Found 9 results.

an evolutionary approach to gait learning for four-legged robots

Author(s): Sonia Chernova, Manuela Veloso
Venue: International Conference on Intelligent Robots and Systems
Year Published: 2004
Keywords: planning, mobile robots, evolution, legged robots, genetic algorithms, locomotion
Expert Opinion: This paper presents a clear and concrete mapping of genetic algorithms to a compelling hardware domain: Sony AIBO walking gait and the RoboCup soccer competition. The AIBO was an example of a platform where parameter tuning by hand is particularly tedious (54 parameters), plus the platform was safe to have "practice‚" on its own overnight (i.e. without human supervision, a rarity for mobile robots)---offering an opportunity for fully autonomous and on-hardware optimization-based learning, where it was feasible for each generation to be evaluated (according to the fitness function) on the actual robot platform without human supervision or intervention. The learned walk that resulted outperformed all hand-tuned and learned walks that participated in (including that which won) the RoboCup 2003 competition. ** This recommendation is getting a bit into the weeds of specific algorithms---not quite sure if the list is planning to go that deep. It's a work I would present in a class, as a great example of a CS algorithm being translated for use on real robot hardware. Again, not quite sure if that sort of categorization fits the bill.

reinforcement learning: an introduction

Author(s): Richard S. Sutton and Andrew G. Barto
Venue: Book
Year Published: 2018
Keywords: mobile robots, reinforcement learning, unsupervised learning, optimal control, genetic algorithms
Expert Opinion: It presents the definite theoretical basis of reinforcement learning, used widely in robotics.

cad2rl: real single-image flight without a single real image

Author(s): Fereshteh Sadeghi, Sergey Levine
Venue: Robotics: Science and Systems Conference
Year Published: 2017
Keywords: neural networks, reinforcement learning, mobile robots
Expert Opinion: The CAD2RL paper demonstrated that it was possible to train a policy, using Reinforcement Learning, entirely in simulation and zero-shot transfer it to a real world robot. It paved the way for a lot of follow up work on domain transfer and domain randomization for robotic perception and control.

affordances in psychology, neuroscience and robotics: a survey

Author(s): Lorenzo Jamone, Emre Ugur, Angelo Cangelosi, Luciano Fadiga, Alexandre Bernardino, Justus Piater and Jose Santos-Victor
Venue: IEEE Transactions on Cognitive and Developmental Systems
Year Published: 2018
Keywords: survey, visual perception, mobile robots, reinforcement learning
Expert Opinion: Affordances is an important term for robot learning, but also one that tends to be overloaded and can lead to confusion. If an object allows an agent to perform an action, then the object is said to afford the action to that agent. Affordances can generally be learned autonomously and are thus a fundamental aspect of self-supervised learning for autonomous robots. The nuances of the term however are still widely discussed in robotics and other fields. As a result, one should be aware of the ambiguity and different perspectives regarding the term when talking about affordances. This survey paper discusses some of the nuanced interpretations of the term affordances.

particle filter networks with application to visual localization

Author(s): Peter Karkus, David Hsu, Wee Sun Lee
Venue: Proceedings of The 2nd Conference on Robot Learning
Year Published: 2018
Keywords: state estimation, neural networks, mobile robots
Expert Opinion: makes clear how the algorithmic ideas from before and end-to-end learning can be combined

square root sam: simultaneous localization and mapping via square root information smoothing

Author(s): Frank Dellaert, Michael Kaess
Venue: Intl. J. of Robotics Research
Year Published: 2006
Keywords: manipulation, planning, mobile robots, state estimation, visual perception, probabilistic models
Expert Opinion: This paper, as well as the follow up iSAM2 paper, focuses on treating localization and mapping problems as nonlinear least squares and then optimizing as efficiently as possible. This is a powerful technique that serves as the backbone for many SAM solvers, and, can be applied more generally to all sorts of inference problems in robotics. Techniques building on this work have been used in planning, manipulation, and control.

reinforcement learning in robotics: a survey

Author(s): Jens Kober, J. Andrew Bagnell, Jan Peters
Venue: International Journal of Robotics Research
Year Published: 2014
Keywords: survey, reinforcement learning, learning from demonstration, optimal control, mobile robots
Expert Opinion: This survey was published at a time when there was still a significant gap between reinforcement learning and its practical employment on real robot hardware. For the majority of real world domains, rollouts are impractical to perform on actual hardware---because, for example, the state/action spaces are continuous, exploration can be dangerous, and rollouts take much longer when physically executed---plus often simulators are too dissimilar to the real world and hardware for what is learned to transfer well. To get reinforcement learning to be effective on a real hardware system, therefore, the devil is in the details, and this article addresses just that. Today the gap is narrowing, in part because of advances in computation, but also because of implementation "tricks‚" becoming codified. This article is a bit of a one-stop-shop for pulling together a lot of these tricks, and putting some theoretical rigor and thought behind why and when they work.

alvinn: an autonomous land vehicle in a neural network

Author(s): Dean A. Pomerleau
Venue: MITP
Year Published: 1989
Keywords: mobile robots, learning from demonstration, neural networks
Expert Opinion: On the theoretical side, the first paper to recognize covariate shift in imitation learning and provide a simple data-augmentation style strategy to improve it. On the implementation side, a real self-driving first that led to "No Hands Across America".

the cityscapes dataset for semantic urban scene understanding

Author(s): Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, Bernt Schiele
Venue: IEEE Conference on Computer Vision and Pattern Recognition
Year Published: 2016
Keywords: visual perception, neural networks, mobile robots
Expert Opinion: Deep Learning has transformed the Robot Learning field completely in the last year. Whereas e.g. Computer Vision applications have access to large volumes of data (necessary to train DNNs), Robotics applications generally suffer from the problem of collecting enough data for learning tasks. The KITTI dataset is one example of a great effort to provide relevant data for Robot (or autonomous vehicle) Learning.