Collaborative Learning of Hierarchical Task Networks from Demonstration and Instruction Anahita Mohseni-Kabir, Sonia Chernova and Charles Rich Worcester Polytechnic Institute
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Collaborative Learning of Hierarchical Task Networks from Demonstration and Instruction Anahita Mohseni-Kabir, Sonia Chernova and Charles Rich Worcester.
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Slide 1
Collaborative Learning of Hierarchical Task Networks from
Demonstration and Instruction Anahita Mohseni-Kabir, Sonia Chernova
and Charles Rich Worcester Polytechnic Institute
Slide 2
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 2
Slide 3
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
tasks traces (Garland et al., 2001) Segmentation (Niekum et al.,
2012) Active learning (Cakmak and Thomaz, 2012) 3
Slide 4
System Architecture 4 Primitive actions Primitive and
Non-primitive actions Task model visualization Questions and
answers
Slide 5
Task Structure Learning Task Hierarchy Top-Down Bottom-Up Mix
of Top-Down and Buttom-Up Temporal Constraints Single demonstration
Data flow 5
Question Asking 9 Question Type 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 Rotates 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?
Slide 10
10
Slide 11
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 11
Slide 12
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 12 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.
Slide 13
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):469483, 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 4956, 2007. 13
Slide 14
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 52395246, 2012. Maya Cakmak and Andrea L
Thomaz. Designing robot learners that ask good questions. In
ACM/IEEE International Conference on Human-Robot Interaction, pages
1724. ACM, 2012. Monica N Nicolescu and Maja J Mataric. Natural
methods for robot task learning: Instructive demonstrations,
generalization and practice. In AAMAS, pages 241248, 2003. 14