2,526 research outputs found
A Review on Robot Manipulation Methods in Human-Robot Interactions
Robot manipulation is an important part of human-robot interaction
technology. However, traditional pre-programmed methods can only accomplish
simple and repetitive tasks. To enable effective communication between robots
and humans, and to predict and adapt to uncertain environments, this paper
reviews recent autonomous and adaptive learning in robotic manipulation
algorithms. It includes typical applications and challenges of human-robot
interaction, fundamental tasks of robot manipulation and one of the most widely
used formulations of robot manipulation, Markov Decision Process. Recent
research focusing on robot manipulation is mainly based on Reinforcement
Learning and Imitation Learning. This review paper shows the importance of Deep
Reinforcement Learning, which plays an important role in manipulating robots to
complete complex tasks in disturbed and unfamiliar environments. With the
introduction of Imitation Learning, it is possible for robot manipulation to
get rid of reward function design and achieve a simple, stable and supervised
learning process. This paper reviews and compares the main features and popular
algorithms for both Reinforcement Learning and Imitation Learning
Robot skill learning through human demonstration and interaction
Nowadays robots are increasingly involved in more complex and less structured tasks. Therefore, it is highly desirable to develop new approaches to fast robot skill acquisition. This research is aimed to develop an overall framework for robot skill learning through human demonstration and interaction. Through low-level demonstration and interaction with humans, the robot can learn basic skills. These basic skills are treated as primitive actions. In high-level learning, the complex skills demonstrated by the human can be automatically translated into skill scripts which are executed by the robot. This dissertation summarizes my major research activities in robot skill learning. First, a framework for Programming by Demonstration (PbD) with reinforcement learning for human-robot collaborative manipulation tasks is described. With this framework, the robot can learn low level skills such as collaborating with a human to lift a table successfully and efficiently. Second, to develop a high-level skill acquisition system, we explore the use of a 3D sensor to recognize human actions. A Kinect based action recognition system is implemented which considers both object/action dependencies and the sequential constraints. Third, we extend the action recognition framework by fusing information from multimodal sensors which can recognize fine assembly actions. Fourth, a Portable Assembly Demonstration (PAD) system is built which can automatically generate skill scripts from human demonstration. The skill script includes the object type, the tool, the action used, and the assembly state. Finally, the generated skill scripts are implemented by a dual-arm robot. The proposed framework was experimentally evaluated
Boosting Reinforcement Learning and Planning with Demonstrations: A Survey
Although reinforcement learning has seen tremendous success recently, this
kind of trial-and-error learning can be impractical or inefficient in complex
environments. The use of demonstrations, on the other hand, enables agents to
benefit from expert knowledge rather than having to discover the best action to
take through exploration. In this survey, we discuss the advantages of using
demonstrations in sequential decision making, various ways to apply
demonstrations in learning-based decision making paradigms (for example,
reinforcement learning and planning in the learned models), and how to collect
the demonstrations in various scenarios. Additionally, we exemplify a practical
pipeline for generating and utilizing demonstrations in the recently proposed
ManiSkill robot learning benchmark
Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems
Recent successes combine reinforcement learning algorithms and deep neural
networks, despite reinforcement learning not being widely applied to robotics
and real world scenarios. This can be attributed to the fact that current
state-of-the-art, end-to-end reinforcement learning approaches still require
thousands or millions of data samples to converge to a satisfactory policy and
are subject to catastrophic failures during training. Conversely, in real world
scenarios and after just a few data samples, humans are able to either provide
demonstrations of the task, intervene to prevent catastrophic actions, or
simply evaluate if the policy is performing correctly. This research
investigates how to integrate these human interaction modalities to the
reinforcement learning loop, increasing sample efficiency and enabling
real-time reinforcement learning in robotics and real world scenarios. This
novel theoretical foundation is called Cycle-of-Learning, a reference to how
different human interaction modalities, namely, task demonstration,
intervention, and evaluation, are cycled and combined to reinforcement learning
algorithms. Results presented in this work show that the reward signal that is
learned based upon human interaction accelerates the rate of learning of
reinforcement learning algorithms and that learning from a combination of human
demonstrations and interventions is faster and more sample efficient when
compared to traditional supervised learning algorithms. Finally,
Cycle-of-Learning develops an effective transition between policies learned
using human demonstrations and interventions to reinforcement learning. The
theoretical foundation developed by this research opens new research paths to
human-agent teaming scenarios where autonomous agents are able to learn from
human teammates and adapt to mission performance metrics in real-time and in
real world scenarios.Comment: PhD thesis, Aerospace Engineering, Texas A&M (2020). For more
information, see https://vggoecks.com
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