How can we imbue robots with the ability to manipulate objects precisely but also to reason about them in terms of abstract concepts?
Recent works in manipulation have shown that end-to-end networks can learn dexterous skills that require precise spatial reasoning, but these methods often fail to generalize to new goals or quickly learn transferable concepts across tasks. In parallel, there has been great progress in learning generalizable semantic representations for vision and language by training on large-scale internet data, however these representations lack the spatial understanding necessary for fine-grained manipulation. To this end, we propose a framework that combines the best of both worlds: a two-stream architecture with semantic and spatial pathways for vision-based manipulation. Specifically, we present CLIPort , a language-conditioned imitation-learning agent that combines the broad semantic understanding (what) of CLIP with the spatial precision (where) of TransporterNets.
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.