3 citations · 6 across the 8 of their papers we have counts for
8 papers
Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning
Vittorio Giammarino, James Queeney, Ioannis Ch. Paschalidis
We propose C-LAIfO, a computationally efficient algorithm designed for imitation learning from videos in the presence of visual mismatch between agent and expert domains. We analyz…
Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees
Yilei Chen, Vittorio Giammarino, James Queeney +1
Adversarial Imitation Learning (AIL) faces challenges with sample inefficiency because of its reliance on sufficient on-policy data to evaluate the performance of the current polic…
Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems
Ehsan Sabouni, H. M. Sabbir Ahmad, Vittorio Giammarino +3
Optimal control methods provide solutions to safety-critical problems but easily become intractable. Control Barrier Functions (CBFs) have emerged as a popular technique that facil…
A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations
Erhan Can Ozcan, Vittorio Giammarino, James Queeney +1
This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficie…
Adversarial Imitation Learning from Visual Observations using Latent Information
Vittorio Giammarino, James Queeney, Ioannis Ch. Paschalidis
We focus on the problem of imitation learning from visual observations, where the learning agent has access to videos of experts as its sole learning source. The challenges of this…
Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation
Vittorio Giammarino, James Queeney, Lucas C. Carstensen +2
We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theor…