Active learning agents typically employ a query selection algorithm which solely considers the agent's learning objectives. However, this may be insufficient in more realistic human domains. This work uses imitation learning to enable an agent in a constrained environment to concurrently reason about both its internal learning goals and environmental constraints externally imposed, all within its objective function. Experiments are conducted on a concept learning task to test generalization of the proposed algorithm to different environmental conditions and analyze how time and resource constraints impact efficacy of solving the learning problem. Our findings show the environmentally-aware learning agent is able to statistically outperform all other active learners explored under most of the constrained conditions. A key implication is adaptation for active learning agents to more realistic human environments, where constraints are often externally imposed on the learner.

The state features available to a robot define the variables on which the learning computation depends. However, little prior work considers feature selection in the context of deploying a general-purpose robot able to learn new tasks. In this work, we explore human-driven feature selection in which a robotic agent can identify useful features with the aid of a human user, by extracting information from users about which features are most informative for discriminating between classes of objects needed for a given task (e.g. sorting groceries). The research questions examine (a) whether a domain expert is able to identify a subset of informative task features, (b) whether human selected features will enable the agent to classify unseen examples as accurately as using computational feature selection, and (c) if the interaction strategy used to elicit the information from the user impacts the quality of resulting feature selection. Toward that end, we conducted a user study with 30 participants on campus, given a multi-class classification task and one of five different approaches for conveying information about informative features to a robot learner. Our findings show that when features are semantically interpretable, human feature selection is effective in LfD scenarios because it is able to outperform computational methods when there is limited training data, yet still remains on-par with computational methods as the training sample size increases.

Active learning literature has explored the selection of optimal queries by a learning agent with respect to given criteria, but prior work in classification has focused only on obtaining labels for queried samples. In contrast, proficient learners, like humans, integrate multiple forms of information during learning. This work seeks to enable an active learner to reason about multiple query types concurrently, aimed at soliciting both instance and feature information from the teacher, and to autonomously arbitrate between queries of different types. We contribute the design of rule-based and decision-theoretic arbitration strategies and evaluate all against baselines of more traditional passive and active learning. Our findings show that all arbitration strategies lead to more efficient learning, compared to the baselines. Moreover, given a dynamically changing environment and constrained questioning budget (typical in human settings), the decision-theoretic strategy statistically outperforms all other methods since it reasons about both what query to make and when to make a query, in order to most effectively utilize its questioning budget.

Personal robots are intended to assist people in their daily lives; therefore, it is important for them to efficiently learn how to accomplish tasks delineated by their human partners, in the environment in which they are situated. Toward that end however, it is arguably even more important to enable these robots to characterize their own uncertainty as they attempt to generalize task-specific knowledge learned and equip them with a domain-independent framework for asking the appropriate questions of their human partners to resolve the uncertainty. The work presented in this extended abstract is a high level overview of current and future work that we are developing in this domain, using learning from demonstration and active robot learning.

When a robot is deployed to a new setting, it must reason about how to accomplish the goals of domain-appropriate tasks within the environment it is situated. We investigate the problem of enabling robots to interactively learn how to perform known tasks in new environments. Each task is composed of a sequence of parameterized actions, which we assume are given to the robot in the form of a task recipe. In order to learn how to ground the task in a new environment, our learner builds classifiers to model each of the parameters (i.e. all unique objects and semantic locations) associated with the task. In evaluation for two tasks across three different environments, our results show that these groundings are both (1) capable of being learned efficiently from demonstrations, and (2) necessary to learn for each new environment.

Our goal is to develop robots that naturally engage people in social exchanges. In this paper, we focus on the problem of recognizing that a person is responsive to a robot's request for interaction. Inspired by human cognition, our approach is to treat this as a contingency detection problem. We present a simple discriminative Support Vector Machine (SVM) classifier to compare against previous generative methods introduced in prior work by Lee et al. [1]. We evaluate these methods in two ways. First, by training three separate SVMs with multi-modal sensory input on a set of batch data collected in a controlled setting, where we obtain an average F1score of 0.82. Second, in an open-ended experiment setting with seven participants, we show that our model is able to perform contingency detection in real-time and generalize to new people with a best F1score of 0.72.