Human Perceptions of a Curious Robot that Performs Off-Task Actions (original) (raw)
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2017
Service robots are becoming a widespread tool for assisting humans in scientific, industrial and even domestic settings. Yet, our understanding of how to motivate and sustain interactions between human users and robots remains limited. In this work, we conducted a study to investigate how surprising robot behaviour evokes curiosity and influences trust and engagement in the context of participants interacting with Recyclo, a service robot for providing recycling recommendations. In a Wizard-of-Oz experiment, 36 participants were asked to interact with Recyclo to recognize and sort a variety of objects, and were given object recognition responses that were either unsurprising or surprising. Results show that surprise gave rise to information seeking behavior indicative of curiosity, while having a positive influence on engagement and negative influence on trust.
On Curiosity in Intelligent Robotic Systems
Curiosity is a notion that is increasingly receiving special attention, particularly in the context of the emerging fields of developmental robotics. In the first part of the paper we give a brief critical overview of the research in motivational systems in intelligent robotics. The overall impression is that the prevailing understanding of curiosity is rather one dimensional and reductionist in spirit. We argue that this is a result of rather simple agent's representations of the environment that are usually adopted. In the second part of the paper we put forward some arguments towards modeling of curiosity in context of other cognitive phenomena like feeling of understanding, analogy-making and expectations, integrated in a more general cognitive architecture.
Lecture Notes in Computer Science, 2019
In this paper we present a fully autonomous and intrinsically motivated robot usable for HRI experiments. We argue that an intrinsically motivated approach based on the Predictive Information formalism, like the one presented here, could provide us with a pathway towards autonomous robot behaviour generation, that is capable of producing behaviour interesting enough for sustaining the interaction with humans and without the need for a human operator in the loop. We present a possible reactive baseline behaviour for comparison for future research. Participants perceive the baseline and the adaptive, intrinsically motivated behaviour differently. In our exploratory study we see evidence that participants perceive an intrinsically motivated robot as less intelligent than the reactive baseline behaviour. We argue that is mostly due to the high adaptation rate chosen and the design of the environment. However, we also see that the adaptive robot is perceived as more warm, a factor which carries more weight in interpersonal interaction than competence.
The playground experiment: Task-independent development of a curious robot
Proceedings of the AAAI …, 2005
This paper presents the mechanism of Intelligent Adaptive Curiosity. This is an intrinsic motivation system which pushes the robot towards situations in which it maximizes its learning progress. It makes the robot focus on situations which are neither too predictable nor too unpredictable. This mechanism is a source of autonomous mental development for the robot: the complexity of its activities autonomously increases and a developmental sequence appears without being manually constructed. We test this motivation system on a real robot which evolves on a baby playmat with objects that it can learn to manipulate. We show that it first spends time in situations which are easy to learn, then shifts progressively its attention to situations of increasing difficulty, avoiding situations in which nothing can be learnt.
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To create a robot with a mind of its own, we extended a formalized version of a model that explains human-robot interaction with mechanisms for goaldirected behavior. By running simulation experiments, we found that robots could perceive affordances in other agents to achieve their goals and suppress rational decisions in favor of affective decisions, given baseline involvement or distancing tendencies. Limitations are that models of situation selection are still wanted and empirical validation is needed. However, our good-bad balancing approach explains more complex phenomena in (affective) decision making than hedonic-bias models do.
Early Experiments using Motivations to Regulate Human-Robot Interaction
We present the results of some early experiments with an autonomous robot to demonstrate its ability to regulate the intensity of social inter- action with a human. The mode of social inter- action is that of a caretaker-infant pair where a human acts as the caretaker for the robot. With respect to this type of socially situated learning, the ability to regulate the intensity of the interac- tion is important for promoting and maintaing a suitable learning environment where the learner (infant or robot) is neither overwhelmed nor un- der stimulated. The implementation and early demonstrations of this skill by our robot is the topic of this paper.
Intrinsically Motivated Learning in Natural and Artificial Systems, 2012
In this chapter the design and fabrication of a new mechatronic platform (called "Mechatronic Board") for behavioral analysis of children, non-human primates, and robots are presented and discussed. The platform is the result of a multidisciplinary design approach which merges indications coming from neuroscientists, psychologists, primatologists, roboticists, and bioengineers, with the main goal of studying learning mechanisms driven by intrinsic motivations and curiosity. This chapter firstly introduces the main requirements of the platform, coming from the different needs of the experiments involving the different types of participants. Then, it provides a detailed analysis of the main features of the Mechatronic Board, focusing on its key aspects which allow the study of intrinsically motivated learning in children and nonhuman primates. Finally, it shows some preliminary results on curiositydriven learning coming from pilot experiments involving children, capuchin monkeys, and a computational model of the behavior of these organisms tested with a humanoid robot (the iCub robot). These experiments investigate the capacity of children, capuchin monkeys, and a computational model implemented on the iCub robot to learn actionoutcome contingencies on the basis of intrinsic motivations.
A curiosity model for artificial agents
Proceedings of the 14th European Conference on Artificial Life ECAL 2017, 2017
Curiosity is an inherent characteristic of the animal instinct, which stimulates the need to obtain further knowledge and leads to the exploration of the surrounding environment. In this document we present a computational curiosity model, which aims at simulating that kind of behavior on artificial agents. This model is influenced by the two main curiosity theories defended by psychologists-Curiosity Drive Theory and Optimal Arousal Model. By merging both theories, as well as aspects from other sources, we concluded that curiosity can be defined in terms of the agent's personality, its level of arousal, and the interest of the object of curiosity. The interest factor is defined in terms of the importance of the object of curiosity to the agent's goals, its novelty, and surprise. To assess the performance of the model in practice, we designed a scenario consisting of virtual agents exploring a tile-based world, where objects may exist. The performance of the model in this scenario was evaluated in incremental steps, each one introducing a new component to the model. Furthermore, in addition to empirical evaluation, the model was also subjected to evaluation by human observers. The results obtained from both sources show that our model is able to simulate curiosity on virtual agents and that each of the identified factors has its role in the simulation.
In this chapter the design and fabrication of a new mechatronic platform (called "Mechatronic Board") for behavioral analysis of children, non-human primates, and robots are presented and discussed. The platform is the result of a multidisciplinary design approach which merges indications coming from neuroscientists, psychologists, primatologists, roboticists, and bioengineers, with the main goal of studying learning mechanisms driven by intrinsic motivations and curiosity. This chapter firstly introduces the main requirements of the platform, coming from the different needs of the experiments involving the different types of participants. Then, it provides a detailed analysis of the main features of the Mechatronic Board, focusing on its key aspects which allow the study of intrinsically motivated learning in children and nonhuman primates. Finally, it shows some preliminary results on curiositydriven learning coming from pilot experiments involving children, capuchin monkeys, and a computational model of the behavior of these organisms tested with a humanoid robot (the iCub robot). These experiments investigate the capacity of children, capuchin monkeys, and a computational model implemented on the iCub robot to learn actionoutcome contingencies on the basis of intrinsic motivations.
The curious robot-structuring interactive robot learning
… , 2009. ICRA'09. …, 2009
If robots are to succeed in novel tasks, they must be able to learn from humans. To improve such humanrobot interaction, a system is presented that provides dialog structure and engages the human in an exploratory teaching scenario. Thereby, we specifically target untrained users, who are supported by mixed-initiative interaction using verbal and non-verbal modalities. We present the principles of dialog structuring based on an object learning and manipulation scenario. System development is following an interactive evaluation approach and we will present both an extensible, eventbased interaction architecture to realize mixed-initiative and evaluation results based on a video-study of the system. We show that users benefit from the provided dialog structure to result in predictable and successful human-robot interaction.