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policy gradient reinforcement learning for fast quadrupedal locomotion

Author(s): Nate Kohl, Peter Stone
Venue: IEEE International Conference on Robotics and Automation (ICRA)
Year Published: 2004
Keywords: reinforcement learning, policy gradients, locomotion, legged robots
Expert Opinion: The work is practical in that it allowed the authors to improve the walking speed of Aibos, something essential to creating top-flight robocup players. The reason I adore this work and frequently cite it in my talks on machine learning is the fantastic way it allowed the robots to learn autonomously. In particular, for the Aibo robots to succeed in robocup, they need to be able to localize on the field based on their perception of provided markers. The authors enabled the robots to measure their own walking speed leveraging this capability. By marching a team of robots back and forth across the width of the pitch, experimenting with and evaluating different gaits each time, the robots were able to find movement patterns that surpassed hand-designed ones. It's a beautiful example of exploiting measurable quantities to drive learning---a key enabling technology for robot learning.

end-to-end training of deep visuomotor policies

Author(s): Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
Venue: Journal of Machine Learning Research
Year Published: 2016
Keywords: manipulation, probabilistic models, planning, locomotion, learning from demonstration, reinforcement learning, neural networks, visual perception
Expert Opinion: This is an excellent example of reinforcement learning applied to closed-loop visual control for challenging robotics tasks and a good example of the application of deep-learning to real-world robotics.

intrinsic motivation systems for autonomous mental development

Author(s): Pierre-Yves Oudeyer, Frederic Kaplan, and Verena V. Hafner
Venue: IEEE Transactions on Evolutionary Computation (Volume 11, Issue 2)
Year Published: 2007
Keywords: reinforcement learning, evolution, neural networks
Expert Opinion: This article describes some of the first successful experiments about "curious robots" and intrinsic motivation. It is one of the foundational articles in the "developmental robotics" field and inspired hundreds of papers about intrinsic motivation in reinforcement learning.

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: Somewhat repeating myself from the last suggestion: for learning robot behavior, reinforcement learning is an essential tool. While Sutton & Barto do not focus specifically on the case of robotics, their book is a very accessible text that nevertheless manages to cover many aspects, techniques, and challenges in reinforcement learning.

movement imitation with nonlinear dynamical systems in humanoid robots

Author(s): Auke Jan Ijspeert, Jun Nakanishi, Stefan Schaal
Venue: IEEE International Conference on Robotics and Automation (ICRA)
Year Published: 2002
Keywords: probabilistic models, nonlinear systems, dynamical systems, learning from demonstration, humanoid robotics
Expert Opinion: This paper introduced Dynamic Motor Primitives (DMPs) - a very prominent representation for robot motion used in many learning approaches. While originally introduced as an imitation learning approach, DMPs have gone on to become central to many reinforcement learning papers. Many modern approaches that end with the word "primitive" are descendants of this work, including Probabilistic Motion Primitives (ProMPs), or Interaction Primitives.

probabilistic robotics

Author(s): Sebastian Thrun, Wolfram Burgard, Dieter Fox
Venue: Book
Year Published: 2005
Keywords: probabilistic models
Expert Opinion: It laid out basis for robotics in uncertain real world.

maximum entropy inverse reinforcement learning

Author(s): Brian D. Ziebart, Andrew Maas, J.Andrew Bagnell, and Anind K. Dey
Venue: AAAI Conference on Artificial Intelligence
Year Published: 2008
Keywords: probabilistic models, learning from demonstration, reinforcement learning
Expert Opinion: This is a seminal paper for IRL. It has not only become a standard way to think about IRL, but the observation model for a demonstration given the reward has propagated to many other related areas, like goal inference, human prediction, etc.

learning and generalization of motor skills by learning from demonstration

Author(s): Peter Pastor, Heiko Hoffmann, Tamim Asfour, and Stefan Schaal
Venue: IEEE International Conference on Robotics and Automation (ICRA)
Year Published: 2009
Keywords: planning, learning from demonstration
Expert Opinion: Not the first DMP paper, but the most understandable and with fixes to some annoying problems with the original formulation. Incredibly simple idea, but that's the nice thing about it -- it is a great starting point for talking about what generalization means in policy learning and how a restricted policy representation with the right inductive bias can allow you to learn something meaningful from a single trajectory, as well as learn quickly from practice.

hindsight experience replay

Author(s): Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, Wojciech Zaremba
Venue: Neural Information Processing Systems Conference (NeurIPS)
Year Published: 2018
Keywords: manipulation, humanoid robotics, reinforcement learning, neural networks
Expert Opinion: HER addresses the issue of sample inefficiency in DRL, especially for those problems with sparse and binary reward functions. It has become one of the most effective algorithms for learning problems with multiple goals which have the potential to solve many challenging manipulation tasks. The idea of "EVERY experience is a good experience for SOME task" is a powerful insight that succinctly reflects how we teach our children to be lifelong learners. We should teach our robots the same way.

robotic grasping of novel objects using vision

Author(s): Ashutosh Saxena, Justin Driemeyer, Andrew Y. Ng
Venue: International Journal of Robotics Research
Year Published: 2008
Keywords: neural networks, dynamical systems, visual perception, learning from demonstration, manipulation, planning
Expert Opinion: This is one of the first works in literature that utilized machine learning for the robotic manipulation problem. The proposed framework is still useful to design similar robot learning solutions. The particular importance of this work is to identify local features that are related to manipulation planning

from skills to symbols: learning symbolic representations for abstract high-level planning

Author(s): George Konidaris, Leslie Pack Kaelbling, Tomas Lozano-Perez
Venue: Journal of Artificial Intelligence Research
Year Published: 2018
Keywords: probabilistic models, planning
Expert Opinion: Abstraction is an important aspect of robot learning. This paper addresses the issue of learning state abstractions for efficient high-level planning. Importantly, the state abstraction should be induced from the set of skills/options that the robot is capable of executing. The resulting abstraction can then be used to determine if any plan is feasible. The paper addresses both deterministic and probabilistic planning. It is also a great example of learning the preconditions and effects of skills for planning complex tasks.

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: This work presents the first successful application of imitation learning to an autonomous system. I think this is a pivotal work in the early years of robot learning.

autonomous helicopter aerobatics through apprenticeship learning

Author(s): Pieter Abbeel, Adam Coates and Andrew Y. Ng
Venue: International Journal of Robotics Research
Year Published: 2010
Keywords: learning from demonstration, optimal control, dynamical systems
Expert Opinion: Real application of methods based on inverse reinforcement learning to learn to control a helicopter in highly difficult aerobatic maneuvers.

pilco: a model-based and data-efficient approach to policy search

Author(s): Marc Peter Deisenroth, Carl Edward Rasmussen
Venue: International Conference of Machine Learning
Year Published: 2011
Keywords: state estimation, reinforcement learning, probabilistic models, gaussians, dynamical systems, visual perception, policy gradients
Expert Opinion: it is a nice answer to the problem of learning models.

dynamical movement primitives: learning attractor models for motor behaviors

Author(s): Auke Jan Ijspeert, Jun Nakanishi, Heiko Hoffmann, Peter Pastor, Stefan Schaal
Venue: Neural Computation (Volume 25, Issue 2)
Year Published: 2013
Keywords: planning, learning from demonstration, dynamical systems, nonlinear systems
Expert Opinion: Not the first paper on Dynamical movement primitives, but a great update on DMP.

probabilistic movement primitives

Author(s): Alexandros Paraschos, Christian Daniel, Jan Peters, and Gerhard Neumann
Venue: Neural Information Processing Systems Conference (NeurIPS)
Year Published: 2013
Keywords: manipulation, probabilistic models, gaussians, planning, learning from demonstration
Expert Opinion: This and the following papers using ProMPs, because they provided a very nice formulation for representing probabilistic movement primitives. ProMPs have many advantages and I found them better than classical DMPs in many robotics applications, from gestures to whole-body manipulations.

a survey on policy search for robotics

Author(s): Marc Peter Deisenroth, Gerhard Neumann, Jan Peters
Venue: Book
Year Published: 2013
Keywords: survey, reinforcement learning
Expert Opinion: A great unifying view on policy search

a reduction of imitation learning and structured prediction to no-regret online learning

Author(s): Stephane Ross, Geoffrey J. Gordon, J. Andrew Bagnell
Venue: 14th International Conference on Artificial Intelligence and Statistics
Year Published: 2011
Keywords: neural networks, learning from demonstration, dynamical systems
Expert Opinion: Introduces Data Aggregation and the general approach of viewing policy optimization as online learning. Formalizes the notion of interaction with an expert as surrogate objective to the usual policy optimization objective.

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