5,488 research outputs found
A vision-guided parallel parking system for a mobile robot using approximate policy iteration
Reinforcement Learning (RL) methods enable autonomous robots to learn skills from scratch by interacting with the environment. However, reinforcement learning can be very time consuming. This paper focuses on accelerating the reinforcement learning process on a mobile robot in an unknown environment. The presented algorithm is based on approximate policy iteration with a continuous state space and a fixed number of actions. The action-value function is represented by a weighted combination of basis functions.
Furthermore, a complexity analysis is provided to show that the implemented approach is guaranteed to converge on an optimal policy with less computational time.
A parallel parking task is selected for testing purposes. In the experiments, the efficiency of the proposed approach is demonstrated and analyzed through a set of simulated and real robot experiments, with comparison drawn from two well known algorithms (Dyna-Q and Q-learning)
Learning visual docking for non-holonomic autonomous vehicles
This paper presents a new method of learning visual docking skills for non-holonomic vehicles by direct interaction with the environment. The method is based on a reinforcement algorithm, which speeds up Q-learning by applying memorybased sweeping and enforcing the āadjoining propertyā, a filtering mechanism to only allow transitions between states that satisfy a fixed distance. The method overcomes some limitations of reinforcement learning techniques when they are employed in applications with continuous non-linear systems, such as car-like vehicles. In particular, a good approximation to the optimal
behaviour is obtained by a small look-up table. The algorithm is tested within an image-based visual servoing framework on a docking task. The training time was less than 1 hour on the real vehicle. In experiments, we show the satisfactory performance of the algorithm
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
This paper was motivated by the problem of how to make robots fuse and
transfer their experience so that they can effectively use prior knowledge and
quickly adapt to new environments. To address the problem, we present a
learning architecture for navigation in cloud robotic systems: Lifelong
Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge
fusion algorithm for upgrading a shared model deployed on the cloud. Then,
effective transfer learning methods in LFRL are introduced. LFRL is consistent
with human cognitive science and fits well in cloud robotic systems.
Experiments show that LFRL greatly improves the efficiency of reinforcement
learning for robot navigation. The cloud robotic system deployment also shows
that LFRL is capable of fusing prior knowledge. In addition, we release a cloud
robotic navigation-learning website based on LFRL
Vision-based reinforcement learning using approximate policy iteration
A major issue for reinforcement learning (RL) applied to robotics is the time required to learn a new skill. While RL has been used to learn mobile robot control in many simulated domains, applications involving learning on real
robots are still relatively rare. In this paper, the Least-Squares Policy Iteration (LSPI) reinforcement learning algorithm and a new model-based algorithm Least-Squares Policy Iteration with Prioritized Sweeping (LSPI+), are implemented on a mobile robot to acquire new skills quickly and efficiently. LSPI+ combines the benefits of LSPI and prioritized sweeping, which uses all previous experience to focus the computational effort on the most āinterestingā or dynamic parts of the state space.
The proposed algorithms are tested on a household vacuum
cleaner robot for learning a docking task using vision as the only sensor modality. In experiments these algorithms are compared to other model-based and model-free RL algorithms. The results show that the number of trials required to learn the docking task is significantly reduced using LSPI compared to the other RL algorithms investigated, and that LSPI+ further improves on the performance of LSPI
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
Millirobots are a promising robotic platform for many applications due to
their small size and low manufacturing costs. Legged millirobots, in
particular, can provide increased mobility in complex environments and improved
scaling of obstacles. However, controlling these small, highly dynamic, and
underactuated legged systems is difficult. Hand-engineered controllers can
sometimes control these legged millirobots, but they have difficulties with
dynamic maneuvers and complex terrains. We present an approach for controlling
a real-world legged millirobot that is based on learned neural network models.
Using less than 17 minutes of data, our method can learn a predictive model of
the robot's dynamics that can enable effective gaits to be synthesized on the
fly for following user-specified waypoints on a given terrain. Furthermore, by
leveraging expressive, high-capacity neural network models, our approach allows
for these predictions to be directly conditioned on camera images, endowing the
robot with the ability to predict how different terrains might affect its
dynamics. This enables sample-efficient and effective learning for locomotion
of a dynamic legged millirobot on various terrains, including gravel, turf,
carpet, and styrofoam. Experiment videos can be found at
https://sites.google.com/view/imageconddy
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
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