About¶
As the tasks of autonomous manipulation robots get more complex, the tasking of the robots using natural-language instructions becomes more important. Executing such instructions in the way they are intended often requires robots to infer missing, and disambiguate given information using lots of common and commonsense knowledge. During my research work, I proposed the concept of Probabilistic Action Cores (PRAC) – an activity-centric probabilistic knowledge base for interpretation, disambiguation and completion of underspecified and vaguely stated instructions in natural language.
This package consists of an implementation of probabilistic knowledge services for natural-language instruction interpretation as a Python module (prac) that you can use to work with these services in your own Python scripts. For an introduction into using PRAC in your own scripts, see API-Specification.
Release notes¶
- Release 1.0.0 (19.12.2017)
- Initial Release
Contents:¶
Credits¶
Lead Developer¶
Daniel Nyga ()
Contributors¶
- Mareike Picklum ()
Acknowledgments¶
This work is supported in part by the EU FP7 projects RoboHow (grant number 288533), ACAT (grant number 600578) and CoTeSys cluster of excellence (Cognition for Technical Systems), part of the Excellence Initiative of the German Research Foundation (DFG):
Publications¶
- Gheorghe Lisca, Daniel Nyga, Ferenc Bálint-Benczédi, Hagen Langer, and Michael Beetz. Towards robots conducting chemical experiments. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Hamburg, Germany, 2015.
- Daniel Nyga and Michael Beetz. Everything robots always wanted to know about housework (but were afraid to ask). In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vilamoura, Portugal, October, 7–12 2012.
- Daniel Nyga and Michael Beetz. Cloud-based Probabilistic Knowledge Services for Instruction Interpretation. In International Symposium of Robotics Research (ISRR). Sestri Levante (Genoa), Italy, 2015.
- Daniel Nyga and Michael Beetz. Reasoning about Unmodelled Concepts – Incorporating Class Taxonomies in Probabilistic Relational Models. In Arxiv.org. 2015. Preprint: http://arxiv.org/abs/1504.05411.
- Daniel Nyga, Mareike Picklum, and Michael Beetz. What No Robot Has Seen Before – Probabilistic Interpretation of Natural-language Object Descriptions. In International Conference on Robotics and Automation (ICRA). Singapore, 2017. Accepted for publication.
- Daniel Nyga, Mareike Picklum, Sebastian Koralewski, and Michael Beetz. Instruction Completion through Instance-based Learning and Semantic Analogical Reasoning. In International Conference on Robotics and Automation (ICRA). Singapore, 2017. Accepted for publication.