Search Results for author: Arunkumar Byravan

Found 22 papers, 2 papers with code

Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities

no code implementations4 Dec 2023 Markus Wulfmeier, Arunkumar Byravan, Sarah Bechtle, Karol Hausman, Nicolas Heess

Contemporary artificial intelligence systems exhibit rapidly growing abilities accompanied by the growth of required resources, expansive datasets and corresponding investments into computing infrastructure.

Computational Efficiency reinforcement-learning +1

Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning

no code implementations14 Sep 2023 Cristina Pinneri, Sarah Bechtle, Markus Wulfmeier, Arunkumar Byravan, Jingwei Zhang, William F. Whitney, Martin Riedmiller

We present a novel approach to address the challenge of generalization in offline reinforcement learning (RL), where the agent learns from a fixed dataset without any additional interaction with the environment.

Data Augmentation Offline RL +2

Towards A Unified Agent with Foundation Models

no code implementations18 Jul 2023 Norman Di Palo, Arunkumar Byravan, Leonard Hasenclever, Markus Wulfmeier, Nicolas Heess, Martin Riedmiller

Language Models and Vision Language Models have recently demonstrated unprecedented capabilities in terms of understanding human intentions, reasoning, scene understanding, and planning-like behaviour, in text form, among many others.

Efficient Exploration Reinforcement Learning (RL) +2

Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains

no code implementations24 Feb 2023 Jingwei Zhang, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Abbas Abdolmaleki, Dushyant Rao, Nicolas Heess, Martin Riedmiller

We conduct a set of experiments in the RGB-stacking environment, showing that planning with the learned skills and the associated model can enable zero-shot generalization to new tasks, and can further speed up training of policies via reinforcement learning.

reinforcement-learning Reinforcement Learning (RL) +1

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

no code implementations10 Oct 2022 Arunkumar Byravan, Jan Humplik, Leonard Hasenclever, Arthur Brussee, Francesco Nori, Tuomas Haarnoja, Ben Moran, Steven Bohez, Fereshteh Sadeghi, Bojan Vujatovic, Nicolas Heess

A simulation is then created using the rendering engine in a physics simulator which computes contact dynamics from the static scene geometry (estimated from the NeRF volume density) and the dynamic objects' geometry and physical properties (assumed known).

Novel View Synthesis

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

1 code implementation21 Apr 2022 Bobak Shahriari, Abbas Abdolmaleki, Arunkumar Byravan, Abe Friesen, SiQi Liu, Jost Tobias Springenberg, Nicolas Heess, Matt Hoffman, Martin Riedmiller

Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control tasks.

Continuous Control reinforcement-learning +1

The Challenges of Exploration for Offline Reinforcement Learning

no code implementations27 Jan 2022 Nathan Lambert, Markus Wulfmeier, William Whitney, Arunkumar Byravan, Michael Bloesch, Vibhavari Dasagi, Tim Hertweck, Martin Riedmiller

Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked processes of reinforcement learning: collecting informative experience and inferring optimal behaviour.

Model Predictive Control Offline RL +2

Learning Dynamics Models for Model Predictive Agents

no code implementations29 Sep 2021 Michael Lutter, Leonard Hasenclever, Arunkumar Byravan, Gabriel Dulac-Arnold, Piotr Trochim, Nicolas Heess, Josh Merel, Yuval Tassa

This paper sets out to disambiguate the role of different design choices for learning dynamics models, by comparing their performance to planning with a ground-truth model -- the simulator.

Model-based Reinforcement Learning

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning

no code implementations15 Jun 2021 Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra, Dhruva TB, Arunkumar Byravan, Konstantinos Bousmalis, Andras Gyorgy, Csaba Szepesvari, Raia Hadsell, Nicolas Heess, Martin Riedmiller

Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional objectives or constraints in the policy optimization step.

Offline RL reinforcement-learning +1

Local Search for Policy Iteration in Continuous Control

no code implementations12 Oct 2020 Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz, Josh Merel, Arunkumar Byravan, Abbas Abdolmaleki, Jackie Kay, Jonas Degrave, Julian Schrittwieser, Yuval Tassa, Jonas Buchli, Dan Belov, Martin Riedmiller

We demonstrate that additional computation spent on model-based policy improvement during learning can improve data efficiency, and confirm that model-based policy improvement during action selection can also be beneficial.

Continuous Control Reinforcement Learning (RL)

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

no code implementations30 Oct 2019 Felix Leeb, Arunkumar Byravan, Dieter Fox

In this work, we bridge the gap between recent pose estimation and tracking work to develop a powerful method for robots to track objects in their surroundings.

Pose Estimation Translation

Prospection: Interpretable Plans From Language By Predicting the Future

no code implementations20 Mar 2019 Chris Paxton, Yonatan Bisk, Jesse Thomason, Arunkumar Byravan, Dieter Fox

High-level human instructions often correspond to behaviors with multiple implicit steps.

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

no code implementations2 Oct 2017 Arunkumar Byravan, Felix Leeb, Franziska Meier, Dieter Fox

In this work, we present an approach to deep visuomotor control using structured deep dynamics models.


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

no code implementations8 Jun 2016 Arunkumar Byravan, Dieter Fox

We introduce SE3-Nets, which are deep neural networks designed to model and learn rigid body motion from raw point cloud data.

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