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While solving complex manipulation tasks, manipulation policies often need tolearn a set of diverse skills to accomplish these tasks. The set of skills isoften quite multimodal - each one may have a quite distinct distribution ofactions and states. Standard deep policy-learning algorithms often modelpolicies as deep neural networks with a single output head (deterministic orstochastic). This structure requires the network to learn to switch betweenmodes internally, which can lead to lower sample efficiency and poorperformance. In this paper we explore a simple structure which is conducive toskill learning required for so many of the manipulation tasks. Specifically, wepropose a policy architecture that sequentially executes different action headsfor fixed durations, enabling the learning of primitive skills such as reachingand grasping. Our empirical evaluation on the Metaworld tasks reveals that thissimple structure outperforms standard policy learning methods, highlighting itspotential for improved skill acquisition.

On Time-Indexing as Inductive Bias in Deep RL for Sequential Manipulation Tasks