Before revision:  Strong evaluation methods should explore the diverse scenarios of interaction between humans and robots . We propose quality diversity (QD) algorithms as a method for simultaneously exploring both environments and human actions to discover diverse failure scenarios .
After revision:  Strong evaluation methods should explore the diverse scenarios of interaction between humans and robots in simulation can improve understanding of the system and avoid potentially costly failures in real-world settings. We formulate this as a quality diversity (QD) algorithms as a method for simultaneously exploring both environments and human actions to discover diverse failure scenarios .