When Shall I Be Empathetic? The Utility of Empathetic Parameter Estimation in Multi-Agent Interactions
This work addresses robustness in multi-agent interactions for robotics, though it is incremental by building on existing parameter estimation and planning methods.
The paper tackles the problem of non-empathetic parameter estimation in human-robot interactions, showing that empathetic estimation improves reward values in scenarios where agents' true parameters mismatch with common beliefs, as demonstrated in a two-vehicle intersection case.
Human-robot interactions (HRI) can be modeled as dynamic or differential games with incomplete information, where each agent holds private reward parameters. Due to the open challenge in finding perfect Bayesian equilibria of such games, existing studies often consider approximated solutions composed of parameter estimation and motion planning steps, in order to decouple the belief and physical dynamics. In parameter estimation, current approaches often assume that the reward parameters of the robot are known by the humans. We argue that by falsely conditioning on this assumption, the robot performs non-empathetic estimation of the humans' parameters, leading to undesirable values even in the simplest interactions. We test this argument by studying a two-vehicle uncontrolled intersection case with short reaction time. Results show that when both agents are unknowingly aggressive (or non-aggressive), empathy leads to more effective parameter estimation and higher reward values, suggesting that empathy is necessary when the true parameters of agents mismatch with their common belief. The proposed estimation and planning algorithms are therefore more robust than the existing approaches, by fully acknowledging the nature of information asymmetry in HRI. Lastly, we introduce value approximation techniques for real-time execution of the proposed algorithms.