Robot Autonomy

Robot autonomy research aims to enable robots to operate independently and effectively in complex, unpredictable environments. Current efforts focus on improving robustness to failures through techniques like vision-language models optimized for spatial reasoning and self-supervised neural-symbolic learning frameworks that leverage symbolic reasoning for better generalization. These advancements, along with the development of modular and scalable system architectures and improved human-robot interaction strategies, are crucial for expanding the capabilities and reliability of robots across diverse applications, from search and rescue to autonomous driving and space exploration. The field is also actively addressing challenges in safety, trust, and efficient resource management.