Recent advances in robot learning have enabled robots to become increasingly better at mastering a predefined set of tasks. On the other hand, as humans, we have the ability to learn a growing set of tasks over our lifetime. Continual robot learning is an emerging research direction with the goal of endowing robots with this ability. In order to learn new tasks over time, the robot first needs to infer the task at hand. Task inference, however, has received little attention in the multi-task learning literature. In this paper, we propose a novel approach to continual learning of robotic control tasks. Our approach performs unsupervised learning of behavior embeddings by incrementally self-organizing demonstrated behaviors. Task inference is made by finding the nearest behavior embedding to a demonstrated behavior, which is used together with the environment state as input to a multi-task policy trained with reinforcement learning to optimize performance over tasks. Unlike previous approaches, our approach makes no assumptions about task distribution and requires no task exploration to infer tasks. We evaluate our approach in experiments with concurrently and sequentially presented tasks and show that it outperforms other multi-task learning approaches in terms of generalization performance and convergence speed, particularly in the continual learning setting.
Jagiellonian University（雅盖隆大学）；DeepMind；Polish Academy of Sciences（波兰科学院）；University of Oxford（牛津大学）
Continual learning (CL) -- the ability to continuously learn, building on previously acquired knowledge -- is a natural requirement for long-lived autonomous reinforcement learning (RL) agents. While building such agents, one needs to balance opposing desiderata, such as constraints on capacity and compute, the ability to not catastrophically forget, and to exhibit positive transfer on new tasks. Understanding the right trade-off is conceptually and computationally challenging, which we argue has led the community to overly focus on catastrophic forgetting. In response to these issues, we advo…
Evidence of an Emergent "Self" in Continual Robot Learning
Creative Machines Lab, Department of Mechanical Engineering, Columbia University（创意机器实验室，机械工程系，哥伦比亚大学）；Creative Machines Lab, Department of Computer Science, Columbia University（创意机器实验室，计算机科学系，哥伦比亚大学）
Tsinghua University（清华大学）；Beijing Innovation Center for Future Chip（北京未来芯片创新中心）
Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations. However, they need multi-task demonstrations to be provided at once for acquiring diverse skills, which is difficult in real world. In this work we study how to realize continual imitation learning ability that empowers robots to continually learn new tasks one by one, thus reducing the burden of multi-task IL and accelerating the process of new task learning at the same time. We propose a novel trajectory generation model that employs both a generative adversarial network and…
SPECI: Skill Prompts based Hierarchical Continual Imitation Learning for Robot Manipulation
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）
A Continual Learning Framework for Adaptive Control of Modular Soft Robots
The BioRobotics Institute, Scuola Superiore Sant’Anna（生物机器人研究所，圣安娜高等学校）；Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna（机器人与人工智能卓越系，圣安娜高等学校）；Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）；ETIS Lab UMR8051 CY Cergy Paris University - ENSEA - CNRS（ETIS实验室UMR8051，CY赛尔吉 - 巴黎大学 - ENSEA - 法国国家科学研究中心）
In order to detect and correct physical exercises, a Grow-When-Required Network (GWR) with recurrent connections, episodic memory and a novel subnode mechanism is developed in order to learn spatiotemporal relationships of body movements and poses. Once an exercise is performed, the information of pose and movement per frame is stored in the GWR. For every frame, the current pose and motion pair is compared against a predicted output of the GWR, allowing for feedback not only on the pose but also on the velocity of the motion. In a practical scenario, a physical exercise is performed by an exp…
Coverage path planning is a well-studied problem in robotics in which a robot must plan a path that passes through every point in a given area repeatedly, usually with a uniform frequency. To address the scenario in which some points need to be visited more frequently than others, this problem has been extended to non-uniform coverage planning. This paper considers the variant of non-uniform coverage in which the robot does not know the distribution of relevant events beforehand and must nevertheless learn to maximize the rate of detecting events of interest. This continual area sweeping probl…
We focus on the problem of teaching a robot to solve tasks presented sequentially, i.e., in a continual learning scenario. The robot should be able to solve all tasks it has encountered, without forgetting past tasks. We provide preliminary work on applying Reinforcement Learning to such setting, on 2D navigation tasks for a 3 wheel omni-directional robot. Our approach takes advantage of state representation learning and policy distillation. Policies are trained using learned features as input, rather than raw observations, allowing better sample efficiency. Policy distillation is used to comb…
Nanyang Technological University（南洋理工大学）；University of Geneva（日内瓦大学）
Hiring robots for the workplaces is a challenging task as robots have to cater to customer demands, follow organizational protocols and behave with social etiquette. In this study, we propose to have a humanoid social robot, Nadine, as a customer service agent in an open social work environment. The objective of this study is to analyze the effects of humanoid robots on customers at work environment, and see if it can handle social scenarios. We propose to evaluate these objectives through two modes, namely, survey questionnaire and customer feedback. We also propose a novel approach to analyz…
Mila（米拉研究所）；McGill University（麦吉尔大学）；Université de Montréal（蒙特利尔大学）；IBM Research（IBM研究院）；DeepMind（深度思维）
In this article, we aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We begin by discussing our perspective on why RL is a natural fit for studying continual learning. We then provide a taxonomy of different continual RL formulations by mathematically characterizing two key properties of non-stationarity, namely, the scope and driver non-stationarity. This offers a unified view of various formulations. Next, we review and present a taxonomy of continual RL approaches. We go on to d…
Brain-Inspired Hierarchical Modularity for General Continual Learning
Tsinghua University（清华大学）；IDG/McGovern Institute for Brain Research, Tsinghua University（清华大学IDG/麦戈文脑科学研究院）；Tsinghua-Peking Center for Life Sciences（清华-北大生命科学联合中心）
Never-Ending Behavior-Cloning Agent for Robotic Manipulation
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences（机器人国家重点实验室，沈阳自动化研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Automation Science and Engineering, South China University of Technology（自动化科学与工程学院，华南理工大学）；Mohamed bin Zayed University of Artificial Intelligence（马斯达尔大学人工智能学院）
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Sun Yat-sen University（中山大学）；Lehigh University（里海大学）；College of Computing and Data Science, NTU（南洋理工大学计算与数据科学学院）
ENSTA Paris（巴黎高等先进技术学院）；Institute Polytechnique de Paris（巴黎理工学院）；INRIA（法国国家信息与自动化研究所）；Softbank Robotics Europe（软银机器人欧洲公司）；Thales（泰雷兹集团）
In multi-task reinforcement learning there are two main challenges: at training time, the ability to learn different policies with a single model; at test time, inferring which of those policies applying without an external signal. In the case of continual reinforcement learning a third challenge arises: learning tasks sequentially without forgetting the previous ones. In this paper, we tackle these challenges by proposing DisCoRL, an approach combining state representation learning and policy distillation. We experiment on a sequence of three simulated 2D navigation tasks with a 3 wheel omni-…
Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model
Faculty of Electrical Engineering and Computer Science, Technical University of Berlin（电气工程与计算机科学系，柏林技术大学）；Department of Computer Science, University of Innsbruck（计算机科学系，因斯布鲁克大学）；Digital Science Center (DiSC), University of Innsbruck（数字科学中心（DiSC），因斯布鲁克大学）；Department of Industrial Engineering, University of Trento（工业工程系，特伦托大学）；Singular Research Center on Intelligent Systems (CiTIUS), University of Santiago de Compostela（智能系统研究中心（CiTIUS），圣地亚哥-德孔波斯特拉大学）
For many real-world robotics applications, robots need to continually adapt and learn new concepts. Further, robots need to learn through limited data because of scarcity of labeled data in the real-world environments. To this end, my research focuses on developing robots that continually learn in dynamic unseen environments/scenarios, learn from limited human supervision, remember previously learned knowledge and use that knowledge to learn new concepts. I develop machine learning models that not only produce State-of-the-results on benchmark datasets but also allow robots to learn new object…
University of Waterloo（滑铁卢大学）；Capital One（第一资本金融公司）
In this paper, we consider a challenging but realistic continual learning (CL) problem, Few-Shot Continual Active Learning (FoCAL), where a CL agent is provided with unlabeled data for a new or a previously learned task in each increment and the agent only has limited labeling budget available. Towards this, we build on the continual learning and active learning literature and develop a framework that can allow a CL agent to continually learn new object classes from a few labeled training examples. Our framework represents each object class using a uniform Gaussian mixture model (GMM) and uses…
University of Southern California（南加州大学）；Amazon（亚马逊）
In order to be effective general purpose machines in real world environments, robots not only will need to adapt their existing manipulation skills to new circumstances, they will need to acquire entirely new skills on-the-fly. A great promise of continual learning is to endow robots with this ability, by using their accumulated knowledge and experience from prior skills. We take a fresh look at this problem, by considering a setting in which the robot is limited to storing that knowledge and experience only in the form of learned skill policies. We show that storing skill policies, careful pr…
University of Lincoln（林肯大学）；University of Padua（帕多瓦大学）
Reconstructing accurate causal models of dynamic systems from time-series of sensor data is a key problem in many real-world scenarios. In this paper, we present an overview based on our experience about practical challenges that the causal analysis encounters when applied to autonomous robots and how Continual Learning~(CL) could help to overcome them. We propose a possible way to leverage the CL paradigm to make causal discovery feasible for robotics applications where the computational resources are limited, while at the same time exploiting the robot as an active agent that helps to increa…
