Arunkumar Byravan | DeepAI

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∙ 09/14/2023

Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning

We present a novel approach to address the challenge of generalization i...

0 Cristina Pinneri, et al. ∙

∙ 07/18/2023

Towards A Unified Agent with Foundation Models

Language Models and Vision Language Models have recently demonstrated un...

0 Norman Di Palo, et al. ∙

∙ 05/18/2023

A Generalist Dynamics Model for Control

We investigate the use of transformer sequence models as dynamics models...

0 Ingmar Schubert, et al. ∙

∙ 04/26/2023

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning

We investigate whether Deep Reinforcement Learning (Deep RL) is able to ...

0 Tuomas Haarnoja, et al. ∙

∙ 02/24/2023

Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains

In this paper we study the problem of learning multi-step dynamics predi...

0 Jingwei Zhang, et al. ∙

∙ 10/10/2022

NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields

We present a system for applying sim2real approaches to "in the wild" sc...

0 Arunkumar Byravan, et al. ∙

∙ 04/21/2022

Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach

Actor-critic algorithms that make use of distributional policy evaluatio...

0 Bobak Shahriari, et al. ∙

∙ 01/27/2022

The Challenges of Exploration for Offline Reinforcement Learning

Offline Reinforcement Learning (ORL) enablesus to separately study the t...

0 Nathan Lambert, et al. ∙

∙ 10/12/2021

Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes

We study the problem of robotic stacking with objects of complex geometr...

0 Alex X. Lee, et al. ∙

∙ 10/07/2021

Evaluating model-based planning and planner amortization for continuous control

There is a widespread intuition that model-based control methods should ...

0 Arunkumar Byravan, et al. ∙

∙ 09/29/2021

Learning Dynamics Models for Model Predictive Agents

Model-Based Reinforcement Learning involves learning a dynamics model fr...

0 Michael Lutter, et al. ∙

∙ 06/15/2021

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning

Many advances that have improved the robustness and efficiency of deep r...

0 Abbas Abdolmaleki, et al. ∙

∙ 11/03/2020

Representation Matters: Improving Perception and Exploration for Robotics

Projecting high-dimensional environment observations into lower-dimensio...

0 Markus Wulfmeier, et al. ∙

∙ 10/12/2020

Local Search for Policy Iteration in Continuous Control

We present an algorithm for local, regularized, policy improvement in re...

0 Jost Tobias Springenberg, et al. ∙

∙ 10/30/2019

Motion-Nets: 6D Tracking of Unknown Objects in Unseen Environments using RGB

In this work, we bridge the gap between recent pose estimation and track...

2 Felix Leeb, et al. ∙

∙ 10/09/2019

Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models

Humans are masters at quickly learning many complex tasks, relying on an...

5 Arunkumar Byravan, et al. ∙

∙ 03/20/2019

Prospection: Interpretable Plans From Language By Predicting the Future

High-level human instructions often correspond to behaviors with multipl...

8 Chris Paxton, et al. ∙

∙ 10/02/2017

SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control

In this work, we present an approach to deep visuomotor control using st...

0 Arunkumar Byravan, et al. ∙

∙ 06/08/2016

SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks

We introduce SE3-Nets, which are deep neural networks designed to model ...

0 Arunkumar Byravan, et al. ∙

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