A three-stage grasping-picking-stacking curriculum with a redesigned reward reduces training time by about 40 percent and improves block-stacking success by roughly 10 percent over direct learning in the CausalWorld simulator.
In the domain of robotic manipulation, machine learning has significantly advanced its capabilities in handling objects and executing complex tasks
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Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation
A three-stage grasping-picking-stacking curriculum with a redesigned reward reduces training time by about 40 percent and improves block-stacking success by roughly 10 percent over direct learning in the CausalWorld simulator.