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.

翻译：人机协作中的一个关键挑战是由人类行为变化导致的非平稳性。这种非平稳性会改变环境转移过程，阻碍人机协作。我们提出一个原理性的元学习框架，用于探索机器人如何更好地预测人类行为，从而应对非平稳性问题。基于该框架，我们开发了行为变换器（BeTrans）。BeTrans是一种条件变换器，凭借其在序列数据上的卓越性能，使机器人代理能够快速适应具有非平稳行为的新人类代理。我们在协作环境中使用具有不同系统性偏差的模拟人类代理对BeTrans进行训练。通过使用原创的可定制化环境，我们证明BeTrans能够与模拟人类代理有效协作，并且相比当前最优（SOTA）技术，能更快适应非平稳的模拟人类代理。