5 accepted papers
Automatically generating robot applications from natural language promises to lower the barrier to automation, but remains difficult in domains that demand reliability and transparency, such as industrial assembly or collaborative manipulation. End-to-end policies and a large language model(LLM)-bas…
Safety is a critical concern in human-robot collaboration (HRC). As collaborative robots take on increasingly complex tasks in human environments, their systems have become more sophisticated through the integration of multimodal sensors, including force-torque sensors, cameras, LiDARs, and IMUs. Ho
We present LiHRA, a novel dataset designed to facilitate the development of automated, learning-based, or classical risk monitoring (RM) methods for Human-Robot Interaction (HRI) scenarios. The growing prevalence of collaborative robots in industrial environments has increased the need for reliable
AI-controlled robotic systems can introduce significant risks to both humans and the environment. Traditional reliability assessment methods fall short in addressing the complexities of these systems, particularly when dealing with black-box or dynamically changing control policies. The traditional
Due to the increasing behavioral and structural complexity of robots, it is challenging to predict the execution outcome after error detection. Anomaly detection methods can help detect errors and prevent potential failures. However, not every fault leads to a failure due to the system’s fault toler…