Learning from human feedback | Francesco Del Duchetto

Summary

When we take our robots outside the labs, due to the unconstrained nature of real-world deployments where the environment is unknown, always changing and contains people (whose behaviour is typically unpredictable), designing robots that can function autonomously becomes a very challenging task. However, the presence of humans in these enviornments provide a great opportunity for robots to learn how to improve their abilities —for example, on how to become more engaging to users during guided tours in a museum (Del Duchetto 2022)— and how to correct errors —such as, improving navigation from human demonstrated recovery trajectories (Del Duchetto 2018). I am interested in creating interfaces of communication between robots and their users to enable natural interactions and robot learning in situated interactions.

Videos

Projects

Lindsey: the Tour Guide Robot, Funded by Lincolnshire County Council, Partners: University of Lincoln (PI: Prof Marc Hanheide). Link: https://lcas.lincoln.ac.uk/wp/projects/lindsey-a-robot-tour-guide/
Trustworthy Accessible Robots for Inclusive Cultural experienceS (TARICS), Funded by UKRI TAS Hub, Partners: University of Lincoln (PI: Maria Galvez Trigo), University of Nottingham, Lincolnshire County Council, NICER Group - Nottingham International Consortium for Educational Research. Link: https://tas.ac.uk/research-projects-2022-23/tarics/

References

2022

RAL

Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions

Francesco Del Duchetto, and Marc Hanheide

IEEE Robotics and Automation Letters, 2022

🏅 Bib

@article{del2022learning,
  title = {Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions},
  author = {Del Duchetto, Francesco and Hanheide, Marc},
  journal = {IEEE Robotics and Automation Letters},
  volume = {7},
  number = {3},
  pages = {6934--6941},
  year = {2022},
  publisher = {IEEE},
  award = {Finalist for IROS 2022 Best Paper Award on Cognitive Robotics by KROS}
}

2020

Automatic Assessment and Learning of Robot Social Abilities

Francesco Del Duchetto, Paul Baxter, and Marc Hanheide

In Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 2020

Bib

@inproceedings{del2020automatic,
  title = {Automatic Assessment and Learning of Robot Social Abilities},
  author = {Del Duchetto, Francesco and Baxter, Paul and Hanheide, Marc},
  booktitle = {Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction},
  pages = {561--563},
  year = {2020},
}

Abstract visual programming of social robots for novice users

Onis Brown, Laurence Roberts-Elliott, Francesco Del Duchetto, and 2 more authors

In Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 2020

Bib

@inproceedings{brown2020abstract,
  title = {Abstract visual programming of social robots for novice users},
  author = {Brown, Onis and Roberts-Elliott, Laurence and Del Duchetto, Francesco and Hanheide, Marc and Baxter, Paul},
  booktitle = {Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction},
  pages = {154--156},
  year = {2020},
}

2019

Lindsey the Tour Guide Robot-Usage Patterns in a Museum Long-Term Deployment

Francesco Del Duchetto, Paul Baxter, and Marc Hanheide

In IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2019

Bib

@inproceedings{del2019lindsey,
  title = {Lindsey the Tour Guide Robot-Usage Patterns in a Museum Long-Term Deployment},
  author = {Del Duchetto, Francesco and Baxter, Paul and Hanheide, Marc},
  booktitle = {IEEE International Conference on Robot \& Human Interactive Communication (RO-MAN)},
  year = {2019},
  organization = {IEEE},
}

2018

RAL

Do not make the same mistakes again and again: Learning local recovery policies for navigation from human demonstrations

Francesco Del Duchetto, Ayse Kucukyilmaz, Luca Iocchi, and 1 more author

IEEE Robotics and Automation Letters, 2018

Bib Code

@article{del2018not,
  title = {Do not make the same mistakes again and again: Learning local recovery policies for navigation from human demonstrations},
  author = {Del Duchetto, Francesco and Kucukyilmaz, Ayse and Iocchi, Luca and Hanheide, Marc},
  journal = {IEEE Robotics and Automation Letters},
  volume = {3},
  number = {4},
  pages = {4084--4091},
  year = {2018},
  publisher = {IEEE},
}