2 citations · 6 across the 9 of their papers we have counts for
9 papers
Coachable agents for interactive gameplay
Roberto Capobianco, Harm van Seijen, Nolan D. Bard +38
Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation m…
Prosody as a Teaching Signal for Agent Learning: Exploratory Studies and Algorithmic Implications
Matilda Knierim, Sahil Jain, Murat Han Aydoğan +4
Agent learning from human interaction often relies on explicit signals, but implicit social cues, such as prosody in speech, could provide valuable information for more effective l…
Towards Principled Representation Learning from Videos for Reinforcement Learning
Dipendra Misra, Akanksha Saran, Tengyang Xie +2
We study pre-training representations for decision-making using video data, which is abundantly available for tasks such as game agents and software testing. Even though significan…
EyeO: Autocalibrating Gaze Output with Gaze Input
Akanksha Saran, Jacob Alber, Cyril Zhang +3
Gaze tracking devices have the potential to greatly expand interactivity, yet miscalibration remains a significant barrier to use. As devices miscalibrate, people tend to compensat…
Streaming Active Learning with Deep Neural Networks
Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy +2
Active learning is perhaps most naturally posed as an online learning problem. However, prior active learning approaches with deep neural networks assume offline access to the enti…
Personalized Reward Learning with Interaction-Grounded Learning (IGL)
Jessica Maghakian, Paul Mineiro, Kishan Panaganti +3
In an era of countless content offerings, recommender systems alleviate information overload by providing users with personalized content suggestions. Due to the scarcity of explic…
Understanding Acoustic Patterns of Human Teachers Demonstrating Manipulation Tasks to Robots
Akanksha Saran, Kush Desai, Mai Lee Chang +3
Humans use audio signals in the form of spoken language or verbal reactions effectively when teaching new skills or tasks to other humans. While demonstrations allow humans to teac…
Interaction-Grounded Learning with Action-inclusive Feedback
Tengyang Xie, Akanksha Saran, Dylan J. Foster +5
Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its p…
A Ranking Game for Imitation Learning
Harshit Sikchi, Akanksha Saran, Wonjoon Goo +1
We propose a new framework for imitation learning -- treating imitation as a two-player ranking-based game between a policy and a reward. In this game, the reward agent learns to s…