Comparing Robot Grasping Teleoperation Across Desktop and Virtual Reality with ROS Reality

Whitney, David; Rosen, Eric; Phillips, Elizabeth; Konidaris, George; Tellex, Stefanie

doi:10.1007/978-3-030-28619-4_28

Cited by 88 publications

(56 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of Virtual and Augmented Reality to HRI is a powerful concept which is gaining more and more interest [16] in the last years. In this field, successful utilizations of VAR have been proven in particular for applications related to human-robot collaboration [17], teleoperation [18], [19] and robot skill acquisition [20]. Besides HRI, VAR platforms have been historically adopted in education [21], [22], training [23], [24], aviation [25] with important benefits in terms of performance [24], users' involvement and motivation [21] and transfer to real settings [25].…”

Section: B Virtual and Augmented Realitymentioning

confidence: 99%

On-Road Evaluation of Autonomous Driving Training

Sportillo¹,

Paljic²,

Ojeda³

2019

2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

View full text Add to dashboard Cite

Driver interaction with increasingly automated vehicles requires prior knowledge of system capabilities, operational know-how to use novel car equipment and responsiveness to unpredictable situations. With the purpose of getting drivers ready for autonomous driving, in a between-subject study sixty inexperienced participants were trained with an on-board video tutorial, an Augmented Reality (AR) program and a Virtual Reality (VR) simulator. To evaluate the transfer of training to real driving scenarios, a test drive on public roads was conducted implementing, for the first time in these conditions, the Wizard of Oz (WoZ) protocol. Results suggest that VR and AR training can foster knowledge acquisition and improve reaction time performance in take-over requests. Moreover, participants' behavior during the test drive highlights the ecological validity of the experiment thanks to the effective implementation of the WoZ methodology.

show abstract

Section: B Virtual and Augmented Realitymentioning

confidence: 99%

On-Road Evaluation of Autonomous Driving Training

Sportillo¹,

Paljic²,

Ojeda³

2019

2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

View full text Add to dashboard Cite

show abstract

“…A comparison of various control interfaces shows that general purpose hardware is deficient while special purpose hardware is more accurate but is not widely available [19,26]. Virtual reality-based free-space controllers have recently been proposed both for data collection [23,39] and policy learning [40,43]. While these methods have shown the utility of data, they do not provide a seamlessly scalable data collection mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…While these methods have shown the utility of data, they do not provide a seamlessly scalable data collection mechanism. Often the data is either collected locally or requires a powerful local client computer, to render the high definition sensor stream to a VR headset [39,43]. The use of VR hardware and requirement of client-side compute resources has limited the deployment of these interfaces on crowdsourcing platforms.…”

Section: Related Workmentioning

confidence: 99%

Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity

Mandlekar

Booher

Spero

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

Large, richly annotated datasets have accelerated progress in fields such as computer vision and natural language processing, but replicating these successes in robotics has been challenging. While prior data collection methodologies such as self-supervision have resulted in large datasets, the data can have poor signal-to-noise ratio. By contrast, previous efforts to collect task demonstrations with humans provide better quality data, but they cannot reach the same data magnitude. Furthermore, neither approach places guarantees on the diversity of the data collected, in terms of solution strategies. In this work, we leverage and extend the RoboTurk platform to scale up data collection for robotic manipulation using remote teleoperation. The primary motivation for our platform is two-fold: (1) to address the shortcomings of prior work and increase the total quantity of manipulation data collected through human supervision by an order of magnitude without sacrificing the quality of the data and (2) to collect data on challenging manipulation tasks across several operators and observe a diverse set of emergent behaviors and solutions. We collected over 111 hours of robot manipulation data across 54 users and 3 challenging manipulation tasks in 1 week, resulting in the largest robot dataset collected via remote teleoperation. We evaluate the quality of our platform, the diversity of demonstrations in our dataset, and the utility of our dataset via quantitative and qualitative analysis. For additional results, supplementary videos, and to download our dataset, visit roboturk.stanford.edu/realrobotdataset

show abstract

“…It has been previously shown that non-expert users are more efficient with a VR teleoperation interface, and that they prefer using one, compared to a keyboard and monitor interface [6].…”

Section: Related Workmentioning

confidence: 99%

Telerobotic Control in Virtual Reality

Gharaybeh

Chizeck

Stewart

2019

Oceans 2019 MTS/Ieee Seattle

View full text Add to dashboard Cite

and the Navy have identified more than 400 underwater sites potentially contaminated with munitions [22]. These munitions were left as a result of past military activities. They are an explosive hazard and their contents pose an environmental risk. An effective ROS (Robot Operating System) package that interfaces with Simulink/Matlab was developed to teleoperate an underwater robotic system in Virtual Reality for the remediation of these munitions. The key advantage of this VR teleoperation method is more effective and intuitive control in comparison to teleoperation methods that utilize a 2D monitor and joystick/keyboard/manipulandum input device. The ROS package was built to work with the Oculus DK2 VR headset and the Razer TM Hydra hand controllers.

show abstract

Comparing Robot Grasping Teleoperation Across Desktop and Virtual Reality with ROS Reality

Cited by 88 publications

References 7 publications

On-Road Evaluation of Autonomous Driving Training

On-Road Evaluation of Autonomous Driving Training

Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity

Telerobotic Control in Virtual Reality

Contact Info

Product

Resources

About