Interactive Hierarchical Task Learning from a Single Demonstration

By Anahita Mohseni-Kabir, Charles Rich, Sonia Chernova, C. Sidner, Daniel Miller. 2015

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Improving Robot Controller Transparency Through Autonomous Policy Explanation (2017, background): “Autonomous symbol abstraction techniques [16, 27] may also be applied to the learned domain MDPs, reducing the programmer’s workload with respect to annotating actions within the control software.”
Recent Advances in Robot Learning from Demonstration (2020, methodology): “Teleoperation is another widely used demonstration input and has been applied to trajectory learning (21), task learning (22), grasping (23), and high-level tasks (24).”
Collaborative Language Grounding Toward Situated Human-Robot Dialogue (2017, result): “As shown in this article and other recent work (Cantrell et al. 2012; Mohan, Kirk, and Laird 2013; Mohseni-Kabir et al. 2015; Thomason et al. 2016), language and collaborative dialogue play an imporArticles”
Autonomously constructing hierarchical task networks for planning and human-robot collaboration (2016, background): “However, these hierarchical networks are typically assumed to be manually specified or assume the availability of rich domain knowledge and planning operator (action) descriptions to facilitate their discovery [23], [24].”
A survey of robot learning from demonstrations for Human-Robot Collaboration (2017, background): “Although learning of high-level actions is the ultimate goal of the ﬁeld, there have been few studies that have investigated it for human-robot collaborative tasks [18].”