Intrinsic Motivation and Episodic Memories for Robot Exploration of High-Dimensional Sensory Spaces
The BioRobotics Institute, Scuola Superiore Sant'Anna（圣安娜高等学校生物机器人研究所）；Scuola Superiore Sant'Anna（圣安娜高等学校）；Sony Computer Science Laboratories（索尼计算机科学实验室）
Guido Schillaci, Antonio Pico Villalpando, Verena Vanessa Hafner, Peter Hanappe, David Colliaux, Timothée Wintz
备注：
This manuscript has been submitted for consideration for publication in the Adaptive Behaviour Sage Journal, edited by Tom Froese
This work presents an architecture that generates curiosity-driven goal-directed exploration behaviours for an image sensor of a microfarming robot. A combination of deep neural networks for offline unsupervised learning of low-dimensional features from images, and of online learning of shallow neural networks representing the inverse and forward kinematics of the system have been used. The artificial curiosity system assigns interest values to a set of pre-defined goals, and drives the exploration towards those that are expected to maximise the learning progress. We propose the integration of an episodic memory in intrinsic motivation systems to face catastrophic forgetting issues, typically experienced when performing online updates of artificial neural networks. Our results show that adopting an episodic memory system not only prevents the computational models from quickly forgetting knowledge that has been previously acquired, but also provides new avenues for modulating the balance between plasticity and stability of the models.
Building spatial world models from sparse transitional episodic memories
Department of Computer Science, McGill University（麦吉尔大学计算机科学系）；Department of Physiology, McGill University（麦吉尔大学生理学系）；Mila, Université de Montréal（蒙特利尔大学Mila）
UAE University（阿联酋大学）；New York Institute of Technology（纽约理工学院）；Aristotle University（亚里士多德大学）；University of Paderborn（帕德博恩大学）
Our project aims at supporting the creation of sustainable and meaningful longer-term human-robot relationships through the creation of embodied robots with face recognition and natural language dialogue capabilities, which exploit and publish social information available on the web (Facebook). Our main underlying experimental hypothesis is that such relationships can be significantly enhanced if the human and the robot are gradually creating a pool of shared episodic memories that they can co-refer to (shared memories), and if they are both embedded in a social web of other humans and robots …
Recent research developing neural network architectures with external memory have often used the benchmark bAbI question and answering dataset which provides a challenging number of tasks requiring reasoning. Here we employed a classic associative inference task from the memory-based reasoning neuroscience literature in order to more carefully probe the reasoning capacity of existing memory-augmented architectures. This task is thought to capture the essence of reasoning -- the appreciation of distant relationships among elements distributed across multiple facts or memories. Surprisingly, we …
High-fidelity social learning via shared episodic memories enhances collaborative foraging through mnemonic convergence
Donders Institute for Brain, Cognition and Behaviour（唐德斯脑、认知与行为研究所）；Radboud University（拉德堡德大学）；Alicante Institute of Neuroscience（阿利坎特神经科学研究所）；Universidad Miguel Hernandez de Elche（埃尔切米格尔·埃尔南德斯大学）
Flexible Prefrontal Control over Hippocampal Episodic Memory for Goal-Directed Generalization
Department of Psychology, University of California, Davis, California, United States of America（心理学系，加州大学戴维斯分校）；Center for Neuroscience, University of California, Davis, California, United States of America（神经科学中心，加州大学戴维斯分校）；Astera Institute（Astera研究所）；Department of Computer Science, University of California, Davis, California, United States of America（计算机科学系，加州大学戴维斯分校）
Siemens AG（西门子公司）；Ludwig Maximilian University of Munich（慕尼黑大学）
We discuss memory models which are based on tensor decompositions using latent representations of entities and events. We show how episodic memory and semantic memory can be realized and discuss how new memory traces can be generated from sensory input: Existing memories are the basis for perception and new memories are generated via perception. We relate our mathematical approach to the hippocampal memory indexing theory. We describe the first detailed mathematical models for the complete processing pipeline from sensory input and its semantic decoding, i.e., perception, to the formation of e…
The problem of extracting important and meaningful parts of a sensory data stream, without prior training, is studied for symbolic sequences, by using textual narrative as a test case. This is part of a larger study concerning the extraction of concepts from spacetime processes, and their knowledge representations within hybrid symbolic-learning `Artificial Intelligence'. Most approaches to text analysis make extensive use of the evolved human sense of language and semantics. In this work, streams are parsed without knowledge of semantics, using only measurable patterns (size and time) within …
A neural network that maintains and retrieves memories based on context
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Washington University in St. Louis（圣路易斯华盛顿大学）；City University of Hong Kong（香港城市大学）；University of Chicago（芝加哥大学）
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…
IDSIA（达勒莫尔人工智能研究所）；Università della Svizzera italiana (USI)（瑞士意大利语区大学）
Slow Feature Analysis (SFA) extracts features representing the underlying causes of changes within a temporally coherent high-dimensional raw sensory input signal. Our novel incremental version of SFA (IncSFA) combines incremental Principal Components Analysis and Minor Components Analysis. Unlike standard batch-based SFA, IncSFA adapts along with non-stationary environments, is amenable to episodic training, is not corrupted by outliers, and is covariance-free. These properties make IncSFA a generally useful unsupervised preprocessor for autonomous learning agents and robots. In IncSFA, the C…
Universität Hamburg（汉堡大学）；Okinawa Institute of Science and Technology（冲绳科学技术大学院大学）
Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability to learn from continuous streams of information is referred to as lifelong learning and represents a long-standing challenge for neural network models due to catastrophic forgetting. Computational models of lifelong learning typically alleviate catastrophic forgetting in experimental scenarios with given datasets of static images and limited complexity, thereby differing significantly from the conditions artificial a…
In the future we can expect that artificial intelligent agents, once deployed, will be required to learn continually from their experience during their operational lifetime. Such agents will also need to communicate with humans and other agents regarding the content of their experience, in the context of passing along their learnings, for the purpose of explaining their actions in specific circumstances or simply to relate more naturally to humans concerning experiences the agent acquires that are not necessarily related to their assigned tasks. We argue that to support these goals, an agent w…
Intelligent Autonomous Systems, Technische Universität Darmstadt（达姆施塔特工业大学智能自主系统研究所）；Institute for Robotics and Cognitive Systems, Universität zu Lübeck（吕贝克大学机器人与认知系统研究所）；Robot Learning Group, Max-Planck Institute for Intelligent Systems（马克斯-普朗克智能系统研究所机器人学习组）
A key feature of intelligent behaviour is the ability to learn abstract strategies that scale and transfer to unfamiliar problems. An abstract strategy solves every sample from a problem class, no matter its representation or complexity -- like algorithms in computer science. Neural networks are powerful models for processing sensory data, discovering hidden patterns, and learning complex functions, but they struggle to learn such iterative, sequential or hierarchical algorithmic strategies. Extending neural networks with external memories has increased their capacities in learning such strate…
