Task-specific AI agents are showing remarkable performance across different domains. But modeling generalized AI agents like human intelligence will require more than current datasets or only reward-based environments that don't include experiences that an infant gathers throughout its initial stages. In this paper, we present Simulated Environment for Developmental Robotics (SEDRo). It simulates the environments for a baby agent that a human baby experiences throughout the pre-born fetus stage to post-birth 12 months. SEDRo also includes a mother character to provide social interaction with the agent. To evaluate different developmental milestones of the agent, SEDRo incorporates some experiments from developmental psychology.
Baby Sophia: A Developmental Approach to Self-Exploration through Self-Touch and Hand Regard
Robotics Institute, Athena Research Center（机器人研究所，阿提卡研究中心）；HERON – Hellenic Robotics Center of Excellence（HERON – 希腊机器人 excellence 中心）；Institute for Language and Speech Processing, Athena Research Center（语言和语音处理研究所，阿提卡研究中心）
The agent program, called Samu, is an experiment to build a disembodied DevRob (Developmental Robotics) chatter bot that can talk in a natural language like humans do. One of the main design feature is that Samu can be interacted with using only a character terminal. This is important not only for practical aspects of Turing test or Loebner prize, but also for the study of basic principles of Developmental Robotics. Our purpose is to create a rapid prototype of Q-learning with neural network approximators for Samu. We sketch out the early stages of the development process of this prototype, wh…
Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Saudi Basic Industries Corporation (SABIC)（沙特基础工业公司）；optoXense, Inc.（optoXense公司）；NNAISENSE（NNAISENSE公司）；Center of Excellence in Generative AI, King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学生成式AI卓越中心）；Dalle Molle Institute for Artificial Intelligence Research (IDSIA)（达勒·莫勒人工智能研究所）；Università della Svizzera italiana (USI)（意大利语瑞士大学）；Scuola universitaria professionale della Svizzera italiana (SUPSI)（意大利语瑞士应用科学大学）
CNRS（法国国家科学研究中心）；ISIR（智能系统与机器人研究所）；U2IS（U2IS实验室）；INRIA Flowers（法国国家信息与自动化研究所Flowers团队）；ENSTA（法国国立高等先进技术学院）；Institut Polytechnique Paris（巴黎理工学院）；University of Edinburgh（爱丁堡大学）
Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate behavior given either some demonstrations or a reward to guide its exploration with a reinforcement learning algorithm. Reinforcement learning algorithms rely on the definition of state and action spaces that define reachable behaviors. Their adaptation capability critically depends on the representations of these spaces: small and discrete spaces re…
Even with impressive advances in application-specific models, we still lack knowledge about how to build a model that can learn in a human-like way and do multiple tasks. To learn in a human-like way, we need to provide a diverse experience that is comparable to humans. In this paper, we introduce our ongoing effort to build a simulated environment for developmental robotics (SEDRo). SEDRo provides diverse human experiences ranging from those of a fetus to a 12th-month-old. A series of simulated tests based on developmental psychology will be used to evaluate the progress of a learning model. …
Despite the recent advancement in the social robotic field, important limitations restrain its progress and delay the application of robots in everyday scenarios. In the present paper, we propose to develop computational models inspired by our knowledge of human infants' social adaptive abilities. We believe this may provide solutions at an architectural level to overcome the limits of current systems. Specifically, we present the functional advantages that adaptive Theory of Mind (ToM) systems would support in robotics (i.e., mentalizing for belief understanding, proactivity and preparation, …
As the current trend of artificial intelligence is shifting towards self-supervised learning, conventional norms such as highly curated domain-specific data, application-specific learning models, extrinsic reward based learning policies etc. might not provide with the suitable ground for such developments. In this paper, we introduce SEDRo, a Simulated Environment for Developmental Robotics which allows a learning agent to have similar experiences that a human infant goes through from the fetus stage up to 12 months. A series of simulated tests based on developmental psychology will be used to…
Ritsumeikan University（立命馆大学）；Keio University（庆应义塾大学）；The University of Tokyo（东京大学）；Università Milano-Bicocca（米兰比可卡大学）；University of Essex（埃塞克斯大学）；Donders Institute for Brain, Cognition and Behaviour（唐德斯脑、认知与行为研究所）；Cajal International Neuroscience Center, Spanish National Research Council（西班牙国家研究委员会卡哈尔国际神经科学中心）；Bogazici University（博阿齐奇大学）；Queen Mary University of London（伦敦玛丽女王大学）；The University of Electro-Communications（电气通信大学）；National Autonomous University of Mexico（墨西哥国立自治大学）；Universidad Autónoma del Estado de Morelos（莫雷洛斯州自治大学）；Institute of Cognitive Sciences and Technologies, National Research Council of Italy（意大利国家研究委员会认知科学与技术研究所）
Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of world models and predictive coding. Recently, world models have attracted renewed attention as a topic of considerable interest in artificial intelligence. Cognitive systems learn…
Understanding infant development is one of the greatest scientific challenges of contemporary science. A large source of difficulty comes from the fact that the development of skills in infants results from the interactions of multiple mechanisms at multiple spatio-temporal scales. The concepts of "innate" or "acquired" are not any more adequate tools for explanations, which call for a shift from reductionist to systemic accounts. To address this challenge, building and experimenting with robots modeling the growing infant brain and body is crucial. Systemic explanations of pattern formation i…
To achieve human-like common sense about everyday life, machine learning systems must understand and reason about the goals, preferences, and actions of other agents in the environment. By the end of their first year of life, human infants intuitively achieve such common sense, and these cognitive achievements lay the foundation for humans' rich and complex understanding of the mental states of others. Can machines achieve generalizable, commonsense reasoning about other agents like human infants? The Baby Intuitions Benchmark (BIB) challenges machines to predict the plausibility of an agent's…
LIRIS, CNRS UMR 5205 Ecole Centrale de Lyon（里昂中央理工学院 LIRIS 实验室（法国国家科学研究中心 UMR 5205））
In robotics, methods and softwares usually require optimizations of hyperparameters in order to be efficient for specific tasks, for instance industrial bin-picking from homogeneous heaps of different objects. We present a developmental framework based on long-term memory and reasoning modules (Bayesian Optimisation, visual similarity and parameters bounds reduction) allowing a robot to use meta-learning mechanism increasing the efficiency of such continuous and constrained parameters optimizations. The new optimization, viewed as a learning for the robot, can take advantage of past experience…
A3RNN: Bi-directional Fusion of Bottom-up and Top-down Process for Developmental Visual Attention in Robots
Department of Intermedia Art and Science, Waseda University（媒体艺术与科学系，早稻田大学）；Research Development Group, Hitachi, Ltd.（日立株式会社研发发展组）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）
Istituto di Scienze e Tecnologie della Cognizione (ISTC)（认知科学与技术研究所（ISTC））；Consiglio Nazionale delle Ricerche (CNR)（意大利国家研究委员会（CNR））
Autonomous multiple tasks learning is a fundamental capability to develop versatile artificial agents that can act in complex environments. In real-world scenarios, tasks may be interrelated (or "hierarchical") so that a robot has to first learn to achieve some of them to set the preconditions for learning other ones. Even though different strategies have been used in robotics to tackle the acquisition of interrelated tasks, in particular within the developmental robotics framework, autonomous learning in this kind of scenarios is still an open question. Building on previous research in the fr…
Okinawa Institute of Science and Technology Graduate University（冲绳科学技术大学院大学）；Cognitive Neurorobotics Research Unit（认知神经机器人学研究单元）
Efficient exploration has presented a long-standing challenge in reinforcement learning, especially when rewards are sparse. A developmental system can overcome this difficulty by learning from both demonstrations and self-exploration. However, existing methods are not applicable to most real-world robotic controlling problems because they assume that environments follow Markov decision processes (MDP); thus, they do not extend to partially observable environments where historical observations are necessary for decision making. This paper builds on the idea of replaying demonstrations for memo…
What are the functions of curiosity? What are the mechanisms of curiosity-driven learning? We approach these questions about the living using concepts and tools from machine learning and developmental robotics. We argue that curiosity-driven learning enables organisms to make discoveries to solve complex problems with rare or deceptive rewards. By fostering exploration and discovery of a diversity of behavioural skills, and ignoring these rewards, curiosity can be efficient to bootstrap learning when there is no information, or deceptive information, about local improvement towards these probl…
Donders Institute for Brain, Cognition, and Behaviour（唐德斯大脑、认知与行为研究所）；Radboud University（拉德堡德大学）；Joint Research Centre, European Commission（欧盟委员会联合研究中心）；Ghent University（根特大学）；imec（微电子研究中心）；Instituto Superio Técnico（高等技术学院）；University of Lisbon（里斯本大学）
Understanding and modelling children's cognitive processes and their behaviour in the context of their interaction with robots and social artificial intelligence systems is a fundamental prerequisite for meaningful and effective robot interventions. However, children's development involve complex faculties such as exploration, creativity and curiosity which are challenging to model. Also, often children express themselves in a playful way which is different from a typical adult behaviour. Different children also have different needs, and it remains a challenge in the current state of the art t…
Institute for AI, Peking University（北京大学人工智能研究院）；Center on Frontiers of Computing Studies, Peking University（北京大学前沿计算研究中心）；University College London（伦敦大学学院）；Carnegie Mellon University（卡内基梅隆大学）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）
Achieving human-level dexterity is an important open problem in robotics. However, tasks of dexterous hand manipulation, even at the baby level, are challenging to solve through reinforcement learning (RL). The difficulty lies in the high degrees of freedom and the required cooperation among heterogeneous agents (e.g., joints of fingers). In this study, we propose the Bimanual Dexterous Hands Benchmark (Bi-DexHands), a simulator that involves two dexterous hands with tens of bimanual manipulation tasks and thousands of target objects. Specifically, tasks in Bi-DexHands are designed to match di…
Ritsumeikan University（立命馆大学）；The University of Electro-Communications（电气通信大学）；Okayama Prefectural University（冈山县立大学）；Waseda University（早稻田大学）；National Institute of Advanced Industrial Science and Technology（产业技术综合研究所）
Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot th…
University of Hamburg（汉堡大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；University of Potsdam（波茨坦大学）；Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）
Safe human-robot interactions require robots to be able to learn how to behave appropriately in \sout{humans' world} \rev{spaces populated by people} and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots…
This paper outlines a perspective on the future of AI, discussing directions for machines models of human-like intelligence. We explain how developmental and evolutionary theories of human cognition should further inform artificial intelligence. We emphasize the role of ecological niches in sculpting intelligent behavior, and in particular that human intelligence was fundamentally shaped to adapt to a constantly changing socio-cultural environment. We argue that a major limit of current work in AI is that it is missing this perspective, both theoretically and experimentally. Finally, we discus…
Learning mental states estimation through self-observation: a developmental synergy between intentions and beliefs representations in a deep-learning model of Theory of Mind
University of Essex（埃塞克斯大学）；Università Milano-Bicocca（米兰比可卡大学）；Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale della Ricerche（国家研究委员会认知科学与技术研究所）
Autonomous lifelong development and learning is a fundamental capability of humans, differentiating them from current deep learning systems. However, other branches of artificial intelligence have designed crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodiment. These mechanisms guide exploration and autonomous choice of goals, and integrating them with deep learning opens stimulating perspectives. Deep learning (DL) approaches made great advances in artificial intelligence, but are still far away f…
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…
University of Vermont（佛蒙特大学）；University of Wyoming（怀俄明大学）；DNextech s.r.l.（3DNextech有限责任公司）
Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. Another but not unrelated longstanding property of these systems is their brittleness to slight aberrations, as highlighted by the growing deep learning literature on adversarial examples. Here we show robustness can be achieved by evolving the geometry of soft robots, their control systems, and how their material properties develop in response to one particular interoce…
