Disney Research

We present a constrained Rigid Body Dynamics (RBD) that guarantees satisfaction of kinematic constraints, enabling direct simulation of complex mechanical systems with arbitrary kinematic structures.

We introduce a novel method that integrates kinematic generative models with physics based character control. Our approach begins by training a reward surrogate to predict the performance of the downstream non-differentiable control task, offering an efficient and differentiable loss function.

This paper presents an experiment with three artificial agents adopting different strategies when being interrupted by human conversational partners. The agent either ignored the interruption (the most common behavior in conversational engines to date), yielded the turn to the human conversational partner right away, or acknowledged the interruption, finished its thought and then responded to the content of the interruption. Our results show that this change in the agent's conversational behavior had a significant impact on which personality traits people assigned to the agent, as well as how much they enjoyed interacting with it. Moreover, the data also indicates that human interlocutors adapted their own conversational behavior. Our findings suggest that the interactive behavior of an artificial agent should be carefully designed to match its desired personality and the intended conversational dynamics.