SurgicAI: A Hierarchical Platform for Fine-Grained Surgical Policy Learning and Benchmarking

Understand

Despite advancements in robotic-assisted surgery, automating complex tasks like suturing remain challenging due to the need for adaptability and precision.

• Learning-based approaches, particularly reinforcement learning (RL) and imitation learning (IL), require realistic simulation environments for efficient data collection.
• However, current platforms often include only relatively simple, non-dexterous manipulations and lack the flexibility required for effective learning and generalization.
• We introduce SurgicAI, a novel platform for development and benchmarking addressing these challenges by providing the flexibility to accommodate both modular subtasks and more importantly task decomposition in RL-based surgical robotics.