Who is Willing to Help Robots? A User Study on Collaboration Attitude

In order to operate in human-populated environments, robots need to show reasonable behaviors and human-compatible abilities. In the so-called Symbiotic Autonomy, robots and humans help each other to overcome mutual limitations and complete their tasks. When the robot takes the initiative and asks the human for help, there is a change of perspective in the interaction, which has not yet been specifically addressed by HRI studies. In this paper, we investigate the novel scenario brought about by Symbiotic Autonomy, by addressing the factors that may influence the interaction. In particular, we introduce the “Collaboration Attitude” to evaluate how the response of users being asked by the robot for help is influenced by the context of the interaction and by what they are doing (i.e., ongoing activity). We present the results of a first study, which confirms the influence of conventional factors (i.e., proxemics) on the Collaboration Attitude, while it suggests that the context (i.e., relaxing vs. working) may not be much relevant. Then, we present a second study, carried out to better assess the influence of the activity performed by the humans in our population, when (s)he is approached by the robot, as an additional and more compelling characterization of context (i.e., standing vs. sitting). While the experimental scenario takes into account a population with distinctive characteristics (i.e., academic staff and students), the overall findings of our studies suggest that the attitude of users towards robots in the setting of Symbiotic Autonomy is influenced by factors already known to influence robot acceptance while it is not significantly affected by the context of the interaction and by the human ongoing activity

Spatial representation for planning and executing robot behaviors in complex environments

Robots are already improving our well-being and productivity in different applications such as industry, health-care and indoor service applications. However, we are still far from developing (and releasing) a fully functional robotic agent that can autonomously survive in tasks that require human-level cognitive capabilities. Robotic systems on the market, in fact, are designed to address specific applications, and can only run pre-defined behaviors to robustly repeat few tasks (e.g., assembling objects parts, vacuum cleaning). They internal representation of the world is usually constrained to the task they are performing, and does not allows for generalization to other scenarios. Unfortunately, such a paradigm only apply to a very limited set of domains, where the environment can be assumed to be static, and its dynamics can be handled before deployment. Additionally, robots configured in this way will eventually fail if their "handcrafted'' representation of the environment does not match the external world. Hence, to enable more sophisticated cognitive skills, we investigate how to design robots to properly represent the environment and behave accordingly. To this end, we formalize a representation of the environment that enhances the robot spatial knowledge to explicitly include a representation of its own actions. Spatial knowledge constitutes the core of the robot understanding of the environment, however it is not sufficient to represent what the robot is capable to do in it. To overcome such a limitation, we formalize SK4R, a spatial knowledge representation for robots which enhances spatial knowledge with a novel and "functional" point of view that explicitly models robot actions. To this end, we exploit the concept of affordances, introduced to express opportunities (actions) that objects offer to an agent. To encode affordances within SK4R, we define the "affordance semantics" of actions that is used to annotate an environment, and to represent to which extent robot actions support goal-oriented behaviors. We demonstrate the benefits of a functional representation of the environment in multiple robotic scenarios that traverse and contribute different research topics relating to: robot knowledge representations, social robotics, multi-robot systems and robot learning and planning. We show how a domain-specific representation, that explicitly encodes affordance semantics, provides the robot with a more concrete understanding of the environment and of the effects that its actions have on it. The goal of our work is to design an agent that will no longer execute an action, because of mere pre-defined routine, rather, it will execute an actions because it "knows'' that the resulting state leads one step closer to success in its task

ENRICHING COMMUNICATION BETWEEN HUMANS AND AI AGENTS

Equipping AI agents with effective, human-compatible communication capabilities is pivotal to enabling them to effectively serve and aid humans. On one hand, agents should understand humans, being able to infer intentions and extract knowledge from language utterances. On the other hand, they should also help humans understand them, conveying (un)certainties and proactively consulting humans when facing difficult situations. This dissertation presents new training and evaluation frameworks that enrich communication between humans and AI agents. These frameworks improve two capabilities of an agent: (1) the ability to learn through natural communication with humans and (2) the ability to request and interpret information from humans during task execution. Regarding the first capability, I study the possibility and challenges of training agents with noisy human ratings. Providing humans with more expressive tools for teaching agents, I propose a framework that employs descriptive language as the teaching medium. On the second capability, I introduce new benchmarks that evaluate an agent’s ability to exchange information with humans to successfully perform indoor navigation tasks. On these benchmarks, I build agents that are capable of requesting rich, contextually useful information and show that they significantly outperform those without such capability. I conclude the dissertation with discussions on how to develop more sophisticated communication capabilities for agents