OpenRAVE: A Planning Architecture for Autonomous Robotics

By Rosen Diankov, James J. Kuffner Jr.. 2008

492 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c28d3dc33b629916a306cc58cbff05dcd632d42d

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Citations in the corpus

A case study of trajectory transfer through non-rigid registration for a simplified suturing scenario (2013, methodology): “We used OpenRAVE [3] to load the model of the robot and do kinematics calculations, and we used a custom simulation environment based on the Bullet Physics Library [2].”
UniSim: A Neural Closed-Loop Sensor Simulator (2023, background): “Simulation Environments for Robotics: The robotics community has a long history of building simulators for safer and faster robot development [15, 31, 37, 49, 77, 88].”
Human preferences for robot-human hand-over configurations (2011, methodology): “OpenRAVE [6] is used for simulating kinematics of the robot and the human and for grasp planning.”
Hobbit, a care robot supporting independent living at home: First prototype and lessons learned (2016, methodology): “Path planning for the robot arm including obstacle avoidance is performed with the simulation environment OpenRAVE [39].”
The YCB object and Model set: Towards common benchmarks for manipulation research (2015, methodology): “Integration with the OpenRAVE [29] simulation package is similarly straight-forward where we link to the display and collision meshes from a KinBody XML file.”
Hallucinating Humans for Learning Robotic Placement of Objects (2012, unknown): “Only if not, a path planning algorithm (in our case, rBiRRT in OpenRAVE [41]) is then used to verify the validity of placing at O i and the search continues for other objects.”
OpenGRASP: A Toolkit for Robot Grasping Simulation (2010, methodology): “Another existing and publicly available software framework is OpenRAVE, the Open Robotics and Animation Virtual Environment [10].”
Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set (2015, methodology): “Integration with the OpenRAVE [57] simulation package is similarly straightforward where we link to the display and collision meshes from a KinBody XML file.”
HERB: a home exploring robotic butler (2010, methodology): “Finally all sensing information is combined inside the OpenRAVE environment (Diankov and Kuffner 2008) to form the entire world state of the system.”
Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols (2015, methodology): “Integration with the OpenRAVE [56] simulation package is similarly straightforward where we link to the display and collision meshes from a KinBody XML file.”
Learning of grasp selection based on shape-templates (2013, background): “(Miller and Allen 2004), Open-Rave (Diankov and Kuffner 2008) or OpenGRASP (Leon et al. 2010; Ulbrich et al. 2011) that can execute grasps given perfect knowledge of the object and its environment.”
Learning a visuomotor controller for real world robotic grasping using simulated depth images (2017, methodology): “IKFast [6] is used to convert the selected action (i.”
CommonRoad: Composable benchmarks for motion planning on roads (2017, methodology): “Earlier libraries for sampling-based motion planning are the Online, Open-source, Programming System for Motion Planning (OOPS MP ) [20] and the Open Robotics and Animation Virtual Environment (OpenRAVE) [21] with similar goals as OMPL.”
Learning from Demonstrations Through the Use of Non-rigid Registration (2013, methodology): “We would like to acknowledge the following open-source software that we used: OpenRAVE [22], ROS [23], PCL [24], OpenCV [25].”
Analysis and Observations From the First Amazon Picking Challenge (2016, methodology): “[19] and OpenRave [21] provide.”
Making planned paths look more human-like in humanoid robot manipulation planning (2011, methodology): “For path planning, the BiRRT planner [18] implementation in OpenRAVE [ 19 ], an open source planning architecture, is used.”
Robot Collisions: A Survey on Detection, Isolation, and Identification (2017, background): “Offline motion planning techniques are computationally expensive, and their efficient conversion to online methods capable of handling instantaneous changes is still on-going research [8], [9], in particular when considering human-aware situations [10], [11].”
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World (2020, methodology): “Widely used robotic simulators, such as Gazebo and OpenRave [22, 7], also support sensory simulation through physics and graphics engines.”
Learning to place new objects in a scene (2012, methodology): “We use open-source softwares such as ROS (Quigley et al., 2009) and OpenRAVE (Diankov and Kuffner, 2008) for path planning and obstacle avoidance, and use AR-markers (Ferguson, 2011) for robot localization.”