3 accepted papers
Data-driven methods provide effective solutions for robot trajectory generation in dynamic environments. Many physical constraints exist in the real world, and understanding these constraints to generate feasible trajectories for kinematics or dynamics is highly demanding regarding the data quantity…
Enabling robots to navigate safely and efficiently in dynamic, crowded environments requires learning from large-scale demonstrations, which are costly and unsafe to collect on physical platforms. While human videos offer a rich and scalable alternative, transferring these motion patterns to robots …
A planner for autonomous vehicles must be capable of operating in diverse and complex real-world environments. However, learning-based planners often struggle with limited generalization due to the long-tail distribution in datasets. Moreover, the black-box nature of neural networks limits their int