Modular Adaptive Policy Selection for Multi- Task Imitation Learning through Task Division
Dafni Antotsiou,Carlo Ciliberto,Tae–Kyun Kim,Dafni Antotsiou,Carlo Ciliberto,Tae–Kyun Kim
Deep imitation learning requires many expert demonstrations, which can be hard to obtain, especially when many tasks are involved. However, different tasks often share similarities, so learning them jointly can greatly benefit them and alleviate the need for many demonstrations. But, joint multi-task learning often suffers from negative transfer, sharing information that should be task-specific. I...
