In this paper, we will argue that if we want to understand the function of the brain (or the control in the case of robots), we must understand how the brain is embedded into the physical system, and how the organism interacts with the real world. While embodiment has often been used in its trivial meaning, i.e. 'intelligence requires a body', the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. A number of case studies are presented to illustrate the concept. These involve animals and robots and are concentrated around locomotion, grasping, and visual perception. A theoretical scheme that can be used to embed the diverse case studies will be presented. Finally, we will establish a link between the low-level sensory-motor processes and cognition. We will present an embodied view on categorization, and propose the concepts of 'body schema' and 'forward models' as a natural extension of the embodied approach toward first representations.
A large body of compelling evidence has been accumulated demonstrating that embodiment - the agent's physical setup, including its shape, materials, sensors and actuators - is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental appr…
Minimal Embodiment Enables Efficient Learning of Number Concepts in Robot
Cognitive Robotics Lab, Department of Computer Science, University of Manchester（曼彻斯特大学计算机科学系认知机器人实验室）；Smart Interactive Technologies (SIT) Research Laboratory, Department of Computing, Sheffield Hallam University（谢菲尔德哈勒姆大学计算系智能交互技术（SIT）研究实验室）
The Animal-AI Environment: A Virtual Laboratory For Comparative Cognition and Artificial Intelligence Research
University of Cambridge（剑桥大学）；Universitat Politècnica de València（瓦伦西亚理工大学）；University of Hull（赫尔大学）；University of Exeter（埃克塞特大学）；Imperial College London（伦敦帝国学院）
Imperial College London（帝国理工学院）；Leverhulme Centre for the Future of Intelligence（利弗休姆未来智能中心）；Universitat Politècnica de València（瓦伦西亚理工大学）；University of Cambridge（剑桥大学）
Recent advances in artificial intelligence have been strongly driven by the use of game environments for training and evaluating agents. Games are often accessible and versatile, with well-defined state-transitions and goals allowing for intensive training and experimentation. However, agents trained in a particular environment are usually tested on the same or slightly varied distributions, and solutions do not necessarily imply any understanding. If we want AI systems that can model and understand their environment, we need environments that explicitly test for this. Inspired by the extensiv…
The body is not there to compute: Comment on "Informational embodiment: Computational role of information structure in codes and robots" by Pitti et al
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague（控制系，电气工程学院，布拉格捷克技术大学）
Emotions are intimately tied to motivation and the adaptation of behavior, and many animal species show evidence of emotions in their behavior. Therefore, emotions must be related to powerful mechanisms that aid survival, and, emotions must be evolutionary continuous phenomena. How and why did emotions evolve in nature, how do events get emotionally appraised, how do emotions relate to cognitive complexity, and, how do they impact behavior and learning? In this article I propose that all emotions are manifestations of reward processing, in particular Temporal Difference (TD) error assessment. …
Let us reflect on the state of robotics. This year marks the $101$-st anniversary of R.U.R., a play by the writer Karel Čapek, often credited with introducing the word "robot". The word used to refer to feudal forced labourers in Slavic languages. Indeed, it points to one key characteristic of robotic systems: they are mere slaves, have no rights, and execute our wills instruction by instruction, without asking anything in return. The relationship with us humans is commensalism; in biology, commensalism subsists between two symbiotic species when one species benefits from it (robots boost prod…
Animals exploit time to survive in the world. Temporal information is required for higher-level cognitive abilities such as planning, decision making, communication, and effective cooperation. Since time is an inseparable part of cognition, there is a growing interest in the artificial intelligence approach to subjective time, which has a possibility of advancing the field. The current survey study aims to provide researchers with an interdisciplinary perspective on time perception. Firstly, we introduce a brief background from the psychology and neuroscience literature, covering the character…
Center for Connected Learning and Computer-Based Modeling（互联学习与计算机建模中心）；Northwestern Institute of Complex Systems（西北大学复杂系统研究所）；Northwestern University（西北大学）
We present a method of endowing agents in an agent-based model (ABM) with sophisticated cognitive capabilities and a naturally tunable level of intelligence. Often, ABMs use random behavior or greedy algorithms for maximizing objectives (such as a predator always chasing after the closest prey). However, random behavior is too simplistic in many circumstances and greedy algorithms, as well as classic AI planning techniques, can be brittle in the context of the unpredictable and emergent situations in which agents may find themselves. Our method, called agent-centric Monte Carlo cognition (ACMC…
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics). If that hypothesis was correct, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behavior of complex systems like brains, and substantial computation might be needed to simulate human-like intelligence. This hypothesis would suggest that studying the kind of inductive biases that humans and animals exploit could help b…
To interpret deep networks, one main approach is to associate neurons with human-understandable concepts. However, existing methods often ignore the inherent relationships of different concepts (e.g., dog and cat both belong to animals), and thus lose the chance to explain neurons responsible for higher-level concepts (e.g., animal). In this paper, we study hierarchical concepts inspired by the hierarchical cognition process of human beings. To this end, we propose HIerarchical Neuron concepT explainer (HINT) to effectively build bidirectional associations between neurons and hierarchical conc…
KTH Royal Institute of Technology（瑞典皇家理工学院）；Institute for Systems and Robotics, Instituto Superior Técnico（高等技术学院系统与机器人研究所）
Time perception is the phenomenological experience of time by an individual. In this paper, we study how to replicate neural mechanisms involved in time perception, allowing robots to take a step towards temporal cognition. Our framework follows a twofold biologically inspired approach. The first step consists of estimating the passage of time from sensor measurements, since environmental stimuli influence the perception of time. Sensor data is modeled as Gaussian processes that represent the second-order statistics of the natural environment. The estimated elapsed time between two events is c…
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)
Inria Bordeaux（法国国家信息与自动化研究所波尔多分所）；LaBRI, Université de Bordeaux（波尔多大学拉布里实验室）；Université de Bordeaux（波尔多大学）；TU Berlin（柏林工业大学）；National University of Singapore（新加坡国立大学）；Microsoft（微软公司）；International Institute of Information Technology Hyderabad（海得拉巴国际信息技术研究所）；CEA（法国原子能和替代能源委员会）；CNRS（法国国家科学研究中心）
