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Learning And Execution Of Object Manipulation Tasks On Humanoid Robots
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Book Synopsis Learning and Execution of Object Manipulation Tasks on Humanoid Robots by : Waechter, Mirko
Download or read book Learning and Execution of Object Manipulation Tasks on Humanoid Robots written by Waechter, Mirko and published by KIT Scientific Publishing. This book was released on 2018-03-21 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Equipping robots with complex capabilities still requires a great amount of effort. In this work, a novel approach is proposed to understand, to represent and to execute object manipulation tasks learned from observation by combining methods of data analysis, graphical modeling and artificial intelligence. Employing this approach enables robots to reason about how to solve tasks in dynamic environments and to adapt to unseen situations.
Book Synopsis Learning and Execution of Object Manipulation Tasks on Humanoid Robots by : Mirko Wächter
Download or read book Learning and Execution of Object Manipulation Tasks on Humanoid Robots written by Mirko Wächter and published by . This book was released on 2020-10-09 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Equipping robots with complex capabilities still requires a great amount of effort. In this work, a novel approach is proposed to understand, to represent and to execute object manipulation tasks learned from observation by combining methods of data analysis, graphical modeling and artificial intelligence. Employing this approach enables robots to reason about how to solve tasks in dynamic environments and to adapt to unseen situations. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Book Synopsis Robot Learning from Human Demonstration by : Sonia Dechter
Download or read book Robot Learning from Human Demonstration written by Sonia Dechter and published by Springer Nature. This book was released on 2022-06-01 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.
Book Synopsis Advanced Bimanual Manipulation by : Bruno Siciliano
Download or read book Advanced Bimanual Manipulation written by Bruno Siciliano and published by Springer. This book was released on 2012-04-10 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dexterous and autonomous manipulation is a key technology for the personal and service robots of the future. Advances in Bimanual Manipulation edited by Bruno Siciliano provides the robotics community with the most noticeable results of the four-year European project DEXMART (DEXterous and autonomous dual-arm hand robotic manipulation with sMART sensory-motor skills: A bridge from natural to artificial cognition). The volume covers a host of highly important topics in the field, concerned with modelling and learning of human manipulation skills, algorithms for task planning, human-robot interaction, and grasping, as well as hardware design of dexterous anthropomorphic hands. The results described in this five-chapter collection are believed to pave the way towards the development of robotic systems endowed with dexterous and human-aware dual-arm/hand manipulation skills for objects, operating with a high degree of autonomy in unstructured real-world environments.
Book Synopsis Motion Planning for Humanoid Robots by : Kensuke Harada
Download or read book Motion Planning for Humanoid Robots written by Kensuke Harada and published by Springer Science & Business Media. This book was released on 2010-08-12 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on humanoid robots has been mostly with the aim of developing robots that can replace humans in the performance of certain tasks. Motion planning for these robots can be quite difficult, due to their complex kinematics, dynamics and environment. It is consequently one of the key research topics in humanoid robotics research and the last few years have witnessed considerable progress in the field. Motion Planning for Humanoid Robots surveys the remarkable recent advancement in both the theoretical and the practical aspects of humanoid motion planning. Various motion planning frameworks are presented in Motion Planning for Humanoid Robots, including one for skill coordination and learning, and one for manipulating and grasping tasks. The problem of planning sequences of contacts that support acyclic motion in a highly constrained environment is addressed and a motion planner that enables a humanoid robot to push an object to a desired location on a cluttered table is described. The main areas of interest include: • whole body motion planning, • task planning, • biped gait planning, and • sensor feedback for motion planning. Torque-level control of multi-contact behavior, autonomous manipulation of moving obstacles, and movement control and planning architecture are also covered. Motion Planning for Humanoid Robots will help readers to understand the current research on humanoid motion planning. It is written for industrial engineers, advanced undergraduate and postgraduate students.
Book Synopsis Robot Programming by Demonstration by : Sylvain Calinon
Download or read book Robot Programming by Demonstration written by Sylvain Calinon and published by EPFL Press. This book was released on 2009-08-24 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in RbD have identified a number of key issues for ensuring a generic approach to the transfer of skills across various agents and contexts. This book focuses on the two generic questions of what to imitate and how to imitate and proposes active teaching methods.
Book Synopsis Understanding and Learning Robotic Manipulation Skills from Humans by : Elena Galbally Herrero
Download or read book Understanding and Learning Robotic Manipulation Skills from Humans written by Elena Galbally Herrero and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Humans are constantly learning new skills and improving upon their existing abilities. In particular, when it comes to manipulating objects, humans are extremely effective at generalizing to new scenarios and using physical compliance to our advantage. Compliance is key to generating robust behaviors by reducing the need to rely on precise trajectories. Programming robots through predefined trajectories has been highly successful for performing tasks in structured environments, such as assembly lines. However, such an approach is not viable for real-time operations in real-world scenarios. Inspired by humans, we propose to program robots at a higher level of abstraction by using primitives that leverage contact information and compliant strategies. Compliance increases robustness to uncertainty in the environment and primitives provide us with atomic actions that can be reused to avoid coding new tasks from scratch. We have developed a framework that allows us to: (i) collect and segment human data from multiple contact-rich tasks through direct or haptic demonstrations, (ii) analyze this data and extract the human's compliant strategy, and (iii) encode the strategy into robot primitives using task-level controllers. During autonomous task execution, haptic interfaces enable human real-time intervention and additional data collection for recovery from failures. At the core of this framework is the notion of a compliant frame - an origin and three directions in space along and about which we control motion and compliance. The compliant frame is attached to the object being manipulated and together with the desired task parameters defines a primitive. Task parameters include desired forces, moments, positions, and orientations. This task specification provides a physically meaningful, low-dimensional, and robot-independent representation. This thesis presents a novel framework for learning manipulation skills from demonstration data. Leveraging compliant frames enables us to understand human actions and extract strategies that generalize across objects and robots. The framework was extensively validated through simulation and hardware experiments, including five real-world construction tasks.
Book Synopsis Cognitive Reasoning for Compliant Robot Manipulation by : Daniel Sebastian Leidner
Download or read book Cognitive Reasoning for Compliant Robot Manipulation written by Daniel Sebastian Leidner and published by Springer. This book was released on 2018-12-08 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to achieve human-like performance, this book covers the four steps of reasoning a robot must provide in the concept of intelligent physical compliance: to represent, plan, execute, and interpret compliant manipulation tasks. A classification of manipulation tasks is conducted to identify the central research questions of the addressed topic. It is investigated how symbolic task descriptions can be translated into meaningful robot commands.Among others, the developed concept is applied in an actual space robotics mission, in which an astronaut aboard the International Space Station (ISS) commands the humanoid robot Rollin' Justin to maintain a Martian solar panel farm in a mock-up environment
Book Synopsis Human-inspired Robot Task Teaching and Learning by : Xianghai Wu
Download or read book Human-inspired Robot Task Teaching and Learning written by Xianghai Wu and published by . This book was released on 2009 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Current methods of robot task teaching and learning have several limitations: highly-trained personnel are usually required to teach robots specific tasks; service-robot systems are limited in learning different types of tasks utilizing the same system; and the teacher's expertise in the task is not well exploited. A human-inspired robot-task teaching and learning method is developed in this research with the aim of allowing general users to teach different object-manipulation tasks to a service robot, which will be able to adapt its learned tasks to new task setups. The proposed method was developed to be interactive and intuitive to the user. In a closed loop with the robot, the user can intuitively teach the tasks, track the learning states of the robot, direct the robot attention to perceive task-related key state changes, and give timely feedback when the robot is practicing the task, while the robot can reveal its learning progress and refine its knowledge based on the user's feedback. The human-inspired method consists of six teaching and learning stages: 1) checking and teaching the needed background knowledge of the robot; 2) introduction of the overall task to be taught to the robot: the hierarchical task structure, and the involved objects and robot hand actions; 3) teaching the task step by step, and directing the robot to perceive important state changes; 4) demonstration of the task in whole, and offering vocal subtask-segmentation cues in subtask transitions; 5) robot learning of the taught task using a flexible vote-based algorithm to segment the demonstrated task trajectories, a probabilistic optimization process to assign obtained task trajectory episodes (segments) to the introduced subtasks, and generalization of the taught task trajectories in different reference frames; and 6) robot practicing of the learned task and refinement of its task knowledge according to the teacher's timely feedback, where the adaptation of the learned task to new task setups is achieved by blending the task trajectories generated from pertinent frames. An agent-based architecture was designed and developed to implement this robot-task teaching and learning method. This system has an interactive human-robot teaching interface subsystem, which is composed of: a) a three-camera stereo vision system to track user hand motion; b) a stereo-camera vision system mounted on the robot end-effector to allow the robot to explore its workspace and identify objects of interest; and c) a speech recognition and text-to-speech system, utilized for the main human-robot interaction. A user study involving ten human subjects was performed using two tasks to evaluate the system based on time spent by the subjects on each teaching stage, efficiency measures of the robot's understanding of users' vocal requests, responses, and feedback, and their subjective evaluations. Another set of experiments was done to analyze the ability of the robot to adapt its previously learned tasks to new task setups using measures such as object, target and robot starting-point poses; alignments of objects on targets; and actual robot grasp and release poses relative to the related objects and targets. The results indicate that the system enabled the subjects to naturally and effectively teach the tasks to the robot and give timely feedback on the robot's practice performance. The robot was able to learn the tasks as expected and adapt its learned tasks to new task setups. The robot properly refined its task knowledge based on the teacher's feedback and successfully applied the refined task knowledge in subsequent task practices. The robot was able to adapt its learned tasks to new task setups that were considerably different from those in the demonstration. The alignments of objects on the target were quite close to those taught, and the executed grasping and releasing poses of the robot relative to objects and targets were almost identical to the taught poses. The robot-task learning ability was affected by limitations of the vision-based human-robot teleoperation interface used in hand-to-hand teaching and the robot's capacity to sense its workspace. Future work will investigate robot learning of a variety of different tasks and the use of more robot in-built primitive skills.
Book Synopsis Learning Mobile Manipulation Actions from Human Demonstrations: an Approach to Learning and Augmenting Action Models and Their Integration Into Task Representations by : Tim Welschehold
Download or read book Learning Mobile Manipulation Actions from Human Demonstrations: an Approach to Learning and Augmenting Action Models and Their Integration Into Task Representations written by Tim Welschehold and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: While incredible advancements in robotics have been achieved over the last decade, direct physical interaction with an initially unknown and dynamic environment is still a challenging problem. In order to use robots as service assistants and take over household chores in the user's home environment, they must be able to perform goal directed manipulation tasks autonomously and further, learn these task intuitively from their owners. Consider for instance the task of setting a breakfast table: Although it is a relatively simple task for a human being, it poses some serious challenges to the robot. It must physically handle the users customized household environment and the objects therein, i.e., how can the items needed to set up the table be grasped and moved, how can the kitchen cabinets be opened, etc. Additionally the personal preferences of the user on how the breakfast table should be arranged must be respected. Due to the diverse characteristics of the custom objects and the individual human needs even a standard task like setting a breakfast table is impossible to pre-program before knowing the place of use and its occurrences. Therefore, the most promising way to engage robots as domestic help is to enable them to learn the tasks they should perform directly by their owners, without requiring the owner to possess any special knowledge of robotics or programming skills. Throughout this thesis we present various contributions addressing these challenges. Although learning from demonstration is a well-established approach to teaching robots without explicit programming, most approaches in literature for learning manipulation actions use kinesthetic training as these actions require thorough knowledge of the interactions between the robot and the object which can be learned directly by kinesthetic teaching since no abstraction is needed. In addition, in most current imitation learning approaches mobile platforms are not considered. In this thesis we present a novel approach to learn joint robot base and end-effector action models from observing demonstrations carried out by a human teacher. To achieve this we adapt trajectory data obtained from RGBD recordings of the human teacher performing the action to the capabilities of the robot. We formulate a graph optimization problem that the links the observed human trajectories with robot grasping capabilities and kinematic constraints between co-occurring base and gripper poses, allowing us to generate robot suitable trajectories. In a next step, we do not just learn individual manipulation actions, but to combine several actions into one task. Challenges arise from handling ambiguous goals and generalizing the task to new settings. We present an approach to learn both representations together from the same teacher demonstrations, one for individual mobile manipulation actions as described above, and one for the representation of the overall task intent. We leverage a framework based on Monte Carlo tree search to compute sequences of feasible actions imitating the teacher intention in new settings without explicitly specifying a task goal. In this way, we can reproduce complex tasks while ensuring that all composing actions are executable in the given setting. The mobile manipulation models mentioned above are encoded as dynamic systems to facilitate interaction with objects in world coordinates. However, this poses the challenge of translating kinematic constraints of the robot to the task space and including them in the action models. In this thesis we propose to couple robot base and end-effector motions generated by arbitrary dynamical systems by modulating the base velocity, while respecting the robots kinematic design. To this end we learn an approximation of the inverse reachability in closed form and implement the coupling as an obstacle avoidance problem. Furthermore, in this work we address the challenge of imitating manipulation actions, the execution of which depends on additional non-geometric quantities as, e.g., contact forces when handing over an object or measured liquid height, while pouring water into a cup. We suggest an approach to include this additional information in form of measured features directly into the action models. These features are recorded in the demonstrations alongside the geometric route of the manipulation action and their correlation is captured in a Gaussian Mixture Model that parametrizes the dynamic system used. This enables us to also couple the motion's geometric trajectory to the perceived features in the scene during action imitation. All the above described contributions were evaluated extensively in real world robot experiments on a PR2 system and a KUKA Iiwa Robot Arm
Book Synopsis From Robot to Human Grasping Simulation by : Beatriz León
Download or read book From Robot to Human Grasping Simulation written by Beatriz León and published by Springer Science & Business Media. This book was released on 2013-09-29 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: The human hand and its dexterity in grasping and manipulating objects are some of the hallmarks of the human species. For years, anatomic and biomechanical studies have deepened the understanding of the human hand’s functioning and, in parallel, the robotics community has been working on the design of robotic hands capable of manipulating objects with a performance similar to that of the human hand. However, although many researchers have partially studied various aspects, to date there has been no comprehensive characterization of the human hand’s function for grasping and manipulation of everyday life objects. This monograph explores the hypothesis that the confluence of both scientific fields, the biomechanical study of the human hand and the analysis of robotic manipulation of objects, would greatly benefit and advance both disciplines through simulation. Therefore, in this book, the current knowledge of robotics and biomechanics guides the design and implementation of a simulation framework focused on manipulation interactions that allows the study of the grasp through simulation. As a result, a valuable framework for the study of the grasp, with relevant applications in several fields such as robotics, biomechanics, ergonomics, rehabilitation and medicine, has been made available to these communities.
Book Synopsis Algorithms and Law by : Martin Ebers
Download or read book Algorithms and Law written by Martin Ebers and published by Cambridge University Press. This book was released on 2020-07-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms permeate our lives in numerous ways, performing tasks that until recently could only be carried out by humans. Artificial Intelligence (AI) technologies, based on machine learning algorithms and big-data-powered systems, can perform sophisticated tasks such as driving cars, analyzing medical data, and evaluating and executing complex financial transactions - often without active human control or supervision. Algorithms also play an important role in determining retail pricing, online advertising, loan qualification, and airport security. In this work, Martin Ebers and Susana Navas bring together a group of scholars and practitioners from across Europe and the US to analyze how this shift from human actors to computers presents both practical and conceptual challenges for legal and regulatory systems. This book should be read by anyone interested in the intersection between computer science and law, how the law can better regulate algorithmic design, and the legal ramifications for citizens whose behavior is increasingly dictated by algorithms.
Book Synopsis Robot Learning Human Skills and Intelligent Control Design by : Chenguang Yang
Download or read book Robot Learning Human Skills and Intelligent Control Design written by Chenguang Yang and published by CRC Press. This book was released on 2021-06-21 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techniques have been developed and successfully implemented for various robotic tasks. Among these methods, learning from demonstrations (LfD) enables robots to effectively and efficiently acquire skills by learning from human demonstrators, such that a robot can be quickly programmed to perform a new task. This book introduces recent results on the development of advanced LfD-based learning and control approaches to improve the robot dexterous manipulation. First, there's an introduction to the simulation tools and robot platforms used in the authors' research. In order to enable a robot learning of human-like adaptive skills, the book explains how to transfer a human user’s arm variable stiffness to the robot, based on the online estimation from the muscle electromyography (EMG). Next, the motion and impedance profiles can be both modelled by dynamical movement primitives such that both of them can be planned and generalized for new tasks. Furthermore, the book introduces how to learn the correlation between signals collected from demonstration, i.e., motion trajectory, stiffness profile estimated from EMG and interaction force, using statistical models such as hidden semi-Markov model and Gaussian Mixture Regression. Several widely used human-robot interaction interfaces (such as motion capture-based teleoperation) are presented, which allow a human user to interact with a robot and transfer movements to it in both simulation and real-word environments. Finally, improved performance of robot manipulation resulted from neural network enhanced control strategies is presented. A large number of examples of simulation and experiments of daily life tasks are included in this book to facilitate better understanding of the readers.
Book Synopsis Robot Physical Interaction through the combination of Vision, Tactile and Force Feedback by : Mario Prats
Download or read book Robot Physical Interaction through the combination of Vision, Tactile and Force Feedback written by Mario Prats and published by Springer. This book was released on 2012-10-05 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot manipulation is a great challenge; it encompasses versatility -adaptation to different situations-, autonomy -independent robot operation-, and dependability -for success under modeling or sensing errors. A complete manipulation task involves, first, a suitable grasp or contact configuration, and the subsequent motion required by the task. This monograph presents a unified framework by introducing task-related aspects into the knowledge-based grasp concept, leading to task-oriented grasps. Similarly, grasp-related issues are also considered during the execution of a task, leading to grasp-oriented tasks which is called framework for physical interaction (FPI). The book presents the theoretical framework for the versatile specification of physical interaction tasks, as well as the problem of autonomous planning of these tasks. A further focus is on sensor-based dependable execution combining three different types of sensors: force, vision and tactile. The FPI approach allows to perform a wide range of robot manipulation tasks. All contributions are validated with several experiments using different real robots placed on household environments; for instance, a high-DoF humanoid robot can successfully operate unmodeled mechanisms with widely varying structure in a general way with natural motions. This research was recipient of the European Georges Giralt Award and the Robotdalen Scientific Award Honorary Mention.
Book Synopsis A Perspective on Artificial Intelligence in Manufacturing by : George Chryssolouris
Download or read book A Perspective on Artificial Intelligence in Manufacturing written by George Chryssolouris and published by Springer Nature. This book was released on 2023-01-01 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates the rapid developments of Artificial intelligence (AI) as well as their capability to address efficiently and in a cost-effective manner issues of the manufacturing field. A number of manufacturing applications of AI related to manufacturing processes, robots, automation and manufacturing systems design and control, are presented and discussed. The book includes an outlook on a way forward to intelligent manufacturing, through AI. The real benefit from AI in manufacturing will not only derive through the automation of tasks but also through the provision of new levels of autonomy that will make entirely new applications possible and introduce new business processes in manufacturing.
Book Synopsis Intelligent Human Computer Interaction by : Anupam Basu
Download or read book Intelligent Human Computer Interaction written by Anupam Basu and published by Springer. This book was released on 2017-01-20 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 8th International Conference on Intelligent Human Computer Interaction, IHCI 2016, held in Pilani, India, in December 2016. The 22 regular papers and 3 abstracts of invited talks included in this volume were carefully reviewed and selected from 115 initial submissions. They deal with intelligent interfaces; brain machine interaction; HCI applications and technology; and interface and systems.
Book Synopsis Learning to Make Decisions in Robotic Manipulation by : Siyu Dai (Scientist in mechanical engineering)
Download or read book Learning to Make Decisions in Robotic Manipulation written by Siyu Dai (Scientist in mechanical engineering) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order for human-assisting robots to be deployed in the real world such as household environments, challenges in two major scenarios remain to be solved. First, for common tasks that the robot conducts day-to-day, the execution of motion plans need to ensure the safety of surrounding objects and humans. Second, to handle new tasks that some customers might occasionally demand, robots need to be able to learn novel tasks efficiently with a minimal amount of human supervision. In this thesis, we show that machine learning methods can be applied to solve challenges in both scenarios. In the first scenario, we propose learning-based p-Chekov, a chance-constrained motion planning approach that utilizes data-driven methods to obtain safe motion plans in real time. By pre-training a collision risk estimation model off-line instead of conducting online sampling-based risk estimation, learning-based p-Chekov is able to significantly improve the planning speed while maintaining the chance-constraint satisfaction performance. In the second scenario of learning new tasks, we first propose empowerment-based intrinsic motivation, a reinforcement learning (RL) approach that allows robots to learn novel tasks with only sparse or binary reward functions. Through maximizing the mutual dependence between robot actions and environment states, namely the empowerment, this intrinsic motivation helps the agent to focus more on the states where it can effectively "control" the environment during exploration instead of the parts where its actions cause random and unpredictable consequences. Empirical evaluations in different robotic manipulation environments with different shapes of the target object demonstrate that this empowerment-based intrinsic motivation approach can obtain higher extrinsic task rewards faster than other state-of-the-art solutions to sparse-reward RL tasks. Another approach we propose in the second scenario is automatic curricula via expert demonstrations (ACED), an imitation learning method that leverages the idea of curriculum learning and allows robots to learn long-horizon tasks when only provided with a handful of demonstration trajectories. Through moving the reset states from the end to the beginning of demonstrations as the learning agent improves its performance, ACED not only learns challenging manipulation tasks with unseen initializations and goals, but also discovers novel solutions that are distinct from the demonstrations. In addition, ACED can be naturally combined with other imitation learning methods to utilize expert demonstrations in a more efficient manner and allow robotic manipulators to learn novel tasks that other state-of-the-art automatic curriculum learning methods cannot learn. In the experiments presented in this thesis, we show that a combination of ACED with behavior cloning allows pick-and-place tasks to be learned with as few as one demonstration and block stacking tasks to be learned with twenty demonstrations.
