Strategies for human-in-the-loop robotic grasping

By A. Leeper, Kaijen Hsiao, Matei T. Ciocarlie, Leila Takayama, David Gossow. 2012

196 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Social Psychology

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In Social Psychology

Citations in the corpus

ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation (2018, methodology): “Teleoperated supervision can be provided through contemporary game interfaces such as a keyboard and mouse [26, 27], a video game controller [28], a 3D-mouse [29, 30], special purpose master-slave interfaces[31, 32], or through free-space positioning interfaces such as virtual reality (VR) [33, 34,…”
Robots for humanity: using assistive robotics to empower people with disabilities (2013, methodology): “Henry has also used Interactive Manipulation to perform the user study done by able-bodied users in [12], involving grasping objects from a highly cluttered shelf.”
A Comparison of Remote Robot Teleoperation Interfaces for General Object Manipulation (2017, methodology): “We use the standard ring-and-arrow marker and base the approach on the Grasp Execution strategy described by Leeper et al. [13], rendered in a 3D viewer.”
A policy-blending formalism for shared control (2013, background): “In what follows, Q denotes the robot’s current configuration, U denotes the desired next configuration obtained via the interface that maps the user’s direct input through a device onto the robot (e.g. through a GUI, joystick, or a wholebody teleoperation interface, as in Figure 1, or by sending a v”
Shared autonomy via hindsight optimization for teleoperation and teaming (2017, background): “Point-and-click interfaces have been used for object grasping with varying levels of autonomy (Leeper et al., 2012).”
Online Object and Task Learning via Human Robot Interaction (2018, background): “guidance whenever the robot is not able to make a decision on its own [1].”
A Survey of Research on Cloud Robotics and Automation (2015, background): “11). research and many in industry, with the ROS Industrial project created to support these users [17].”
Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor (2014, background): “The results indicate humans are able to select better and more robust grasp strategies [103], [164].”