REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer
Researchers introduce REAP, a reinforcement learning-based autonomous parking system that uses Gaussian Splatting to simulate real-world environments for training, then transfers the model to physical vehicles. The method addresses limitations of traditional multi-stage parking approaches by jointly optimizing perception and planning, achieving successful parking in extreme scenarios like mechanical slots.