Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration

Silver, David; Bagnell, J. Andrew; Stentz, Anthony

doi:10.1007/978-3-319-00065-7_26

Cited by 44 publications

(25 citation statements)

References 19 publications

(29 reference statements)

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“…These methods, typified by , , Ratliff et al [2009] can use arbitrary supervised learning algorithms in an ensemble to create highly non-linear cost functions. This approach has been used to learn locomotion strategies by demonstration Zucker et al [2011] as well as to learn to match the real-world, rough, terrain driving strategies Silver et al [ , 2013. These are among the easiest and most general approaches to implement, and an example of their use is discussed in 4.8.2.…”

Section: Boosting Methodsmentioning

confidence: 99%

An Algorithmic Perspective on Imitation Learning

et al. 2018

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As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning. This work provides an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms developed to tackle the problem; and advice on effective tools and implementation.We intend this paper to serve two audiences. First, we want to familiarize machine learning experts with the challenges of imitation learning, particularly those arising in robotics, and the interesting theoretical and practical distinctions between it and more familiar frameworks like statistical supervised learning theory and reinforcement learning. Second, we want to give roboticists and experts in applied artificial intelligence a broader appreciation for the frameworks and tools available for imitation learning.We organize our work by dividing imitation learning into directly replicating desired behavior (sometimes called behavioral cloning [Bain and Sammut, 1996]) and learning the hidden objectives of the desired behavior from demonstrations (called inverse optimal control [Kalman, 1964] or inverse reinforcement learning [Russell, 1998]). In addition to method analysis, we discuss the design decisions a practitioner must make when selecting an imitation learning approach. Moreover, application examples-such as robots that play table tennis [Kober and Peters, 2009] and programs that play the game of Go [Silver et al., 2016]-illustrate the properties and motivations behind different forms of imitation learning. We conclude by presenting a set of open questions and point towards possible future research directions.

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Section: Boosting Methodsmentioning

confidence: 99%

An Algorithmic Perspective on Imitation Learning

et al. 2018

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“…The behavioral models are learned by maximum-entropy IRL from demonstrations of different social acceptabilities. A similar variant, maximum margin planning (117), was applied to navigate a robot in complex unstructured terrain (118) and to learn autonomous driving styles and maneuvers (119).…”

Section: Wwwannualreviewsorg • Decision-making For Autonomousmentioning

confidence: 99%

Planning and Decision-Making for Autonomous Vehicles

Schwarting

Alonso–Mora

Rus

2018

Annu. Rev. Control Robot. Auton. Syst.

737

378

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In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and systemcompliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, such as interactive planning and end-to-end learning, open up questions regarding safety and reliability that need to be addressed. In this survey, we emphasize recent approaches for integrated perception and planning and for behavior-aware planning, many of which rely on machine learning. This raises the question of verification and safety, which we also touch upon. Finally, we discuss the state of the art and remaining challenges for managing fleets of autonomous vehicles. 8.1

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“…Silver, Bagnell, and Stentz [14] learn cost functions to indicate preference for terrain and maneuvers for mobile robot navigation. Cost functions do not explicitly restrict the configuration space and mix quantities to optimize (e.g.…”

Section: Related Workmentioning

confidence: 99%

A natural language planner interface for mobile manipulators

Howard

Tellex

Roy

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Natural language interfaces for robot control aspire to find the best sequence of actions that reflect the behavior intended by the instruction. This is difficult because of the diversity of language, variety of environments, and heterogeneity of tasks. Previous work has demonstrated that probabilistic graphical models constructed from the parse structure of natural language can be used to identify motions that most closely resemble verb phrases. Such approaches however quickly succumb to computational bottlenecks imposed by construction and search the space of possible actions. Planning constraints, which define goal regions and separate the admissible and inadmissible states in an environment model, provide an interesting alternative to represent the meaning of verb phrases. In this paper we present a new model called the Distributed Correspondence Graph (DCG) to infer the most likely set of planning constraints from natural language instructions. A trajectory planner then uses these planning constraints to find a sequence of actions that resemble the instruction. Separating the problem of identifying the action encoded by the language into individual steps of planning constraint inference and motion planning enables us to avoid computational costs associated with generation and evaluation of many trajectories. We present experimental results from comparative experiments that demonstrate improvements in efficiency in natural language understanding without loss of accuracy.

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Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration

Cited by 44 publications

References 19 publications

An Algorithmic Perspective on Imitation Learning

An Algorithmic Perspective on Imitation Learning

Planning and Decision-Making for Autonomous Vehicles

A natural language planner interface for mobile manipulators

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