To enhance the performance of large language models (LLM) on downstream tasks, one
solution is to fine-tune certain LLM parameters and make it better align with the …

Deep Reinforcement Learning (DRL) has emerged as a transformative approach in mobile
robot path planning, addressing challenges associated with dynamic and uncertain …

Conventional geometry-based SLAM systems lack dense 3D reconstruction capabilities
since their data association usually relies on feature correspondences. Additionally, learning …

Despite the promising future of autonomous robots, several key issues currently remain that
can lead to compromised performance and safety. One such issue is latency, where we find …

Cloud robotics enables robots to offload complex computational tasks to cloud servers for
performance and ease of management. However, cloud compute can be costly, cloud …

Deep Reinforcement Learning (DRL) is critical for autonomous systems to continuously
learn and adapt in dynamic environments. However, frequent retraining in DRL leads to high …

The rapid evolution of electronic embedded systems (EES) has brought significant
challenges in optimizing their performance in real-time environments. These systems are …

This study introduces a novel optimization framework that assesses and enhances deep
learning algorithm performance across autonomous car operating systems. The framework …