135 citations · 186 across the 6 of their papers we have counts for
6 papers
Robust Reinforcement Learning Objectives for Sequential Recommender Systems
Melissa Mozifian, Tristan Sylvain, Dave Evans +1
Attention-based sequential recommendation methods have shown promise in accurately capturing users' evolving interests from their past interactions. Recent research has also explor…
Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop
Sebastian Höfer, Kostas Bekris, Ankur Handa +12
This report presents the debates, posters, and discussions of the Sim2Real workshop held in conjunction with the 2020 edition of the "Robotics: Science and System" conference. Twel…
Intervention Design for Effective Sim2Real Transfer
Melissa Mozifian, Amy Zhang, Joelle Pineau +1
The goal of this work is to address the recent success of domain randomization and data augmentation for the sim2real setting. We explain this success through the lens of causal in…
Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models
Yuchen Wu, Melissa Mozifian, Florian Shkurti
The potential benefits of model-free reinforcement learning to real robotics systems are limited by its uninformed exploration that leads to slow convergence, lack of data-efficien…
Learning Domain Randomization Distributions for Training Robust Locomotion Policies
Melissa Mozifian, Juan Camilo Gamboa Higuera, David Meger +1
Domain randomization (DR) is a successful technique for learning robust policies for robot systems, when the dynamics of the target robot system are unknown. The success of policie…
Joint 3D Proposal Generation and Object Detection from View Aggregation
Jason Ku, Melissa Mozifian, Jungwook Lee +2
We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to gen…