Learning one multi-task policy for 9 real-world tasks including folding cloths, sweeping beans etc. with just 179 image-action training pairs. — “Our end-to-end framework is capable of solving a variety of language-specified tabletop tasks from packing unseen objects to folding cloths, all without any explicit representations of object poses, instance segmentations, memory, symbolic states, or syntactic structures. Experiments in simulation and hardware show that our approach is data-efficient and generalizes effectively to seen and unseen semantic concepts. We even train one multi-task policy for 10 simulated and 9 real-world tasks that shows better or comparable performance to single-task policies.”
Links for 2021-10-01
Links for 2021-10-01
Links for 2021-10-01
Learning one multi-task policy for 9 real-world tasks including folding cloths, sweeping beans etc. with just 179 image-action training pairs. — “Our end-to-end framework is capable of solving a variety of language-specified tabletop tasks from packing unseen objects to folding cloths, all without any explicit representations of object poses, instance segmentations, memory, symbolic states, or syntactic structures. Experiments in simulation and hardware show that our approach is data-efficient and generalizes effectively to seen and unseen semantic concepts. We even train one multi-task policy for 10 simulated and 9 real-world tasks that shows better or comparable performance to single-task policies.”