Planning human-aware motions using a sampling-based costmap planner

By Jim Mainprice, Emrah Akin Sisbot, Leonard Jaillet, Juan Cortés, Rachid Alami, Thierry Siméon. 2011

126 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering, Reinforcement Learning

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Citations in the corpus

Learning Trajectory Preferences for Manipulators via Iterative Improvement (2013, background): “[23] planned trajectories satisfying user specified preferences in form of constraints on the distance of robot from user, the visibility of robot and the user arm comfort.”
An implemented theory of mind to improve human-robot shared plans execution (2016, background): “Human activities can be fairly well monitored ([16], [17]), robot actions can be executed taking into account the humans in the environment ([18], [19]) and human-aware plans can be computed ([2], [3]).”
Initiative in robot assistance during collaborative task execution (2016, background): “Another vein of research focuses on low-level motion planning for the robot within a collaborative context [5], [8], [9], [10], with an eye towards improving team fluency and the user’s sense of safety.”
Artificial cognition for social human-robot interaction: An implementation (2017, methodology): “We briefly mention our human-aware motion and manipulation planner [91,105,92,97,93,95], and finally present two execution controller, the PRS-based Shary [83,89,2] and the event-driven pyRobots [85].”
Symbiotic human-robot collaborative assembly (2019, methodology): “Among offline methodologies, Mainprice et al. [147] worked on the automatic generation of robot paths when the robot has to hand an object over to a human.”
Sharing effort in planning human-robot handover tasks (2012, background): “They have also been used in [13] to generate comfortable handover motions in cluttered environments.”
Learning preferences for manipulation tasks from online coactive feedback (2015, background): “[37] planned trajectories satisfying user speciﬁed preferences in form of constraints on the distance of the robot from the user, the visibility of the robot and the user’s arm comfort.”
A Survey of Methods for Safe Human-Robot Interaction (2017, background): “A motion planner developed by Mainprice et al. (2011) considers similar HRI constraints — specifically, human vision field, separation distance, and reachability — to drive a cost-based, random-sampling search in order to plan safe robot motions within cluttered environments. This planner incorporat”
Human-robot collaborative manipulation planning using early prediction of human motion (2013, background): “Recently this approach was extended to combine planning and more reactive schemes [11], allowing more natural crossings between the robot and the human.”
Shared autonomy via hindsight optimization for teleoperation and teaming (2017, background): “For static users, researchers have incorporated collaboration constraints such as safety and social acceptability (Sisbot et al., 2007), and task constraints such as user visibility and reachability (Sisbot et al., 2010; Pandey and Alami, 2010; Mainprice et al., 2011).”