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Oral
TACO: Learning Task Decomposition via Temporal Alignment for Control
Kyriacos Shiarlis · Markus Wulfmeier · Sasha Salter · Shimon Whiteson · Ingmar Posner

Wed Jul 11 02:40 AM -- 02:50 AM (PDT) @ Victoria
in Deep Learning (Neural Network Architectures) 1 »

Many advanced Learning from Demonstration (LfD) methods consider the decomposition of complex, real-world tasks into simpler sub-tasks.By reusing the corresponding sub-policies within and between tasks, we can provide training data for each policy from different high-level tasks and compose them to perform novel ones.Existing approaches to modular LfD focus either on learning a single high-level task or depend on domain knowledge and temporal segmentation. In contrast, we propose a weakly supervised, domain-agnostic approach based on task sketches, which include only the sequence of sub-tasks performed in each demonstration. Our approachsimultaneously aligns the sketches with the observed demonstrations and learns the required sub-policies. This improves generalisation in comparison to separate optimisation procedures.We evaluate the approach on multiple domains, including a simulated 3D robot arm control task using purely image-based observations. The results show that our approach performs commensurately with fully supervised approaches, while requiring significantly less annotation effort.

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Author Information

Kyriacos Shiarlis (Latent Logic LTD)
Markus Wulfmeier (University of Oxford)
Sasha Salter (University of Oxford)
Shimon Whiteson (University of Oxford)
Ingmar Posner (University of Oxford)

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