Composing Diverse Policies for Temporally Extended Tasks
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Temporally extended and sequenced robot motion tasks are often characterized by discontinuous switches between different types of local dynamics. These change-points can be exploited to build approximate models of the interleaving regions, which in turn allow the design of region-specific controllers. These can then be combined to create the initiation state-space of a final policy. However, such a pipeline can become challenging to implement for combinatorially complex, temporarily extended tasks - especially so when sub-controllers work on different information streams, time scales and action spaces. In this paper, we introduce a method that can compose diverse policies based on scripted motion planning, dynamic motion primitives and neural networks. In order to do this, we extend the options framework to introduce a per-option dynamics module and a global function that evaluates a goal metric. Additionally, we can leverage expert demonstrations to sequence these local policies, converting the learning problem in hierarchical reinforcement learning to a planning problem at inference time. We first illustrate the core concepts with an MDP benchmark, and then with a physical gear assembly task solved on a PR2 robot. We show that the proposed approach successfully discovers the optimal sequence of policies and solves both tasks efficiently.
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