Toronto Intelligent Systems Lab
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HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the …
Xin Huang
,
Guy Rosman
,
Igor Gilitschenski
,
Ashkan M. Jasour
,
Stephen McGill
,
John Leonard
,
Brian Williams
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Learning Interactive Driving Policies via Data-driven Simulation
Data-driven simulators promise high data-efficiency for driving policy learning. When used for modelling interactions, this …
Tsun-Hsuan Wang
,
Alexander Amini
,
Wilko Schwarting
,
Igor Gilitschenski
,
Sertac Karaman
,
Daniela Rus
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Code
Project
VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles
Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios. …
Alexander Amini
,
Tsun-Hsuan Wang
,
Igor Gilitschenski
,
Wilko Schwarting
,
Zhijian Liu
,
Song Han
,
Sertac Karaman
,
Daniela Rus
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Project
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space. In …
Tim Seyde
,
Igor Gilitschenski
,
Wilko Schwarting
,
Bartolomeo Stellato
,
Martin Riedmiller
,
Markus Wulfmeier
,
Daniela Rus
PDF
Strength Through Diversity: Robust Behavior Learning via Mixture Policies
Efficiency in robot learning is highly dependent on hyperparameters. Robot morphology and task structure differ widely and finding the …
Tim Seyde
,
Wilko Schwarting
,
Igor Gilitschenski
,
Markus Wulfmeier
,
Daniela Rus
PDF
GROUNDED: The Localizing Ground Penetrating Radar Evaluation Dataset
Mapping and localization using surface features is prone to failure due to environment changes such as inclement weather. Recently, …
Teddy Ort
,
Igor Gilitschenski
,
Daniela Rus
PDF
Project
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
Learning competitive behaviors in multi-agent settings such as racing requires long-term reasoning about potential adversarial …
Wilko Schwarting
,
Tim Seyde
,
Igor Gilitschenski
,
Lucas Liebenwein
,
Ryan Sander
,
Sertac Karaman
,
Daniela Rus
PDF
Code
Project
Differentiable Logic Layer for Rule Guided Trajectory Prediction
Xiao Li
,
Guy Rosman
,
Igor Gilitschenski
,
Jonathan DeCastro
,
Cristian Ioan Vasile
,
Sertac Karaman
,
Daniela Rus
PDF
Video
Exploiting Semantic and Public Prior Information in MonoSLAM
In this paper, we propose a method to use semantic information to improve the use of map priors in a sparse, feature-based MonoSLAM …
Chenxi Ye
,
Yiduo Wang
,
Ziwen Lu
,
Igor Gilitschenski
,
Martin Parsley
,
Simon J. Julier
PDF
Deep Orientation Uncertainty Learning based on a Bingham Loss
Reasoning about uncertain orientations is one of the core problems in many perception tasks such as object pose estimation or motion …
Igor Gilitschenski
,
Roshni Sahoo
,
Wilko Schwarting
,
Alexander Amini
,
Sertac Karaman
,
Daniela Rus
PDF
Code
Video
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