Large-scale cost function learning for path planning using deep inverse reinforcement learning

Wulfmeier, Markus; Rao, Dushyant; Wang, Dominic Zeng; Ondrúška, Peter; Posner, Ingmar

doi:10.1177/0278364917722396

Cited by 161 publications

(99 citation statements)

References 25 publications

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“…Additionally, thanks to convolutional operators, they are able to capture spatial correlations in the data. Wulfmeier et al (123) were able to learn an end-to-end mapping from raw input data to cost map from more than 25,000 demonstrations over 120 km of driving. Lastly, Kuefler et al (124) demonstrated the effectiveness of generative adversarial imitation learning (125), extended to the optimization of recurrent policies.…”

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.

712

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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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.

712

378

View full text Add to dashboard Cite

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“…The system was shown to learn drivers' personal driving styles from minimal training data and performed adequately in simulated testing. Building on the IRL approaches, Wulfmeier et al [133] proposed an IRL approach for deep learning. The proposed algorithm is based on the Maximum Entropy [134] model for a trajectory planner, and uses CNNs to infer the reward functions from expert demonstration.…”

Section: Simultaneous Lateral and Longitudinal Control Systemsmentioning

confidence: 99%

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Kuutti

Bowden

Jin

et al. 2021

IEEE Trans. Intell. Transport. Syst.

457

182

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Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.

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“…The proposed method is demonstrated on a task of learning driver route choices where the demonstrations may be suboptimal and non-deterministic. This approach is extended to a deep-learning framework in [Wulfmeier et al 2015]. Maximum entropy objective functions enable straightforward learning of the network weights, and thus the use of deep networks trained with stochastic gradient descent [Wulfmeier et al 2015].…”

Section: Apprenticeship Learningmentioning

confidence: 99%

“…This approach is extended to a deep-learning framework in [Wulfmeier et al 2015]. Maximum entropy objective functions enable straightforward learning of the network weights, and thus the use of deep networks trained with stochastic gradient descent [Wulfmeier et al 2015]. The deep architecture is further extended to learn the features via Convolution layers instead of using pre-extracted features.…”

Section: Apprenticeship Learningmentioning

confidence: 99%

Imitation Learning

et al. 2017

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Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions.

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Large-scale cost function learning for path planning using deep inverse reinforcement learning

Cited by 161 publications

References 25 publications

Planning and Decision-Making for Autonomous Vehicles

Planning and Decision-Making for Autonomous Vehicles

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Imitation Learning

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