Cognitive Developmental Robotics: A Survey

By Minoru Asada, K. Hosoda, Yasuo Kuniyoshi, Hiroshi Ishiguro, Toshio Inui, Y. Yoshikawa, M. Ogino, C. Yoshida. 2009

587 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Social Psychology, Robotics & Control, Cognitive Science & Psychology

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Citations in the corpus

Active learning of inverse models with intrinsically motivated goal exploration in robots (2013, neutral): “the SAGG-RANDOM method seems to slightly increase the space covered on the uand vaxis compared to ACTUATOR methods, as shown by the higher concentration of positions explored in the interval [ 5; 3] [[3;5] of u. However, this change does not seem very important when comparing SAGG-RANDOM to these tw”
Empathy in Virtual Agents and Robots (2017, background): “The area of Cognitive Developmental Robotics (CDR) (Asada et al. 2009) aims at providing “new understandings of how humans’ higher cognitive functions develop by means of a synthetic approach that developmentally constructs cognitive functions.””
Bootstrapping intrinsically motivated learning with human demonstration (2011, background): “n-ended repertoire of skills, developmental robots, like animal or human infants, need to be endowed with task-independent mechanisms which push them to explore new activities and new situations [1], [2]. The set of skills that could be learnt is actually inﬁnite, and can not be completely learnt wi”
Guest Editorial Active Learning and Intrinsically Motivated Exploration in Robots: Advances and Challenges (2010, background): “This challenge is at the center of the developmental robotics ﬁeld [7], [35]–[37].”
Bootstrapping Intrinsically Motivated Learning with Human Demonstrations (unknown): “In order to learn an open-ended repertoire of skills, developmental robots, like animal or human infants, need to be endowed with task-independent mechanisms which push them to explore new activities and new situations [1], [2].”
On the Impact of Robotics in Behavioral and Cognitive Sciences: From Insect Navigation to Human Cognitive Development (2010, unknown): “In the last decade of the 20th century, several researchers have begun to employ voluntarily robots as tools for direct experiment and study of animal and human behavior, as opposed to using them as models of these behaviors as those presented in previous sections.”
Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement (2017, background): “…AI evaluation is performed, after the recent progress in areas of AI that are distinct from the narrow AI approach, such as developmental robotics (Asada et al, 2009), deep learning (Arel et al, 2010), inductive programming (Gulwani et al, 2015), artificial general intelligence (Goertzel and…”
Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints (2013, background): “A central aim of developmental robotics is to study the developmental mechanisms that allow life-long and open-ended learning of new skills and new knowledge in robots and animals (Asada et al., 2009; Lungarella et al., 2003; Weng et al., 2001).”
From hardware and software to kernels and envelopes: a concept shift for robotics, developmental psychology, and brain sciences (2011, background): “…has been already explored in several works including for instance (Jordan and Jacobs, 1994; Tani and Nolfi, 1999; Kawato, 1999; Doya et al., 2002; Baldassarre, 2002; Khamassi et al., 2005) The basic circuits we just described permit to compute an internal reward r(t) = pRn(t), each time an…”
The Measure of Intelligence (2019, unknown): “developmental robotics [4], artificial general intelligence [28]).”
Active Learning and Intrinsically Motivated Exploration in Robots : Advances and Challenges (2010, background): “This challenge is at the center of the developmental robotics field [7], [35]–[37].”
Development of artificial empathy. (2015, background): “For our purposes, we will rephrase a part of CDR as affective developmental robotics (ADR) Therefore, ADR follows the approach of CDR, particularly focusing on affective development .”
The Ingredients of Real-World Robotic Reinforcement Learning (2020, background): “While this line of work has connections to developmental robotics (Lungarella et al., 2003; Asada et al., 2009) and its subfields, such as continual (Lesort et al.”
Bootstrapping bilinear models of Simple Vehicles (2015, background): “D(Y;X;U; ∆) is the set of all discrete-time dynamical systems with sampling interval ∆.”