Work in Progress - Automated Generation of Robotic Planning Domains from Observations
Other conference contribution, 2021

In this paper, we report the results of our latest work on the automated generation of planning operators from human demonstrations, and we present some of our future research ideas. To automatically generate planning operators, our system segments and recognizes different observed actions from human demonstrations. We then proposed an automatic extraction method to detect the relevant preconditions and effects from these demonstrations. Finally, our system generates the associated planning operators and finds a sequence of actions that satisfies a user-defined goal using a symbolic planner. The plan is deployed on a simulated TIAGo robot. Our future research directions include learning from and explaining execution failures and detecting cause-effect relationships between demonstrated hand activities and their consequences on the robot's environment. The former is crucial for trust-based and efficient human-robot collaboration and the latter for learning in realistic and dynamic environments.


Learning from Experience


Maximilian Diehl

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Karinne Ramirez-Amaro

Chalmers, Electrical Engineering, Systems and control, Mechatronics

18th International Conference on Ubiquitous Robots (UR), Organised session: 'Robots in the household: A review of task knowledge acquisition, planning, and execution'

18th International Conference on Ubiquitous Robots (UR)
Hybrid conference (Online & Gangwon-do), South Korea,

Learning & Understanding Human-Centered Robotic Manipulation Strategies

Chalmers AI Research Centre (CHAIR), 2020-01-13 -- 2025-01-14.

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