A robot can learn to pick and place an object using grasp poses that are feasible in both the pick pose and the place pose, by summing learned feasibility scores for the two poses.
Enabling grasp synthesis approaches to task-oriented grasping considering the end-state comfort and confidence effects,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Learning from Planned Data to Improve Robotic Pick-and-Place Planning Efficiency
A robot can learn to pick and place an object using grasp poses that are feasible in both the pick pose and the place pose, by summing learned feasibility scores for the two poses.