In this paper we explore if human mental models of objects, even when flawed, can be integrated with a collaborative robot's decision making framework to allow it to make smarter choices under partial observability for different object-related tasks such as assembly and troubleshooting. We demonstrate how (1) these informative causal models can be extracted from humans through crowdsourcing, (2) object assembly and troubleshooting can be formulated as Partially Observable Markov Decision Processes (POMDPs) and (3) our extracted causal models can be incorporated into those models in the form of approximate priors. Finally, (4) we use systematic experimentation in simulation to demonstrate the success of this approach, with 2 X average improvement in reward observed for object assembly tasks, and 1.4 X average improvement in reward observed for troubleshooting tasks.

Causal reasoning is a key factor that allows human beings to effortlessly draw parallels between two seemingly different tasks. Given access to a limited number of human-generated causal models, if robots could be taught to perform a similar "transfer", then they could potentially generalize to different domains using limited human intervention. The goal is to effectively generate human-like causal models for an unknown task by learning from a small dataset of human-generated causal models on other related tasks. In this paper we propose two different methods to leverage causal models obtained from humans to generalize to a new task. We ran a user study to obtain causal models on how different light-producing objects function. We then explore one-to-one transfer, where we directly use a causal model generated by a participant to infer the causal model of another object. We also explore many-to-one transfer, where we leverage causal models from different objects and users to infer the causal model of an unknown object. Automated transfer of causal models from humans to unrelated domains has the potential to replicate human-like reasoning in an unknown scenario in the absence of an expert. That chain of reasoning represented through causal models can then be used by autonomous agents to guide several downstream tasks such as object assembly, trouble shooting, and repair.

In this paper we present two design guidelines, causal order and continuity, to be used as rules of thumb for designing intuitive interactions based on principles of causal reasoning. We propose that designing interactions to behave like real-world systems of cause and effect makes them more intuitive. Using these basic principles avoids the limitations inherent to specific metaphors. In three experiments, participants solved puzzles using variations of a novel graphical interface. Participants using interfaces that were consistent with the causal guidelines consistently solved the puzzle faster than participants using inconsistent interfaces. We also discuss common interactions already consistent with the causal guidelines as well as areas where the guidelines are likely to apply successfully. The causal order guidelines provide specific utility while also demonstrating how principles of causal psychology can be applied to help interface designers better convey the functionality of their interfaces.