Search Results for author: Melissa Mozifian

Found 6 papers, 1 papers with code

Generalizing Successor Features to continuous domains for Multi-task Learning

no code implementations29 Sep 2021 Melissa Mozifian, Dieter Fox, David Meger, Fabio Ramos, Animesh Garg

In this paper, we consider the problem of continuous control for various robot manipulation tasks with an explicit representation that promotes skill reuse while learning multiple tasks, related through the reward function.

Continuous Control Decision Making +1

Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop

no code implementations7 Dec 2020 Sebastian Höfer, Kostas Bekris, Ankur Handa, Juan Camilo Gamboa, Florian Golemo, Melissa Mozifian, Chris Atkeson, Dieter Fox, Ken Goldberg, John Leonard, C. Karen Liu, Jan Peters, Shuran Song, Peter Welinder, Martha White

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.

Intervention Design for Effective Sim2Real Transfer

no code implementations3 Dec 2020 Melissa Mozifian, Amy Zhang, Joelle Pineau, David Meger

The goal of this work is to address the recent success of domain randomization and data augmentation for the sim2real setting.

Causal Inference Data Augmentation

Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models

no code implementations2 Nov 2020 Yuchen Wu, Melissa Mozifian, Florian Shkurti

Unlike the majority of existing methods that assume optimal demonstrations and incorporate the demonstration data as hard constraints on policy optimization, we instead incorporate demonstration data as advice in the form of a reward shaping potential trained as a generative model of states and actions.

Imitation Learning reinforcement-learning

Learning Domain Randomization Distributions for Training Robust Locomotion Policies

no code implementations2 Jun 2019 Melissa Mozifian, Juan Camilo Gamboa Higuera, David Meger, Gregory Dudek

We explore the use of gradient-based search methods to learn a domain randomization with the following properties: 1) The trained policy should be successful in environments sampled from the domain randomization distribution 2) The domain randomization distribution should be wide enough so that the experience similar to the target robot system is observed during training, while addressing the practicality of training finite capacity models.

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