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

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.

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 was the paper which first seriously popularized robotics as a problem of interest to the deep learning community. Many of the previous papers that applied deep learning techniques to robotics tasks looked at the perception problem in isolation. This paper hinted at the fact that the entire stack, from perception to actuation, could potentially be recast as a learning problem without suffering a catastrophic degradation in sample efficiency.

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.

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.

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.

Author(s): Dean A. Pomerleau
Venue: MITP
Year Published: 1989
Keywords: mobile robots, learning from demonstration, neural networks
Expert Opinion: This work was pioneering with respect to machine learning in robotics broadly, learning from demonstration specifically, and also autonomous driving. It applied a neural net to learn steering angles from examples of human driving (even online!), way back in 1989. By today's deep learning standards the net was tiny (5 hidden units) and the sensor input extremely limited (30x32 image pixels), but it worked... and at a time when robots rarely operated outside of the lab or factory, and machine learning was rarely deployed on real hardware. It is the first* example of using demonstration-based learning for high-stakes control, that required (comparatively) fast sampling (25Hz) and operated a large van at regular road speeds (20mph). The vehicle was a part of NavLab, which was the precursor to CMU's DARPA Grand and Urban Challenges entires in the early 2000's, and those challenges in turn played a big role in accelerating today's driverless car boom. * To my knowledge! ...also, there actually are two papers (and my words above mix the two): [first publication] D. Pomerleau. ALVINN: An Autonomous Land Vehicle in a Neural Network. In Advances in Neural Information Processing Systems, 1989. [real world driving results] D. Pomerleau. Efficient Training of Artificial Neural Networks for Autonomous Navigation. Neural Compuation, 3(1), 88-97, 1991.

Author(s): Sebastian Thrun, Wolfram Burgard, Dieter Fox
Venue: Book
Year Published: 2005
Keywords: probabilistic models
Expert Opinion: Probabilistic Robotics is a tour de force, replete with material for students and practitioners alike.

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 paper lead a generation of PhD students to reimagine how grasping, and manipulation more generally, could be approached as a machine learning problem. Treating the grasp learning problem as a supervised learning problem without explicit human demonstrations or reinforcement learning, Saxena and colleagues' work stood as an example of how manipulation could be approached from a perceptual angle. A decade before deep learning made a splash in robotics, this work showed how robots could be trained to manipulate previously unseen objects without a need for complete 3D or dynamics models. While the learning techniques and features may have changed, the general formulation still stands as the initial approach many researchers take when implementing a grasp planning algorithm.

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 paper is one of the first impressive applications of policy gradient algorithms on real robots. The policy gradient algorithm is rather simple, but is able to optimize the gait of the AIBO robot efficiently.

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.

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 work contributes to the general question of obtaining life-long learning robotic systems. Large body of the existing robot learning literature mostly focus on methods that enable the robots to learn particular pre-defined skills and achieve particular tasks. Life-long learning, on the other hand, requires the robots to learn skills and adapt to situations that were not (and cannot be) foreseen. Inspired from human development, intrinsic motivation is an important drive that guides the robots towards regions that can be most effectively and efficiently learned with the capabilities developed so far; exploiting metrics such as novelty, curiosity, diversity, etc. This paper, in particular, is a seminal study that exploits maximization of learning progress in a real robot that explores its continuous sensorimotor space. It nicely shows that the robot exhibits stage-like development, learning easy tasks first, and focusing to more complex problems later; progressively developing more advanced skills.

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: Dynamic Movement Primitives (DMPs) specify a way to model goal-directed behaviours as non-linear dynamical system with a learnable attractor behaviour. In this way, the movement trajectory can be of almost arbitrary complexity but remains well-behaved and stable. DMPs are interesting for robot learning as they provide a simple way to learn from demonstrations. The forcing term that shapes the movement trajectory is linear in a set of learnable weights. Any function approximator can be used to learn these parameters. Locally weighted regression has been of particular interest as it is a very easy one-shot learning procedure.

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 work provides a novel and useful approach to the problem of inverse reinforcement learning. It is commonly used in practice and has influenced many follow up works in modeling humans in human-robot interaction.

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: First work that proproses practical movement primitive representation for robotics. Very concise paper: shows how much can be packed into 6 pages.

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.

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: There exists a representational gap between the continuous sensorimotor world of a robot and the discrete symbols used by advanced AI planning methods. Many existing studies typically assume the existence of precoded planning symbols, and investigate how to learn the relations between these pre-coded symbols and continuous world of the robot. Few others argue that symbols should be formed in relation to the experience of agents through their sensorimotor experience. This paper presents a structured approach, which is built on Markov-decision process formalism, to discover symbolic abstract representations from low-level high-dimensional continuous sensorimotor experience. The learned symbols and rules can automatically and effectively expressed in PDDL, a canonical high-level planning domain language, enabling high-level planning with traditional off-the-shelf AI planners.

Author(s): Pieter Abbeel, Andrew Y. Ng
Venue: International Conference on Machine Learning
Year Published: 2004
Keywords: reinforcement learning, learning from demonstration
Expert Opinion: Key paper on inverse reinforcement learning.

Author(s): Lerrel Pinto, Abhinav Gupta
Venue: IEEE International Conference on Robotics and Automation (ICRA)
Year Published: 2015
Keywords: manipulation, reinforcement learning, neural networks
Expert Opinion: It demonstrates how you can scale end-to-end robot learning of grasping by using self-supervision without human involvement.

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: This paper presents a beautiful and compelling demonstration of the strength of learning dynamical models and using optimal control to learn complex tasks on intrinsically unstable systems even if the learned models rather crude and the optimal controllers are based on linearization, both strong approximations of reality. Furthermore, it addresses the problem of learning from demonstrations and improving from such demonstrations to beat human performance. To the best of my knowledge, on of the first paper demonstrating the use of learning by demonstration, model learning and optimal control together to achieve acrobatic tasks.