Intrinsic Motivation Systems for Autonomous Mental Development

Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development.The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology. Key words: Active learning, autonomy, behavior, complexity, curiosity, development, developmental trajectory, epigenetic robotics, intrinsic motivation, learning, reinforcement learning, values

Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks

Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signalcognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given way points.Comment: accepted in Neural Network

Constraining the Size Growth of the Task Space with Socially Guided Intrinsic Motivation using Demonstrations

This paper presents an algorithm for learning a highly redundant inverse model in continuous and non-preset environments. Our Socially Guided Intrinsic Motivation by Demonstrations (SGIM-D) algorithm combines the advantages of both social learning and intrinsic motivation, to specialise in a wide range of skills, while lessening its dependence on the teacher. SGIM-D is evaluated on a fishing skill learning experiment.Comment: JCAI Workshop on Agents Learning Interactively from Human Teachers (ALIHT), Barcelona : Spain (2011

Robot pain: a speculative review of its functions

Given the scarce bibliography dealing explicitly with robot pain, this chapter has enriched its review with related research works about robot behaviours and capacities in which pain could play a role. It is shown that all such roles ¿ranging from punishment to intrinsic motivation and planning knowledge¿ can be formulated within the unified framework of reinforcement learning.Peer ReviewedPostprint (author's final draft

A Mixed Methods Approach to Using Collaborative and Proactive Solutions with Students with Emotional and Behavioral Disorders while Applying the Self-Determination Theory

Students with emotional and behavioral disorders (EBD) that lack social skills and problem solving have stronger features of depression, higher drop-out rates and struggle with peer relations. With such an emphasis on academics in high school, students still need strategies taught to compensate for skill deficits in problem solving, relationship- building and choice making. This pragmatic mixed methods study used pre-and post-assessment data from the self-determination theory and examined the implementation of collaborative and proactive solutions through focus groups of teachers and mental health practitioners that work with students with EBD in a special education high school. While quantitative data was not significant, focus group findings specified changes in restructuring the current schedule, trust, time, buy-in and predominantly leadership implications. Recommendations for future studies include additional data sets to be included in the study; choosing elementary or middle school student populations; and applying a leadership frameworks at the onset of implementing collaborative and proactive solutions. Limitations of this study consisted of a small sample size and typical limitations of a focus group. This study adds to current gaps in high-school students with EBD, self-determination, and collaborative and proactive solutions

Autonomy and autonomy disturbances in self-development and psychopathology: research on motivation, attachment, and clinical process

Self-determination theory (SDT) maintains that the adequate support and satisfaction of individuals' psychological needs for autonomy, competence, and relatedness promotes the gradual unfolding of individuals' integrative tendencies, as manifested through intrinsic motivation, internalization, identity development, and integrative emotion regulation. At the same time, the thwarting of these same psychological needs and the resultant need frustration is presumed to evoke or amplify a variety of psychopathologies, many of which involve autonomy disturbances. We begin by defining what autonomy involves and how socializing agents, particularly parents, can provide a nurturing (i.e., need-supportive) environment, and we review research within the SDT literature that has shed light on various integrative tendencies and how caregivers facilitate them. In the second part of this chapter, we detail how many forms of psychopathology involve autonomy disturbances and are associated with a history of psychological need thwarting. We especially focus on internally controlling regulation in internalizing disorders; impairments of internalization in conduct disorders and antisocial behavior; and fragmented self-functioning in borderline and dissociative disorders. The role of autonomy support as an ameliorative factor in treatment settings is then discussed among other translational issues. Finally we highlight some implications of recognizing the important role of basic psychological needs for both growth-related and pathology-related processes