no code implementations • 28 Sep 2022 • Zhao Han, Emmanuel Senft, Muneeb I. Ahmad, Shelly Bagchi, Amir Yazdani, Jason R. Wilson, Boyoung Kim, Ruchen Wen, Justin W. Hart, Daniel Hernández García, Matteo Leonetti, Ross Mead, Reuth Mirsky, Ahalya Prabhakar, Megan L. Zimmerman
The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration on AI theory and methods aimed at HRI since 2014.
no code implementations • 22 Jun 2022 • Aravinda Ramakrishnan Srinivasan, Yi-Shin Lin, Morris Antonello, Anthony Knittel, Mohamed Hasan, Majd Hawasly, John Redford, Subramanian Ramamoorthy, Matteo Leonetti, Jac Billington, Richard Romano, Gustav Markkula
Even though the models' RMSE value differed, all the models captured the kinematic-dependent merging behavior but struggled at varying degrees to capture the more nuanced courtesy lane change and highway lane change behavior.
no code implementations • 21 Oct 2021 • Yi-Shin Lin, Aravinda Ramakrishnan Srinivasan, Matteo Leonetti, Jac Billington, Gustav Markkula
Many models account for the traffic flow of road users but few take the details of local interactions into consideration and how they could deteriorate into safety-critical situations.
no code implementations • 22 Sep 2021 • Reuth Mirsky, Megan Zimmerman, Muneed Ahmad, Shelly Bagchi, Felix Gervits, Zhao Han, Justin Hart, Daniel Hernández García, Matteo Leonetti, Ross Mead, Emmanuel Senft, Jivko Sinapov, Jason Wilson
In addition, acknowledging that ethics is an inherent part of the human-robot interaction, we encourage submissions of works on ethics for HRI.
no code implementations • 6 Jul 2021 • Ricardo Luna Gutierrez, Matteo Leonetti
In Meta-Reinforcement Learning (meta-RL) an agent is trained on a set of tasks to prepare for and learn faster in new, unseen, but related tasks.
no code implementations • 21 Apr 2021 • Aravinda Ramakrishnan Srinivasan, Mohamed Hasan, Yi-Shin Lin, Matteo Leonetti, Jac Billington, Richard Romano, Gustav Markkula
There is quickly growing literature on machine-learned models that predict human driving trajectories in road traffic.
no code implementations • 6 Nov 2020 • Wissam Bejjani, Wisdom C. Agboh, Mehmet R. Dogar, Matteo Leonetti
Solving this task requires reasoning over the likely locations of the target object.
no code implementations • NeurIPS 2020 • Ricardo Luna Gutierrez, Matteo Leonetti
In Meta-Reinforcement Learning (meta-RL) an agent is trained on a set of tasks to prepare for and learn faster in new, unseen, but related tasks.
no code implementations • 26 Oct 2020 • Shelly Bagchi, Jason R. Wilson, Muneeb I. Ahmad, Christian Dondrup, Zhao Han, Justin W. Hart, Matteo Leonetti, Katrin Lohan, Ross Mead, Emmanuel Senft, Jivko Sinapov, Megan L. Zimmerman
We see a growing need for research that lives directly at the intersection of AI and HRI that is serviced by this symposium.
no code implementations • 2 Aug 2020 • Andrea Bassich, Francesco Foglino, Matteo Leonetti, Daniel Kudenko
Curriculum Learning for Reinforcement Learning is an increasingly popular technique that involves training an agent on a sequence of intermediate tasks, called a Curriculum, to increase the agent's performance and learning speed.
no code implementations • 10 Mar 2020 • Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone
Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback.
1 code implementation • 28 Feb 2020 • Mohamed Hasan, Matthew Warburton, Wisdom C. Agboh, Mehmet R. Dogar, Matteo Leonetti, He Wang, Faisal Mushtaq, Mark Mon-Williams, Anthony G. Cohn
From this, we devised a qualitative representation of the task space to abstract the decision making, irrespective of the number of obstacles.
no code implementations • 11 Sep 2019 • Justin W. Hart, Nick DePalma, Richard G. Freedman, Luca Iocchi, Matteo Leonetti, Katrin Lohan, Ross Mead, Emmanuel Senft, Jivko Sinapov, Elin A. Topp, Tom Williams
The past few years have seen rapid progress in the development of service robots.
Robotics
no code implementations • 17 Jun 2019 • Francesco Foglino, Matteo Leonetti, Simone Sagratella, Ruggiero Seccia
Curriculum learning is often employed in deep reinforcement learning to let the agent progress more quickly towards better behaviors.
1 code implementation • 13 Jun 2019 • Francesco Foglino, Christiano Coletto Christakou, Ricardo Luna Gutierrez, Matteo Leonetti
We propose a task sequencing algorithm maximizing the cumulative return, that is, the return obtained by the agent across all the learning episodes.
no code implementations • 31 Jan 2019 • Francesco Foglino, Christiano Coletto Christakou, Matteo Leonetti
In reinforcement learning, all previous task sequencing methods have shaped exploration with the objective of reducing the time to reach a given performance level.