A Survey of Brain Inspired Technologies for Engineering

Cognitive engineering is a multi-disciplinary field and hence it is difficult to find a review article consolidating the leading developments in the field. The in-credible pace at which technology is advancing pushes the boundaries of what is achievable in cognitive engineering. There are also differing approaches to cognitive engineering brought about from the multi-disciplinary nature of the field and the vastness of possible applications. Thus research communities require more frequent reviews to keep up to date with the latest trends. In this paper we shall dis-cuss some of the approaches to cognitive engineering holistically to clarify the reasoning behind the different approaches and to highlight their strengths and weaknesses. We shall then show how developments from seemingly disjointed views could be integrated to achieve the same goal of creating cognitive machines. By reviewing the major contributions in the different fields and showing the potential for a combined approach, this work intends to assist the research community in devising more unified methods and techniques for developing cognitive machines

A Neural Framework for Organization and Flexible Utilization of Episodic Memory in Cumulatively Learning Baby Humanoids

Cumulatively developing robots offer a unique opportunity to reenact the constant interplay between neural mechanisms related to learning, memory, prospection, and abstraction from the perspective of an integrated system that acts, learns, remembers, reasons, and makes mistakes. Situated within such interplay lie some of the computationally elusive and fundamental aspects of cognitive behavior: the ability to recall and flexibly exploit diverse experiences of one’s past in the context of the present to realize goals, simulate the future, and keep learning further. This article is an adventurous exploration in this direction using a simple engaging scenario of how the humanoid iCub learns to construct the tallest possible stack given an arbitrary set of objects to play with. The learning takes place cumulatively, with the robot interacting with different objects (some previously experienced, some novel) in an open-ended fashion. Since the solution itself depends on what objects are available in the “now,” multiple episodes of past experiences have to be remembered and creatively integrated in the context of the present to be successful. Starting from zero, where the robot knows nothing, we explore the computational basis of organization episodic memory in a cumulatively learning humanoid and address (1) how relevant past experiences can be reconstructed based on the present context, (2) how multiple stored episodic memories compete to survive in the neural space and not be forgotten, (3) how remembered past experiences can be combined with explorative actions to learn something new, and (4) how multiple remembered experiences can be recombined to generate novel behaviors (without exploration). Through the resulting behaviors of the robot as it builds, breaks, learns, and remembers, we emphasize that mechanisms of episodic memory are fundamental design features necessary to enable the survival of autonomous robots in a real world where neither everything can be known nor can everything be experienced

Task-adaptable, Pervasive Perception for Robots Performing Everyday Manipulation

Intelligent robotic agents that help us in our day-to-day chores have been an aspiration of robotics researchers for decades. More than fifty years since the creation of the first intelligent mobile robotic agent, robots are still struggling to perform seemingly simple tasks, such as setting or cleaning a table. One of the reasons for this is that the unstructured environments these robots are expected to work in impose demanding requirements on a robota s perception system. Depending on the manipulation task the robot is required to execute, different parts of the environment need to be examined, the objects in it found and functional parts of these identified. This is a challenging task, since the visual appearance of the objects and the variety of scenes they are found in are large. This thesis proposes to treat robotic visual perception for everyday manipulation tasks as an open question-asnswering problem. To this end RoboSherlock, a framework for creating task-adaptable, pervasive perception systems is presented. Using the framework, robot perception is addressed from a systema s perspective and contributions to the state-of-the-art are proposed that introduce several enhancements which scale robot perception toward the needs of human-level manipulation. The contributions of the thesis center around task-adaptability and pervasiveness of perception systems. A perception task-language and a language interpreter that generates task-relevant perception plans is proposed. The task-language and task-interpreter leverage the power of knowledge representation and knowledge-based reasoning in order to enhance the question-answering capabilities of the system. Pervasiveness, a seamless integration of past, present and future percepts, is achieved through three main contributions: a novel way for recording, replaying and inspecting perceptual episodic memories, a new perception component that enables pervasive operation and maintains an object belief state and a novel prospection component that enables robots to relive their past experiences and anticipate possible future scenarios. The contributions are validated through several real world robotic experiments that demonstrate how the proposed system enhances robot perception

From focused thought to reveries: A memory system for a conscious robot

© 2018 Balkenius, Tjøstheim, Johansson and Gärdenfors. We introduce a memory model for robots that can account for many aspects of an inner world, ranging from object permanence, episodic memory, and planning to imagination and reveries. It is modeled after neurophysiological data and includes parts of the cerebral cortex together with models of arousal systems that are relevant for consciousness. The three central components are an identification network, a localization network, and a working memory network. Attention serves as the interface between the inner and the external world. It directs the flow of information from sensory organs to memory, as well as controlling top-down influences on perception. It also compares external sensations to internal top-down expectations. The model is tested in a number of computer simulations that illustrate how it can operate as a component in various cognitive tasks including perception, the A-not-B test, delayed matching to sample, episodic recall, and vicarious trial and error

EEG theta and Mu oscillations during perception of human and robot actions.

The perception of others' actions supports important skills such as communication, intention understanding, and empathy. Are mechanisms of action processing in the human brain specifically tuned to process biological agents? Humanoid robots can perform recognizable actions, but can look and move differently from humans, and as such, can be used in experiments to address such questions. Here, we recorded EEG as participants viewed actions performed by three agents. In the Human condition, the agent had biological appearance and motion. The other two conditions featured a state-of-the-art robot in two different appearances: Android, which had biological appearance but mechanical motion, and Robot, which had mechanical appearance and motion. We explored whether sensorimotor mu (8-13 Hz) and frontal theta (4-8 Hz) activity exhibited selectivity for biological entities, in particular for whether the visual appearance and/or the motion of the observed agent was biological. Sensorimotor mu suppression has been linked to the motor simulation aspect of action processing (and the human mirror neuron system, MNS), and frontal theta to semantic and memory-related aspects. For all three agents, action observation induced significant attenuation in the power of mu oscillations, with no difference between agents. Thus, mu suppression, considered an index of MNS activity, does not appear to be selective for biological agents. Observation of the Robot resulted in greater frontal theta activity compared to the Android and the Human, whereas the latter two did not differ from each other. Frontal theta thus appears to be sensitive to visual appearance, suggesting agents that are not sufficiently biological in appearance may result in greater memory processing demands for the observer. Studies combining robotics and neuroscience such as this one can allow us to explore neural basis of action processing on the one hand, and inform the design of social robots on the other

DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users

A Survey of Embodied AI: From Simulators to Research Tasks

There has been an emerging paradigm shift from the era of "internet AI" to "embodied AI", where AI algorithms and agents no longer learn from datasets of images, videos or text curated primarily from the internet. Instead, they learn through interactions with their environments from an egocentric perception similar to humans. Consequently, there has been substantial growth in the demand for embodied AI simulators to support various embodied AI research tasks. This growing interest in embodied AI is beneficial to the greater pursuit of Artificial General Intelligence (AGI), but there has not been a contemporary and comprehensive survey of this field. This paper aims to provide an encyclopedic survey for the field of embodied AI, from its simulators to its research. By evaluating nine current embodied AI simulators with our proposed seven features, this paper aims to understand the simulators in their provision for use in embodied AI research and their limitations. Lastly, this paper surveys the three main research tasks in embodied AI -- visual exploration, visual navigation and embodied question answering (QA), covering the state-of-the-art approaches, evaluation metrics and datasets. Finally, with the new insights revealed through surveying the field, the paper will provide suggestions for simulator-for-task selections and recommendations for the future directions of the field.Comment: Under Review for IEEE TETC

의미론적 환경 이해 기반 인간 로봇 협업

학위논문(박사)--서울대학교 대학원 :공과대학 전기·정보공학부,2020. 2. 이범희.Human-robot cooperation is unavoidable in various applications ranging from manufacturing to field robotics owing to the advantages of adaptability and high flexibility. Especially, complex task planning in large, unconstructed, and uncertain environments can employ the complementary capabilities of human and diverse robots. For a team to be effectives, knowledge regarding team goals and current situation needs to be effectively shared as they affect decision making. In this respect, semantic scene understanding in natural language is one of the most fundamental components for information sharing between humans and heterogeneous robots, as robots can perceive the surrounding environment in a form that both humans and other robots can understand. Moreover, natural-language-based scene understanding can reduce network congestion and improve the reliability of acquired data. Especially, in field robotics, transmission of raw sensor data increases network bandwidth and decreases quality of service. We can resolve this problem by transmitting information in the form of natural language that has encoded semantic representations of environments. In this dissertation, I introduce a human and heterogeneous robot cooperation scheme based on semantic scene understanding. I generate sentences and scene graphs, which is a natural language grounded graph over the detected objects and their relationships, with the graph map generated using a robot mapping algorithm. Subsequently, a framework that can utilize the results for cooperative mission planning of humans and robots is proposed. Experiments were performed to verify the effectiveness of the proposed methods. This dissertation comprises two parts: graph-based scene understanding and scene understanding based on the cooperation between human and heterogeneous robots. For the former, I introduce a novel natural language processing method using a semantic graph map. Although semantic graph maps have been widely applied to study the perceptual aspects of the environment, such maps do not find extensive application in natural language processing tasks. Several studies have been conducted on the understanding of workspace images in the field of computer vision; in these studies, the sentences were automatically generated, and therefore, multiple scenes have not yet been utilized for sentence generation. A graph-based convolutional neural network, which comprises spectral graph convolution and graph coarsening, and a recurrent neural network are employed to generate sentences attention over graphs. The proposed method outperforms the conventional methods on a publicly available dataset for single scenes and can be utilized for sequential scenes. Recently, deep learning has demonstrated impressive developments in scene understanding using natural language. However, it has not been extensively applied to high-level processes such as causal reasoning, analogical reasoning, or planning. The symbolic approach that calculates the sequence of appropriate actions by combining the available skills of agents outperforms in reasoning and planning; however, it does not entirely consider semantic knowledge acquisition for human-robot information sharing. An architecture that combines deep learning techniques and symbolic planner for human and heterogeneous robots to achieve a shared goal based on semantic scene understanding is proposed for scene understanding based on human-robot cooperation. In this study, graph-based perception is used for scene understanding. A planning domain definition language (PDDL) planner and JENA-TDB are utilized for mission planning and data acquisition storage, respectively. The effectiveness of the proposed method is verified in two situations: a mission failure, in which the dynamic environment changes, and object detection in a large and unseen environment.인간과 이종 로봇 간의 협업은 높은 유연성과 적응력을 보일 수 있다는 점에서 제조업에서 필드 로보틱스까지 다양한 분야에서 필연적이다. 특히, 서로 다른 능력을 지닌 로봇들과 인간으로 구성된 하나의 팀은 넓고 정형화되지 않은 공간에서 서로의 능력을 보완하며 복잡한 임무 수행을 가능하게 한다는 점에서 큰 장점을 갖는다. 효율적인 한 팀이 되기 위해서는, 팀의 공통된 목표 및 각 팀원의 현재 상황에 관한 정보를 실시간으로 공유할 수 있어야 하며 함께 의사 결정을 할 수 있어야 한다. 이러한 관점에서, 자연어를 통한 의미론적 환경 이해는 인간과 서로 다른 로봇들이 모두 이해할 수 있는 형태로 환경을 인지한다는 점에서 가장 필수적인 요소이다. 또한, 우리는 자연어 기반 환경 이해를 통해 네트워크 혼잡을 피함으로써 획득한 정보의 신뢰성을 높일 수 있다. 특히, 대량의 센서 데이터 전송에 의해 네트워크 대역폭이 증가하고 통신 QoS (Quality of Service) 신뢰도가 감소하는 문제가 빈번히 발생하는 필드 로보틱스 영역에서는 의미론적 환경 정보인 자연어를 전송함으로써 통신 대역폭을 감소시키고 통신 QoS 신뢰도를 증가시킬 수 있다. 본 학위 논문에서는 환경의 의미론적 이해 기반 인간 로봇 협동 방법에 대해 소개한다. 먼저, 로봇의 지도 작성 알고리즘을 통해 획득한 그래프 지도를 이용하여 자연어 문장과 검출한 객체 및 각 객체 간의 관계를 자연어 단어로 표현하는 그래프를 생성한다. 그리고 자연어 처리 결과를 이용하여 인간과 다양한 로봇들이 함께 협업하여 임무를 수행할 수 있도록 하는 프레임워크를 제안한다. 본 학위 논문은 크게 그래프를 이용한 의미론적 환경 이해와 의미론적 환경 이해를 통한 인간과 이종 로봇 간의 협업 방법으로 구성된다. 먼저, 그래프를 이용한 의미론적 환경 이해 부분에서는 의미론적 그래프 지도를 이용한 새로운 자연어 처리 방법에 대해 소개한다. 의미론적 그래프 지도 작성 방법은 로봇의 환경 인지 측면에서 많이 연구되었지만 이를 이용한 자연어 처리 방법은 거의 연구되지 않았다. 반면 컴퓨터 비전 분야에서는 이미지를 이용한 환경 이해 연구가 많이 이루어졌지만, 연속적인 장면들은 다루는데는 한계점이 있다. 따라서 우리는 그래프 스펙트럼 이론에 기반한 그래프 컨볼루션과 그래프 축소 레이어로 구성된 그래프 컨볼루션 신경망 및 순환 신경망을 이용하여 그래프를 설명하는 문장을 생성한다. 제안한 방법은 기존의 방법들보다 한 장면에 대해 향상된 성능을 보였으며 연속된 장면들에 대해서도 성공적으로 자연어 문장을 생성한다. 최근 딥러닝은 자연어 기반 환경 인지에 있어 급속도로 큰 발전을 이루었다. 하지만 인과 추론, 유추적 추론, 임무 계획과 같은 높은 수준의 프로세스에는 적용이 힘들다. 반면 임무를 수행하는 데 있어 각 에이전트의 능력에 맞게 행위들의 순서를 계산해주는 상징적 접근법(symbolic approach)은 추론과 임무 계획에 있어 뛰어난 성능을 보이지만 인간과 로봇들 사이의 의미론적 정보 공유 방법에 대해서는 거의 다루지 않는다. 따라서, 인간과 이종 로봇 간의 협업 방법 부분에서는 딥러닝 기법들과 상징적 플래너(symbolic planner)를 연결하는 프레임워크를 제안하여 의미론적 이해를 통한 인간 및 이종 로봇 간의 협업을 가능하게 한다. 우리는 의미론적 주변 환경 이해를 위해 이전 부분에서 제안한 그래프 기반 자연어 문장 생성을 수행한다. PDDL 플래너와 JENA-TDB는 각각 임무 계획 및 정보 획득 저장소로 사용한다. 제안한 방법의 효용성은 시뮬레이션을 통해 두 가지 상황에 대해서 검증한다. 하나는 동적 환경에서 임무 실패 상황이며 다른 하나는 넓은 공간에서 객체를 찾는 상황이다.1 Introduction 1 1.1 Background and Motivation 1 1.2 Literature Review 5 1.2.1 Natural Language-Based Human-Robot Cooperation 5 1.2.2 Artificial Intelligence Planning 5 1.3 The Problem Statement 10 1.4 Contributions 11 1.5 Dissertation Outline 12 2 Natural Language-Based Scene Graph Generation 14 2.1 Introduction 14 2.2 Related Work 16 2.3 Scene Graph Generation 18 2.3.1 Graph Construction 19 2.3.2 Graph Inference 19 2.4 Experiments 22 2.5 Summary 25 3 Language Description with 3D Semantic Graph 26 3.1 Introduction 26 3.2 Related Work 26 3.3 Natural Language Description 29 3.3.1 Preprocess 29 3.3.2 Graph Feature Extraction 33 3.3.3 Natural Language Description with Graph Features 34 3.4 Experiments 35 3.5 Summary 42 4 Natural Question with Semantic Graph 43 4.1 Introduction 43 4.2 Related Work 45 4.3 Natural Question Generation 47 4.3.1 Preprocess 49 4.3.2 Graph Feature Extraction 50 4.3.3 Natural Question with Graph Features 51 4.4 Experiments 52 4.5 Summary 58 5 PDDL Planning with Natural Language 59 5.1 Introduction 59 5.2 Related Work 60 5.3 PDDL Planning with Incomplete World Knowledge 61 5.3.1 Natural Language Process for PDDL Planning 63 5.3.2 PDDL Planning System 64 5.4 Experiments 65 5.5 Summary 69 6 PDDL Planning with Natural Language-Based Scene Understanding 70 6.1 Introduction 70 6.2 Related Work 74 6.3 A Framework for Heterogeneous Multi-Agent Cooperation 77 6.3.1 Natural Language-Based Cognition 78 6.3.2 Knowledge Engine 80 6.3.3 PDDL Planning Agent 81 6.4 Experiments 82 6.4.1 Experiment Setting 82 6.4.2 Scenario 84 6.4.3 Results 87 6.5 Summary 91 7 Conclusion 92Docto