Search Results for author:
Found 16 papers, 2 papers with code
Proceedings of the AI-HRI Symposium at AAAI-FSS 2022
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.
Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?
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.
A Utility Maximization Model of Pedestrian and Driver Interactions
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.
AI-HRI 2021 Proceedings
In addition, acknowledging that ethics is an inherent part of the human-robot interaction, we encourage submissions of works on ethics for HRI.
Meta-Reinforcement Learning for Heuristic Planning
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.
Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned model
There is quickly growing literature on machine-learned models that predict human driving trajectories in road traffic.
Occlusion-Aware Search for Object Retrieval in Clutter
Solving this task requires reasoning over the likely locations of the target object.
Information-theoretic Task Selection for Meta-Reinforcement Learning
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.
Proceedings of the AI-HRI Symposium at AAAI-FSS 2020
We see a growing need for research that lives directly at the intersection of AI and HRI that is serviced by this symposium.
Curriculum Learning with a Progression Function
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.
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback.
Human-like Planning for Reaching in Cluttered Environments
From this, we devised a qualitative representation of the task space to abstract the decision making, irrespective of the number of obstacles.
Proceedings of the AI-HRI Symposium at AAAI-FSS 2019
The past few years have seen rapid progress in the development of service robots.
Robotics
A gray-box approach for curriculum learning
Curriculum learning is often employed in deep reinforcement learning to let the agent progress more quickly towards better behaviors.
Curriculum Learning for Cumulative Return Maximization
We propose a task sequencing algorithm maximizing the cumulative return, that is, the return obtained by the agent across all the learning episodes.
An Optimization Framework for Task Sequencing in Curriculum Learning
In reinforcement learning, all previous task sequencing methods have shaped exploration with the objective of reducing the time to reach a given performance level.
