Self-Organization, Embodiment, and Biologically Inspired Robotics

By Rolf Pfeifer, Max Lungarella, Fumiya Iida. 2007

1,312 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Control and Systems Engineering, Mechanical Engineering, Robotics & Control

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, background): “, 2013), and goal exploration (Baranes and Oudeyer, 2013), social learning and natural interaction (Chernova and Thomaz, 2014; Vollmer et al., 2014), maturation (Oudeyer et al., 2013) and embodiment (Pfeifer et al., 2007). These mechanisms crucially complement processes of incremental online model b”
Active Learning and Intrinsically Motivated Exploration in Robots : Advances and Challenges (2010, background): “…characterized by various constraints such as motor primitives or maturation of the perceptual system, within a body that has peculiar morphological properties and performs morphological computation [55], and within a physical and social environment which also strongly influences the organism.”
Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment (2008, background): “The robotics experiment is important when one considers the idea of the embodied mind by Varela [12], who explained that cognitive functions of neural systems emerge not only in the brain, but also in dynamic interactions between the physical body and the environment (see also a recent review [13]).”
Guest Editorial Active Learning and Intrinsically Motivated Exploration in Robots: Advances and Challenges (2010, background): “…characterized by various constraints such as motor primitives or maturation of the perceptual system, within a body that has peculiar morphological properties and performs morphological computation [55], and within a physical and social environment which also strongly inﬂuences the organism.”
Survey on Coverage Path Planning with Unmanned Aerial Vehicles (2019, background): “Biological systems are able to adapt, evolve and survive in natural environments characterized by quick modiﬁcations, elevated uncertainty, and restricted information [69].”
The evolution, challenges, and future of knowledge representation in product design systems (2013, background): “A deeper understanding of properties of biological organisms such as adaptivity, robustness, versatility, and agility [87] also guide the construction of new representational and functional models [88] during the design process and improve the quantity and quality of the generated design ideas in th”
Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model (2011, background): “Exploiting knowledge of primate neuroscience can help in the design of cognitive systems enabling robots to adapt to varying task conditions and to have satisfying, if not optimal, performance, in a variety of different situations (Pfeifer et al., 2007; Arbib et al., 2008; Meyer and Guillot, 2008).”
Soft robotics: Technologies and systems pushing the boundaries of robot abilities (2016, background): “The study of living organisms can shed light on principles that can be fruitfully adopted to develop additional robot abilities or to facilitate more efficient accomplishment of tasks (8), because living organisms exploit soft tissues and compliant structures to move effectively in complex natural e”
Information processing via physical soft body (2015, methodology): “By generating passive body dynamics resulting from the interaction between the water and the soft silicone material 29,30 , we will show that the sensory time series reflected in the body dynamics can be used to emulate the desired nonlinear dynamical systems, which are often targeted with a…”
Physical reservoir computing—an introductory perspective (2020, background): “[157–159] For example, a seminal platform called a “passive dynamic walker” can walk naturally like a human without having an external controller [160].”
Tactile Sensing—From Humans to Humanoids (2010, background): “For centuries, biological systems 78 have inspired engineers [37] and are now inspiring roboticists 79 as well [38]–[40].”
Flexible Sensors—From Materials to Applications (2019, background): “Soft and deformable robots safely interact with humans [614,615], and, at the same time, flexible and stretchable sensors perfectly fit with the demanding requirements of biologically inspired soft robots in terms of conformability to the bending structures in which large strains are involved [90,61”