Erin Botti

Assistive in-home robots have the potential to enable older adults to age in place by offloading mentally or physically demanding tasks to a robot. However, one challenge for in-home robots is that each individual will have differing needs, preferences, and home environments, which can all change over time. Learning from Demonstration (LfD) is one solution to enable non-expert users to communicate their differing and changing preferences to a robot, but LfD has not been evaluated with a population of older adults. In a human-subjects experiment where participants teach a robot via LfD, we characterize disparities between older and younger adult participants in terms of robot performance, usability, and participant perceptions. We find that older adults are significantly more critical of the robot’s performance and found the LfD process less usable than younger adults. Based on participant performance and feedback, we present design guidelines that will enable roboticists to increase LfD accessibility across demographics.

As the world’s population is aging and there are growing shortages of caregivers, research into assistive robots is increasingly important. Due to differing needs and preferences, which may change over time, end-users will need to be able to communicate their preferences to a robot. Learning from Demonstration (LfD) is one method that enables non-expert users to program robots. While a powerful tool, prior research in LfD has made assumptions that break down in real-world scenarios. In this work, we investigate how to learn from suboptimal and heterogeneous demonstrators, how users react to failure with LfD, and the feasibility of LfD with a target population of older adults.

Endowing robots with the ability to learn novel tasks via demonstrations will increase the accessibility of robots for non-expert, non-roboticists. However, research has shown that humans can be poor teachers, making it difficult for robots to effectively learn from humans. If the robot could instruct humans how to provide better demonstrations, then humans might be able to effectively teach a broader range of novel, out-of-distribution tasks. In this work, we introduce Reciprocal MIND MELD, a framework in which the robot learns the way in which a demonstrator is suboptimal and utilizes this information to provide feedback to the demonstrator to improve upon their demonstrations. We additionally develop an Embedding Predictor Network which learns to predict the demonstrator’s suboptimality online without the need for optimal labels. In a series of human-subject experiments in a driving simulator domain, we demonstrate that robotic feedback can effectively improve human demonstrations in two dimensions of suboptimality (p < .001) and that robotic feedback translates into better learning outcomes for a robotic agent on novel tasks (p = .045).

Learning from demonstration (LfD) techniques seek to enable users without computer programming experience to teach robots novel tasks. There are generally two types of LfD: human- and robot-centric. While human-centric learning is intuitive, human centric learning suffers from performance degradation due to covariate shift. Robot-centric approaches, such as Dataset Aggregation (DAgger), address covariate shift but can struggle to learn from suboptimal human teachers. To create a more human-aware version of robot-centric LfD, we present Mutual Information-driven Meta-learning from Demonstration (MIND MELD). MIND MELD meta-learns a mapping from suboptimal and heterogeneous human feedback to optimal labels, thereby improving the learning signal for robot-centric LfD. The key to our approach is learning an informative personalized em-bedding using mutual information maximization via variational inference. The embedding then informs a mapping from human provided labels to optimal labels. We evaluate our framework in a human-subjects experiment, demonstrating that our approach improves corrective labels provided by human demonstrators. Our framework outperforms baselines in terms of ability to reach the goal (p<.001), average distance from the goal (p=.006), and various subjective ratings (p=.008).

Learning from demonstration (LfD) techniques seek to enable users without computer programming experience to teach robots novel tasks. There are generally two types of LfD: human- and robot-centric. While human-centric learning is intuitive, human centric learning suffers from performance degradation due to covariate shift. Robot-centric approaches, such as Dataset Aggregation (DAgger), address covariate shift but can struggle to learn from suboptimal human teachers. To create a more human-aware version of robot-centric LfD, we present Mutual Information-driven Meta-learning from Demonstration (MIND MELD). MIND MELD meta-learns a mapping from suboptimal and heterogeneous human feedback to optimal labels, thereby improving the learning signal for robot-centric LfD. The key to our approach is learning an informative personalized em-bedding using mutual information maximization via variational inference. The embedding then informs a mapping from human provided labels to optimal labels. We evaluate our framework in a human-subjects experiment, demonstrating that our approach improves corrective labels provided by human demonstrators. Our framework outperforms baselines in terms of ability to reach the goal (p<.001), average distance from the goal (p=.006), and various subjective ratings (p=.008).

Learning from Demonstration (LfD) algorithms seek to enable end-users to teach robots new skills through human demonstration of a task. Previous studies have analyzed how robot failure affects human trust, but not in the context of the human teaching the robot. In this paper, we investigate how human teachers react to robot failure in an LfD setting. We conduct a study in which participants teach a robot how to complete three tasks, using one of three instruction methods, while the robot is pre-programmed to either succeed or fail at the task. We find that when the robot fails, people trust the robot less (p < .001) and themselves less (p=.004) and they believe that others will trust them less (p < .001). Human teachers also have a lower impression of the robot and themselves (p < .001) and found the task more difficult when the robot fails (p < .001$). Motion capture was found to be a less difficult instruction method than teleoperation (p=.016), while kinesthetic teaching gave the teachers the lowest impression of themselves compared to teleoperation (p=.017) and motion capture (p < .001). Importantly, a mediation analysis showed that people’s trust in themselves is heavily mediated by what they think that others – including the robot – think of them (p < .001). These results provide valuable insights to improving the human-robot relationship for LfD.

With an aging population and a growing shortage of caregivers, the need for in-home robots is increasing. However, it is intractable for robots to have all functionalities pre-programmed prior to deployment. Instead, it is more realistic for robots to engage in supplemental, on-site learning about the user’s needs and preferences. Such learning may occur in the presence of or involve the user. We investigate the impacts on end-users of in situ robot learning through a series of human-subjects experiments. We examine how different learning methods influence both in-person and remote participants’ perceptions of the robot. While we find that the degree of user involvement in the robot’s learning method impacts perceived anthropomorphism (p=.001), we find that it is the participants’ perceived success of the robot that impacts the participants’ trust in (p<.001) and perceived usability of the robot (p<.001) rather than the robot’s learning method. Therefore, when presenting robot learning, the performance of the learning method appears more important than the degree of user involvement in the learning. Furthermore, we find that the physical presence of the robot impacts perceived safety (p<.001), trust (p<.001), and usability (p<.014). Thus, for tabletop manipulation tasks, researchers should consider the impact of physical presence on experiment participants.