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.
Reinforcement learning is one of the solutions, allowing agents to understand and optimize the process through continuous interaction with their environment [14-16]
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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.