Transferable Task Execution from Pixels through Deep Planning Domain Learning
Kei Kase,Chris Paxton,Hammad Mazhar,Tetsuya Ogata,Dieter Fox,Kei Kase,Chris Paxton,Hammad Mazhar,Tetsuya Ogata,Dieter Fox
While robots can learn models to solve many manipulation tasks from raw visual input, they cannot usually use these models to solve new problems. On the other hand, symbolic planning methods such as STRIPS have long been able to solve new problems given only a domain definition and a symbolic goal, but these approaches often struggle on the real world robotic tasks due to the challenges of groundi...
