Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions.

Abstract. This paper presents a constructive model by which a robot acquires the ability of joint attention with a human caregiver based on its embedded mechanisms of visual attention and learning with self-evaluation. The former is to look at a salient object in the robot’s view, and the latter is to learn sensorimotor co-ordination when visual attention has succeeded. Since the success of visual attention does not always correspond to the success of joint attention, the robot has incorrect learning data for joint attention as well as correct data. However, the robot is expected statistically to lose incorrect data as outliers since such data do not have any correlation in the sensorimotor co-ordination while correct data have a correlation. The robot consequently acquires the ability of joint attention by finding the correlation in the sensorimotor co-ordination even if multiple objects are placed at random positions in an environment and a human caregiver does not provide any task evaluation to the robot. The experimental results show that the proposed model makes the robot reproduce the developmental process of infants ’ joint attention. Therefore, the proposed model could be one of the models to explain how infants develop the ability of joint attention.

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We present a framework for the programming of manipulation behavior by means of an intrinsic reward function that encourages the building of deep control knowledge. We show how this framework can be used to teach new manipulation skills in a hierarchical and incremental fashion. We demonstrate the contributions of this paper on a humanoid robot through three incremental learning stages.

Abstract — We present a strategy whereby a robot acquires the capability to learn by imitation following a developmental pathway consisting on three levels: (i) sensory-motor coordination, (ii) world interaction, (iii) imitation. With these stages, the system is able to learn tasks by imitating human demonstrators. We describe results of the different developmental stages, involving perceptual and motor skills, implemented in our humanoid robot, Baltazar. At each stage, the system’s attention is drawn towards different entities: its own body and later on, objects and people. Our main contributions are the general architecture and the implementation of all the necessary modules until imitation capabilities are eventually acquired by the robot. Also several other contributions are made at each level: learning of sensory-motor maps for redundant robots, a novel method for learning how to grasp objects and a framework for learning task description from observation for program-level imitation. Finally, vision is used extensively as the sole sensing modality (sometimes in a simplified setting) avoiding the need for special data-acquisition hardware. Index Terms — Humanoid Robots, development, imitation I.

Developmental robotics is an emerging field located at the intersection of developmental psychology and robotics, that has lately attracted quite some attention. This paper gives a survey of a variety of research projects dealing with or inspired by developmental issues, and outlines possible future directions.

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This study argues how a human infant acquires the ability of joint attention through interactions with its caregiver from the viewpoint of a constructive approach. This paper presents a constructive model by which a robot acquires a sensorimotor coordination for joint attention based on visual attention and learning with self-evaluation. Since visual attention does not always correspond to joint attention, the robot may have incorrect learning situations for joint attention as well as correct ones. However, the robot is expected to statistically lose the data of the incorrect ones as outliers through the learning, and consequently acquires the appropriate sensorimotor coordination for joint attention even if the environment is not controlled nor the caregiver provides any task evaluation. The experimental results suggest that the proposed model could explain the developmental mechanism of the infant's joint attention because the learning process of the robot's joint attention can be regarded as equivalent to the developmental process of the infant's one.

This dissertation would not have been possible without the help and support of many people. Most of all, I would like to extend my gratitude to Rod Grupen for many years of inspiring work, our discussions, and his guidance. Without his support and vision, I cannot imagine that the journey would have been as enormously enjoyable and rewarding as it turned out to be. I am very excited about what we discovered during my time at UMass, but there is much more to be done. I look forward to what comes next! In addition to providing professional inspiration, Rod was a great person to work with and for—creating a warm and encouraging laboratory atmosphere, motivating us to stay in shape for his annual half-marathons, and ensuring a sufficient amount of cake at the weekly lab meetings. Thanks for all your support, Rod! I am very grateful to my thesis committee—Andy Barto, David Jensen, and Rachel Keen—for many encouraging and inspirational discussions. Their comments and feedback significantly contributed to the form of this document. I would especially

Abstract. We present a review of recent trends in cognitive robotics that deal with online learning approaches to the acquisition of knowledge, control strategies and behaviors of a cognitive robot or agent. Along this line we focus on the topics of object recognition in cognitive vision, trajectory learning and adaptive control of multi-DOF robots, task learning from demonstration, and general developmental approaches in robotics. We argue for the relevance of online learning as a key ability for future intelligent robotic systems to allow flexible and adaptive behavior within a changing and unpredictable environment. 1