Treating vascular diseases in the brain requires access to the affected region inside the
body. This is usually accomplished through a minimally invasive technique that involves the …

The dominant paradigm for end-to-end robot learning focuses on optimizing task-specific
objectives that solve a single robotic problem such as picking up an object or reaching a …

Recent advances in robot-assisted surgery have resulted in progressively more precise,
efficient, and minimally invasive procedures, sparking a new era of robotic surgical …

Recent applications of deep reinforcement learning (DRL) in surgical autonomy have shown
promising results in automating various surgical sub-tasks. While most of these studies …

Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods
that exhibit the desired motion quality for delicate surgical interventions. To this end, we …

Physics-based simulations have accelerated progress in robot learning for driving,
manipulation, and locomotion. Yet, a fast, accurate, and robust surgical simulation …

The rise of Large Language Models (LLMs) has impacted research in robotics and
automation. While progress has been made in integrating LLMs into general robotics tasks …

In this work, we investigate laparoscopic camera motion automation through imitation
learning from retrospective videos of laparoscopic interventions. A novel method is …

Autonomous robots in endovascular operations have the potential to navigate circulatory
systems safely and reliably while decreasing the susceptibility to human errors. However …

In this work, we present SuFIA, the first framework for natural language-guided augmented
dexterity for robotic surgical assistants. SuFIA incorporates the strong reasoning capabilities …