5 papers
Autonomous Vehicle Path Planning by Searching With Differentiable Simulation
Asen Nachkov, Jan-Nico Zaech, Danda Pani Paudel +2
Planning allows an agent to safely refine its actions before executing them in the real world. In autonomous driving, this is crucial to avoid collisions and navigate in complex, d…
Unlocking Efficient Vehicle Dynamics Modeling via Analytic World Models
Asen Nachkov, Danda Pani Paudel, Jan-Nico Zaech +2
Differentiable simulators represent an environment's dynamics as a differentiable function. Within robotics and autonomous driving, this property is used in Analytic Policy Gradien…
LLM Agents Beyond Utility: An Open-Ended Perspective
Asen Nachkov, Xi Wang, Luc Van Gool
Recent LLM agents have made great use of chain of thought reasoning and function calling. As their capabilities grow, an important question arises: can this software represent not…
Autonomous Vehicle Controllers From End-to-End Differentiable Simulation
Asen Nachkov, Danda Pani Paudel, Luc Van Gool
Current methods to learn controllers for autonomous vehicles (AVs) focus on behavioural cloning. Being trained only on exact historic data, the resulting agents often generalize po…
Neural 4D Evolution under Large Topological Changes from 2D Images
AmirHossein Naghi Razlighi, Tiago Novello, Asen Nachkov +2
In the literature, it has been shown that the evolution of the known explicit 3D surface to the target one can be learned from 2D images using the instantaneous flow field, where t…