Wilko Schwarting

About Wilko Schwarting

Wilko Schwarting, With an exceptional h-index of 22 and a recent h-index of 21 (since 2020), a distinguished researcher at Massachusetts Institute of Technology, specializes in the field of Artificial Intelligence, Robotics, Machine Learning, Game Theory, Optimization.

His recent articles reflect a diverse array of research interests and contributions to the field:

Social behavior for autonomous vehicles

OptFlow: Fast Optimization-based Scene Flow Estimation without Supervision

Systems and methods for training a scene simulator using real and simulated agent data

Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution

Autonomous navigation in a cluttered environment

Dynamic multi-team racing: Competitive driving on 1/10-th scale vehicles via learning in simulation

Solving Continuous Control via Q-learning

Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks

Wilko Schwarting Information

University

Position

___

Citations(all)

2945

Citations(since 2020)

2844

Cited By

809

hIndex(all)

22

hIndex(since 2020)

21

i10Index(all)

27

i10Index(since 2020)

27

Email

University Profile Page

Google Scholar

Wilko Schwarting Skills & Research Interests

Artificial Intelligence

Robotics

Machine Learning

Game Theory

Optimization

Top articles of Wilko Schwarting

Title

Journal

Author(s)

Publication Date

Social behavior for autonomous vehicles

2024/1/30

OptFlow: Fast Optimization-based Scene Flow Estimation without Supervision

Rahul Ahuja

Chris Baker

Wilko Schwarting

2024

Systems and methods for training a scene simulator using real and simulated agent data

2024/4/11

Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution

arXiv preprint arXiv:2404.04253

Tim Seyde

Peter Werner

Wilko Schwarting

Markus Wulfmeier

Daniela Rus

2024/4/5

Autonomous navigation in a cluttered environment

2023/11/7

Dynamic multi-team racing: Competitive driving on 1/10-th scale vehicles via learning in simulation

Peter Werner

Tim Seyde

Paul Drews

Thomas Matrai Balch

Igor Gilitschenski

...

2023/8/30

Solving Continuous Control via Q-learning

International Conference on Learning Representations (ICLR)

Tim Seyde

Peter Werner

Wilko Schwarting

Igor Gilitschenski

Martin Riedmiller

...

2023

Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks

Ryan Sander

Wilko Schwarting

Tim Seyde

Igor Gilitschenski

Sertac Karaman

...

2022/5/18

Deep interactive motion prediction and planning: Playing games with motion prediction models

Jose Luis Vazquez Espinoza

Alexander Liniger

Wilko Schwarting

Daniela Rus

Luc Van Gool

2022/5/11

Navigating congested environments with risk level sets

2022/4/12

Learning Interactive Driving Policies via Data-driven Simulation

2022 IEEE International Conference on Robotics and Automation (ICRA)

Tsun-Hsuan Wang

Alexander Amini

Wilko Schwarting

Igor Gilitschenski

Sertac Karaman

...

2022

VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles

2022 IEEE International Conference on Robotics and Automation (ICRA)

Alexander Amini

Tsun-Hsuan Wang

Igor Gilitschenski

Wilko Schwarting

Zhijian Liu

...

2022

Stochastic Dynamic Games in Belief Space

IEEE Transactions on Robotics

Wilko Schwarting

Alyssa Pierson

Sertac Karaman

Daniela Rus

2021

Strength Through Diversity: Robust Behavior Learning via Mixture Policies

Tim Seyde

Wilko Schwarting

Igor Gilitschenski

Markus Wulfmeier

Daniela Rus

2021/6/19

Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies

Advances in Neural Information Processing Systems (NeurIPS) 34

Tim Seyde

Igor Gilitschenski

Wilko Schwarting

Bartolomeo Stellato

Martin Riedmiller

...

2021

Semi-Cooperative Control for Autonomous Emergency Vehicles

Noam Buckman

Wilko Schwarting

Sertac Karaman

Daniela Rus

2021

Learning and control for interactions in mixed human-robot environments

Wilko Schwarting

2021

Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles

5th Annual Conference on Robot Learning (CoRL)

Tim Seyde*

Wilko Schwarting*

Sertac Karaman

Daniela Rus

2021

Deep Evidential Regression

Advances in Neural Information Processing Systems (NeurIPS) 33

Alexander Amini

Wilko Schwarting

Ava Soleimany

Daniela Rus

2020

Weighted buffered voronoi cells for distributed semi-cooperative behavior

Alyssa Pierson

Wilko Schwarting

Sertac Karaman

Daniela Rus

2020/5/31

See List of Professors in Wilko Schwarting University(Massachusetts Institute of Technology)

Co-Authors

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