Inverse Reinforcement Learning algorithms and features for robot navigation in crowds: An experimental comparison

Vasquez, Dizan; Okal, Billy; Arras, Kai O.

doi:10.1109/iros.2014.6942731

Cited by 143 publications

(92 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As mentioned in Sect. 2, Vasquez et al [82] investigated different cost features. They concluded that social forces showed the best results for the learned scenes while being at the same time the ones generalizing worst for unknown scenes.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, Kim and Pineau [36] proposed to use the population density and velocity of the surrounding objects. The effect of the different features in [29] and [36] were investigate by Vasquez et al [82] and compared with social force features [28]. Results showed that the social force features perform best when applied specifically for the learned scene, but seem to generalize worst to other scenes.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Understanding Human Avoidance Behavior: Interaction-Aware Decision Making Based on Game Theory

Turnwald

Althoff

Wollherr

et al. 2016

Int J of Soc Robotics

View full text Add to dashboard Cite

Being aware of mutual influences between individuals is a major requirement a robot to efficiently operate in human populated environments. This is especially true for the navigation among humans with its mutual avoidance maneuvers. While humans easily manage this task, robotic systems are still facing problems. Most of the recent approaches concentrate on predicting the motions of humans individually and deciding afterwards. Thereby, interactivity is mostly neglected. In this work, we go one step back and focus on understanding the underlying principle of human decision making in the presence of multiple humans. Noncooperative game theory is applied to formulate the problem of predicting the decisions of multiple humans that interact which each other during navigation. Therefore, we use the theory of Nash equilibria in static and dynamic games where different cost functions from literature rate the payoffs of the individual humans. The approach anticipates collisions and additionally reasons about several avoidance maneuvers of all humans. For the evaluation of the game theoretic approach we recorded trajectories of humans passing each other. The evaluation shows that game theory is able to reproduce the

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Understanding Human Avoidance Behavior: Interaction-Aware Decision Making Based on Game Theory

Turnwald

Althoff

Wollherr

et al. 2016

Int J of Soc Robotics

View full text Add to dashboard Cite

show abstract

“…Related to ViBe are several existing LfD methods that learn road and pedestrian behaviour [29], [30], [31], [32]. Most relevant is learning highway merging behaviour [33], [34] from NGSIM [35], a publicly available dataset of vehicle trajectories.…”

Section: B Learning From Demonstrationmentioning

confidence: 99%

Learning From Demonstration in the Wild

Behbahani

Shiarlis

Chen

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

Learning from demonstration (LfD) is useful in settings where hand-coding behaviour or a reward function is impractical. It has succeeded in a wide range of problems but typically relies on manually generated demonstrations or specially deployed sensors and has not generally been able to leverage the copious demonstrations available in the wild: those that capture behaviours that were occurring anyway using sensors that were already deployed for another purpose, e.g., traffic camera footage capturing demonstrations of natural behaviour of vehicles, cyclists, and pedestrians. We propose video to behaviour (ViBe), a new approach to learn models of behaviour from unlabelled raw video data of a traffic scene collected from a single, monocular, initially uncalibrated camera with ordinary resolution. Our approach calibrates the camera, detects relevant objects, tracks them through time, and uses the resulting trajectories to perform LfD, yielding models of naturalistic behaviour. We apply ViBe to raw videos of a traffic intersection and show that it can learn purely from videos, without additional expert knowledge.

show abstract

“…This is more robust than policy search, because rewards are better generalizable and more succinct (see Vasquez et al, 2014). We use Bayesian IRL (Michini and How, 2012) to learn a distribution over the rewards and select the best reward as the MAP estimate.…”

Section: Behavior Learning Via Inverse Reinforcement Learningmentioning

confidence: 99%

SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports

Triebel

Arras

Alami

et al. 2016

Springer Tracts in Advanced Robotics

Self Cite

202

124

View full text Add to dashboard Cite

We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in realtime for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.

show abstract

Inverse Reinforcement Learning algorithms and features for robot navigation in crowds: An experimental comparison

Cited by 143 publications

References 13 publications

Understanding Human Avoidance Behavior: Interaction-Aware Decision Making Based on Game Theory

Understanding Human Avoidance Behavior: Interaction-Aware Decision Making Based on Game Theory

Learning From Demonstration in the Wild

SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports

Contact Info

Product

Resources

About