text: 443,64,971310121203781633,721931072,Shimon Whiteson,Our latest paper is the fruit of a new collaboration with @oxfordrobots: hierarchical learning from demonstration: weakly supervising the demonstrations enables zero-shot learning for robots <LINK> @KyriacosShiarli @IngmarPosner @markus_with_k @whi_rl,https://arxiv.org/abs/1803.01840,"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, they 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 approach simultaneously 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. ",TACO: Learning Task Decomposition via Temporal Alignment for Control,1,['Our latest paper is the fruit of a new collaboration with @oxfordrobots: hierarchical learning from demonstration: weakly supervising the demonstrations enables zero-shot learning for robots <LINK> @KyriacosShiarli @IngmarPosner @markus_with_k @whi_rl'],18,03,251