Developmental Robotics: From Babies to Robots

By Angelo Cangelosi, M. Schlesinger. 2015

361 citations recorded by the source corpus.

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

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, background): “ld, and through bodily and physical experimentation, under severe constraints on energy, time, and computational resources. In the two last decades, the field of Developmental and Cognitive Robotics (Cangelosi and Schlesinger, 2015; Asada et al., 2009), in strong interaction with developmental psych”
Continual Lifelong Learning with Neural Networks: A Review (2018, background): “e-distributed through additional learning phases. Developmental learning strategies have been experimented on with embedded agents to regulate the real-time embodied interaction with the environment (Cangelosi and Schlesinger, 2015; Tani, 2016). In contrast with computational models that are fed wit”
Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization (2018, background): “tricted (Thrun and Mitchell, 1995). Importantly, there may be no distinction between training and test phases, which requires the system to concurrently learn and timely trigger behavioral responses (Cangelosi and Schlesinger, 2015; Tani, 2016). Lifelong machine learning represents a long-standing c”
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics (2020, neutral): “As human infants, the proposed approach needs to face the challenges of understanding the robot’s environment and its own capabilities [34, 35, 36, 37].”
Sensorimotor Representation Learning for an “Active Self” in Robots: A Model Survey (2020, background): “In (Cangelosi and Schlesinger, 2014), the authors presented both theoretical and experimental aspects of”
Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach (2017, background): “core abilities required for social cognition [5], [22]–[24].”
Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement (2017, background): “For instance, we can say that it was the research team after Deep Blue (Campbell et al, 2002) (with the help of a powerful computer) who actually defeated Kasparov.”
Online Continual Learning from Imbalanced Data (2020, background): “In this work, however, we focus on online CL, as it more closely resembles the way humans and animals learn (Cangelosi & Schlesinger, 2015).”
GRAIL: A Goal-Discovering Robotic Architecture for Intrinsically-Motivated Learning (2016, background): “Since biological agents are versatile and naturally-adaptive, looking at the characteristics that allow them to improve their knowledge and their competence could provide useful hints to develop intelligent artificial systems with similar capabilities [23].”
What do we learn about development from baby robots? (2015, background): “The central role of the body in psychological development is the reason why robots, and not simply abstract computer simulations, will play such a key role in future progress.(10)”