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Reinforcement Learning in Robotics: A Survey
"... Reinforcement learning offers to robotics a framework and set oftoolsfor the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between di ..."
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Cited by 39 (2 self)
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Reinforcement learning offers to robotics a framework and set oftoolsfor the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between modelbased and model-free as well as between value function-based and policy search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and
Learning to select and generalize striking movements in robot table tennis
- In Proceedings of the AAAI 2012 Fall Symposium on robots that Learn Interactively from Human Teachers
, 2012
"... Learning new motor tasks from physical interactions is an important goal for both robotics and machine learning. However, when moving beyond basic skills, most monolithic machine learning approaches fail to scale. For more complex skills, methods that are tailored for the domain of skill learning ar ..."
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Cited by 28 (12 self)
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Learning new motor tasks from physical interactions is an important goal for both robotics and machine learning. However, when moving beyond basic skills, most monolithic machine learning approaches fail to scale. For more complex skills, methods that are tailored for the domain of skill learning are needed. In this paper, we take the task of learning table tennis as an example and present a new framework that allows a robot to learn cooperative table tennis from physical interaction with a human. The robot first learns a set of elementary table tennis hitting movements from a human table tennis teacher by kinesthetic teach-in, which is compiled into a set of motor primitives represented by dynamical systems. The robot subsequently generalizes these movements to a wider range of situations using our mixture of motor primitives approach. The resulting policy enables the robot to select appropriate motor primitives as well as to generalize between them. Finally, the robot plays with a human table tennis partner and learns online to improve its behavior. We show that the resulting setup is capable of playing table tennis using an anthropomorphic robot arm. 1
Improving Imitated Grasping Motions through Interactive Expected Deviation Learning
"... Abstract — One of the major obstacles that hinders the application of robots to human day-to-day tasks is the current lack of flexible learning methods to endow the robots with the necessary skills and to allow them to adapt to new situations. In this work, we present a new intuitive method for teac ..."
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Abstract — One of the major obstacles that hinders the application of robots to human day-to-day tasks is the current lack of flexible learning methods to endow the robots with the necessary skills and to allow them to adapt to new situations. In this work, we present a new intuitive method for teaching a robot anthropomorphic motion primitives. Our method combines the advantages of reinforcement and imitation learning in a single coherent framework. In contrast to existing approaches that use human demonstrations merely as an initialization for reinforcement learning, our method treats both ways of learning as homologous modules and chooses the most appropriate one in every situation. We apply Gaussian Process Regression to generalize a measure of value across the combined state-action-space. Based on the expected value, uncertainty, and expected deviation of generalized movements, our method decides whether to ask for a human demonstration or to improve its performance on its own, using reinforcement learning. The latter employs a probabilistic search strategy, based on expected deviation, that greatly accelerates learning while protecting the robot from unpredictable movements at the same time. To evaluate the performance of our approach, we conducted a series of experiments and successfully trained a robot to grasp an object at arbitrary positions on a table. I.
Learning Sequential Tasks Interactively from Demonstrations and Own Experience
"... Abstract — Deploying robots to our day-to-day life requires them to have the ability to learn from their environment in order to acquire new task knowledge and to flexibly adapt existing skills to various situations. For typical real-world tasks, it is not sufficient to endow robots with a set of pr ..."
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Abstract — Deploying robots to our day-to-day life requires them to have the ability to learn from their environment in order to acquire new task knowledge and to flexibly adapt existing skills to various situations. For typical real-world tasks, it is not sufficient to endow robots with a set of primitive actions. Rather, they need to learn how to sequence these in order to achieve a desired effect on their environment. In this paper, we propose an intuitive learning method for a robot to acquire sequences of motions by combining learning from human demonstrations and reinforcement learning. In every situation, our approach treats both ways of learning as alternative control flows to optimally exploit their strengths without inheriting their shortcomings. Using a Gaussian Process approximation of the state-action sequence value function, our approach generalizes values observed from demonstrated and autonomously generated action sequences to unknown inputs. This approximation is based on a kernel we designed to account for different representations of tasks and action sequences as well as inputs of variable length. From the expected deviation of value estimates, we devise a greedy exploration policy following a Bayesian optimization criterion that quickly converges learning to promising action sequences while protecting the robot from sequences with unpredictable outcome. We demonstrate the ability of our approach to efficiently learn appropriate action sequences in various situations on a manipulation task involving stacked boxes. I.
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Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations
"... Fig. 1. A low-cost sensor glove is used to teleoperate a five-finger robot hand. The robot hand is equipped with tactile sensors (the iCub hand is shown in the picture). Tactile information provides force feedback to the teleoperator through activating vibration motors at the glove’s fingertips. Abs ..."
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Fig. 1. A low-cost sensor glove is used to teleoperate a five-finger robot hand. The robot hand is equipped with tactile sensors (the iCub hand is shown in the picture). Tactile information provides force feedback to the teleoperator through activating vibration motors at the glove’s fingertips. Abstract — Sensor gloves are popular input devices for a large variety of applications including health monitoring, control of music instruments, learning sign language, dexterous computer interfaces, and teleoperating robot hands [1]. Many commercial products as well as low-cost open source projects have been developed.1 We discuss here how low-cost (approx. 250 EUROs) sensor gloves with force feedback can be build, provide an open source software interface for Matlab and present first results in learning object manipulation skills through imitation learning on the humanoid robot iCub. I.
