Do You Want Your Autonomous Car To Drive Like You?

Basu, Chandrayee; Yang, Qian; Hungerman, David; Sinahal, Mukesh; Draqan, Anca D.

doi:10.1145/2909824.3020250

Cited by 105 publications

(86 citation statements)

References 22 publications

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“…Recent work found that people do not actually want their autonomous cars to drive as aggressively as they do [6]. In such cases, the reward function learned by IRL will not encode the humans' desired behavior.…”

Section: Background On Reward Learningmentioning

confidence: 99%

“…Note that this pair of trajectories is clearly querying the user for whether she wants the robot arm to move towards the goal or away from the goal. Additionally, note the jaggedness of the trajectory: this is due to the highly non-convex nature of the optimization problem (6). 3) RolloutDemPref.mov: This video shows a sample trajectory generated by PPO, according to the reward function learned by DemPref (from a specific user).…”

Section: Appendixmentioning

confidence: 99%

See 1 more Smart Citation

Learning Reward Functions by Integrating Human Demonstrations and Preferences

Palan¹,

Shevchuk²,

Landolfi³

et al. 2019

Robotics: Science and Systems XV

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Our goal is to accurately and efficiently learn reward functions for autonomous robots. Current approaches to this problem include inverse reinforcement learning (IRL), which uses expert demonstrations, and preference-based learning, which iteratively queries the user for her preferences between trajectories. In robotics however, IRL often struggles because it is difficult to get high-quality demonstrations; conversely, preference-based learning is very inefficient since it attempts to learn a continuous, high-dimensional function from binary feedback. We propose a new framework for reward learning, DemPref, that uses both demonstrations and preference queries to learn a reward function. Specifically, we (1) use the demonstrations to learn a coarse prior over the space of reward functions, to reduce the effective size of the space from which queries are generated; and (2) use the demonstrations to ground the (active) query generation process, to improve the quality of the generated queries. Our method alleviates the efficiency issues faced by standard preference-based learning methods and does not exclusively depend on (possibly low-quality) demonstrations. In numerical experiments, we find that DemPref is significantly more efficient than a standard active preference-based learning method. In a user study, we compare our method to a standard IRL method; we find that users rated the robot trained with DemPref as being more successful at learning their desired behavior, and preferred to use the DemPref system (over IRL) to train the robot.

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Section: Background On Reward Learningmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

Learning Reward Functions by Integrating Human Demonstrations and Preferences

Palan¹,

Shevchuk²,

Landolfi³

et al. 2019

Robotics: Science and Systems XV

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show abstract

“…For many systems, end-users have difficulty providing demonstrations of what they want. For instance, they cannot coordinate 7 degrees of freedom (DOFs) at a time [2], and they can only show the car how they drive, not how they want the car to drive [5]. In such cases, another option is for the system to regress a reward function from labeled state-action pairs, but assigning precise numeric reward values to observed robot actions is also difficult.…”

Section: Introductionmentioning

confidence: 99%

Active Preference-Based Learning of Reward Functions

Sadigh

Dragan

Sastry

et al. 2017

Robotics: Science and Systems XIII

202

303

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Abstract-Our goal is to efficiently learn reward functions encoding a human's preferences for how a dynamical system should act. There are two challenges with this. First, in many problems it is difficult for people to provide demonstrations of the desired system trajectory (like a high-DOF robot arm motion or an aggressive driving maneuver), or to even assign how much numerical reward an action or trajectory should get. We build on work in label ranking and propose to learn from preferences (or comparisons) instead: the person provides the system a relative preference between two trajectories. Second, the learned reward function strongly depends on what environments and trajectories were experienced during the training phase. We thus take an active learning approach, in which the system decides on what preference queries to make. A novel aspect of our work is the complexity and continuous nature of the queries: continuous trajectories of a dynamical system in environments with other moving agents (humans or robots). We contribute a method for actively synthesizing queries that satisfy the dynamics of the system. Further, we learn the reward function from a continuous hypothesis space by maximizing the volume removed from the hypothesis space by each query. We assign weights to the hypothesis space in the form of a log-concave distribution and provide a bound on the number of iterations required to converge. We show that our algorithm converges faster to the desired reward compared to approaches that are not active or that do not synthesize queries in an autonomous driving domain. We then run a user study to put our method to the test with real people.

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“…The Social robotics field has proposed a great amount of work on human-aware navigation, or socially-aware navigation that integrates social spaces in the navigation decision process [16,17,18,19]. The question of human acceptance of autonomous cars behavior starts to be seen in the field of autonomous cars [20,21,22].…”

Section: Comfort In Autonomous Carsmentioning

confidence: 99%

Personal Space of Autonomous Car’s Passengers Sitting in the Driver’s Seat

Ferrier-Barbut

Vaufreydaz

David

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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This article deals with the specific context of an autonomous car navigating in an urban center within a shared space between pedestrians and cars. The driver delegates the control to the autonomous system while remaining seated in the driver's seat. The proposed study aims at giving a first insight into the definition of human perception of space applied to vehicles by testing the existence of a personal space around the car. It aims at measuring proxemic information about the driver's comfort zone in such conditions. Proxemics, or human perception of space, has been largely explored when applied to humans or to robots, leading to the concept of personal space, but poorly when applied to vehicles. In this article, we highlight the existence and the characteristics of a zone of comfort around the car which is not correlated to the risk of a collision between the car and other road users. Our experiment includes 19 volunteers using a virtual reality headset to look at 30 scenarios filmed in 360°from the point of view of a passenger sitting in the driver's seat of an autonomous car. They were asked to say "stop" when they felt discomfort visualizing the scenarios.As said, the scenarios voluntarily avoid collision effect as we do not want to measure fear but discomfort. The scenarios involve one or three pedestrians walking past the car at different distances from the wings of the car, relative to the direction of motion of the car, on both sides. The car is either static or moving straight forward at different speeds. The results indicate the existence of a comfort zone around the car in which intrusion causes discomfort. The size of the comfort zone is sensitive neither to the side of the car where the pedestrian passes nor to the number of pedestrians. In contrast, the feeling of discomfort is relative to the car's motion (static or moving). Another outcome from this study is an illustration of the usage of first person 360°video and a virtual reality headset to evaluate feelings of a passenger within an autonomous car.

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Do You Want Your Autonomous Car To Drive Like You?

Cited by 105 publications

References 22 publications

Learning Reward Functions by Integrating Human Demonstrations and Preferences

Learning Reward Functions by Integrating Human Demonstrations and Preferences

Active Preference-Based Learning of Reward Functions

Personal Space of Autonomous Car’s Passengers Sitting in the Driver’s Seat

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