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Thrun, Sebastian; Montemerlo, Michael; Dahlkamp, Hendrik; Stavens, David; Aron, Andrei; Diebel, James; Fong, Philip W. L.; Gale, John T.; Halpenny, Morgan; Hoffmann, Gabriel; Lau, Kenny; Oakley, Celia M.; Palatucci, Mark; Pratt, Vaughan R.; Stang, Pascal; Strohband, Sven; Dupont, Cedric; Jendrossek, Lars-Erik; Koelen, Christian; Markey, Charles; Rummel, Carlo; Niekerk, Joe van; Jensen, Eric L. N.; Alessandrini, Philippe; Davies, Bob; Ettinger, Scott; Kaehler, Adrian; Nefian, Ara; Mahoney, Pamela

doi:10.1002/rob.v23:9

Cited by 46 publications

(3 citation statements)

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“…DARPA has historically used competitions to promote research and development in the field of robotics. During 2004, 2005, and 2007, DARPA funded the Grand Challenge [5] and Urban Challenge [6] projects to advance autonomous driving capabilities. The DRC/VRC marks the first time that a simulation-based competition was included in a DARPA challenge.…”

Section: Related Workmentioning

confidence: 99%

Inside the Virtual Robotics Challenge: Simulating Real-Time Robotic Disaster Response

Agüero

Koenig

Chen

et al. 2015

IEEE Trans. Automat. Sci. Eng.

125

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This paper presents the software framework established to facilitate cloud-hosted robot simulation. The framework addresses the challenges associated with conducting a task-oriented and real-time robot competition, the Defense Advanced Research Projects Agency (DARPA) Virtual Robotics Challenge (VRC), designed to mimic reality. The core of the framework is the Gazebo simulator, a platform to simulate robots, objects, and environments, as well as the enhancements made for the VRC to maintain a high fidelity simulation using a high degree of freedom and multisensor robot. The other major component used is the CloudSim tool, designed to enhance the automation of robotics simulation using existing cloud technologies. The results from the VRC and a discussion are also detailed in this work. Note to Practitioners-Advances in robot simulation, cloud hosted infrastructure, and web technology have made it possible to accurately and efficiently simulate complex robots and environments on remote servers while providing realistic data streams for human-in-the-loop robot control. This paper presents the software and hardware frameworks established to facilitate cloud-hosted robot simulation, and addresses the challenges associated with conducting a task-oriented robot competition designed to mimic reality. The competition that spurred this innovation was the VRC, a precursor to the DARPA Robotics Challenge, in which teams from around the world utilized custom human-robot interfaces and control code to solve disaster response-related tasks in simulation. Winners of the VRC received both funding and access to Atlas, a humanoid robot developed by Boston Dynamics. The Gazebo simulator, an open source and high fidelity robot simulator, was improved upon to met the needs of the VRC competition. Additionally, CloudSim was created to act as an interface between users and the cloud-hosted simulations.

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Section: Related Workmentioning

confidence: 99%

Inside the Virtual Robotics Challenge: Simulating Real-Time Robotic Disaster Response

Agüero

Koenig

Chen

et al. 2015

IEEE Trans. Automat. Sci. Eng.

125

View full text Add to dashboard Cite

show abstract

“…They show an astonishing performance: especially advances in statistical inferences (Doucet et al 2001; Grisetti et al 2005; Knill and Richards 1996) combined with geometric modeling of human perception (Field et al 1993; Eric and Grimson 1990; Pentland 1986) and the usage of laser range scanners contributed breakthroughs in robot applications, with spectacular results achieved in the DARPA Grand Challenges (2005 and 2007) where several autonomous vehicles were able to successfully complete the race (Thrun et al 2006). Although sophisticated statistical and geometrical models like Extended Kalman Filters (EKF) (Huang and Dissanayake 2006), Particle Filters (Grisetti et al 2005) and ICP (Iterative Closest Point) (Besl and McKay 1992; Nüchter et al 2005) utilized in mapping approaches show impressive results, their limits are apparently clear in the aforementioned scenarios like search and rescue.…”

Section: Related Work In Spatial Cognition and Mappingmentioning

confidence: 99%

Using virtual scans for improved mapping and evaluation

Lakaemper

Adluru

2009

Auton Robot

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In this paper we present a system to enhance the performance of feature correspondence based alignment algorithms for laser scan data. We show how this system can be utilized as a new approach for evaluation of mapping algorithms. Assuming a certain a priori knowledge, our system augments the sensor data with hypotheses (‘Virtual Scans’) about ideal models of objects in the robot’s environment. These hypotheses are generated by analysis of the current aligned map estimated by an underlying iterative alignment algorithm. The augmented data is used to improve the alignment process. Feedback between data alignment and data analysis confirms, modifies, or discards the Virtual Scans in each iteration. Experiments with a simulated scenario and real world data from a rescue robot scenario show the applicability and advantages of the approach. By replacing the estimated ‘Virtual Scans’ with ground truth maps our system can provide a flexible way for evaluating different mapping algorithms in different settings.

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“…There are two main established paradigms for solving the problem of autonomous driving: a traditional engineeringbased approach and a data-driven, machine-learning approach. The former performs well in structured driving environments, such as highways or modern suburban developments, where explicitly addressing different scenarios is tractable and sufficient for human-like driving [1], [2], [3]. This approach is more mature, and the focus of commercial efforts today.…”

Section: Introductionmentioning

confidence: 99%

Urban Driving with Conditional Imitation Learning

Hawke¹,

Shen²,

Gurău³

et al. 2019

Preprint

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Hand-crafting generalised decision-making rules for real-world urban autonomous driving is hard. Alternatively, learning behaviour from easy-to-collect human driving demonstrations is appealing. Prior work has studied imitation learning (IL) for autonomous driving with a number of limitations. Examples include only performing lane-following rather than following a user-defined route, only using a single camera view or heavily cropped frames lacking state observability, only lateral (steering) control, but not longitudinal (speed) control and a lack of interaction with traffic. Importantly, the majority of such systems have been primarily evaluated in simulation -a simple domain, which lacks real-world complexities. Motivated by these challenges, we focus on learning representations of semantics, geometry and motion with computer vision for IL from human driving demonstrations. As our main contribution, we present an end-to-end conditional imitation learning approach, combining both lateral and longitudinal control on a real vehicle for following urban routes with simple traffic. We address inherent dataset bias by data balancing, training our final policy on approximately 30 hours of demonstrations gathered over six months. We evaluate our method on an autonomous vehicle by driving 35km of novel routes in European urban streets.

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Cited by 46 publications

References 0 publications

Inside the Virtual Robotics Challenge: Simulating Real-Time Robotic Disaster Response

Inside the Virtual Robotics Challenge: Simulating Real-Time Robotic Disaster Response

Using virtual scans for improved mapping and evaluation

Urban Driving with Conditional Imitation Learning

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