enables a robot agent to adapt quickly to new
human agents with non-stationary behaviours, due to its notable performance
with sequential data. We trained BeTrans on simulated human agents with
different systematic biases in collaborative settings. We used an original
customisable environment to show that BeTrans effectively collaborates with
simulated human agents and adapts faster to non-stationary simulated human
agents than SOTA techniques.