Collaborative Learning of Hierarchical Task Networks from Demonstration and Instruction Anahita Mohseni-Kabir, Sonia Chernova and Charles Rich Worcester Polytechnic Institute

Project Objectives and Contributions Main Goal: Learning complex procedural tasks from human demonstration and instruction in the form of hierarchical task networks and applying it to car maintenance domain Project Contributions: Unified system that integrates hierarchical task networks (HTNs) and collaborative discourse theory into the learning from demonstration Learning task model from a small number of demonstrations Generalization techniques Integration of mixed-initiative interaction into the learning process through question asking

Related Work Collaborative Discourse Theory Disco (ANSI/CEA-2018 standard) (Grosz and Sidner, 1986 and Rich et al., 2001) Learning from Demonstration Mix LfD and planning (Nicolescu and Mataric, 2003) Integrate HTN and LfD (Rybski et al., 2007) Learn from Instruction (Mohan and Laird, 2011) Learn the HTN from task’s traces (Garland et al., 2001) Segmentation (Niekum et al., 2012) Active learning (Cakmak and Thomaz, 2012)

System Architecture Primitive actions Primitive and Non-primitive actions Task model visualization Questions and answers Primitive actions

Task Structure Learning Task Hierarchy Top-Down Bottom-Up Mix of Top-Down and Buttom-Up Temporal Constraints Single demonstration Data flow

System Overview

Generalization Input Generalization Merging multiple demonstrations Part/whole generalization Type generalization Merging multiple demonstrations Ontology

System Overview

Question Asking Question Type Repeated steps Grouping steps Example Repeated steps Should I(robot) execute UnscrewStud on other objects of type Stud of LFhub? Grouping steps Should I add a new task with Unscrew and PutDown as its steps? Applicability condition of alternative recipes What is the applicability condition of Rotate’s recipe with these steps? New task name What is the best name that describes this new task? Input of a task Please specify the input of Unscrew. Execution of one of the alternative recipes Should I achieve Rotate by executing recipe1 or recipe2?

Performance Tire rotation task Six primitive actions: Unscrew, Screw, Hang, Unhang, PutDown and PickUp Complete execution of two recipes of tire rotation requires 128 steps Complete teaching of the HTN (two recipes) on average requires 26 demonstration interactions E.g., 15 demonstrations, 11 instructions, 11 question responses

Conclusion and Future Work Make the interaction as natural as possible by making the UI and robot look like a unified system Do user study and use the real robot instead of the simulation Learn applicability conditions and pre/postconditions of the tasks Failure detection and recovery This work is supported in part by ONR contract N00014-13-1-0735, in collaboration with Dmitry Berenson, Jim Mainprice , Artem Gritsenko, and Daniel Miller.

References Barbara J. Grosz and Candace L. Sidner. Attention, intentions, and the structure of discourse. Comput. Linguist., 12(3):175–204, July 1986. Charles Rich, Candace L Sidner, and Neal Lesh. Collagen: applying collaborative discourse theory to human-computer interaction. AI Magazine, 22 (4):15, 2001. Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning. A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5):469–483, 2009. Paul E Rybski, Kevin Yoon, Jeremy Stolarz, and Manuela M Veloso. Interactive robot task training through dialog and demonstration. In ACM/IEEE Int. Conf. on Human-Robot Interaction, pages 49–56, 2007.

References Scott Niekum, Sarah Osentoski, George Konidaris, and Andrew G Barto. Learning and generalization of complex tasks from unstructured demonstrations. In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pages 5239–5246, 2012. Maya Cakmak and Andrea L Thomaz. Designing robot learners that ask good questions. In ACM/IEEE International Conference on Human-Robot Interaction, pages 17–24. ACM, 2012. Monica N Nicolescu and Maja J Mataric. Natural methods for robot task learning: Instructive demonstrations, generalization and practice. In AAMAS, pages 241–248, 2003.

Merging