Sven Weishaupt

About

Sven Weishaupt is a rising researcher in the field of autonomous robotics, specializing in deep reinforcement learning (DRL) for path planning and collision avoidance. His work focuses on developing model-free, actor-critic algorithms—such as Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3)—to enable intelligent navigation for both stationary robotic manipulators and autonomous vehicles. A key contribution is his innovative use of egocentric state space descriptions, which allow a robot to perceive its environment from its own perspective rather than relying on global coordinates, significantly boosting learning efficiency and path-planning performance. His most cited paper (2024, 3 citations) presents a comparative analysis of multiple DRL approaches for a 3-DoF robot, while his subsequent work extends these methods to autonomous vehicle control. Though early in his career, Weishaupt’s research is gaining traction for its practical, real-world applicability in safe, collision-free navigation. His work represents a promising step toward more adaptive and perceptive autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Comparative Analysis of Multiple Deep Reinforcement Learning Approaches for Collision-Free Path-Planning of a 3-DoF-Robot

   3 citations · 2024
2. 2
   Reinforcement Learning for Path Planning and Control of an Autonomous Vehicle with Collision Avoidance

   2 citations · 2024
3. 3
   Boosting Deep Reinforcement Learning-Based Path Planning for Robotic Manipulators With Egocentric State Space Descriptions

   2 citations · 2024

Key Collaborators

Contact & Links