arXiv:2606. 29247v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models represent a promising direction for embodied intelligence in surgical robotics.
arXiv:2608. 11204v1 Announce Type: cross Abstract: Learning reliable surgical manipulation policies is bottlenecked by the scarcity of action-labeled demonstrations: teleoperated surgical robot (e.
arXiv:2604. 21017v3 Announce Type: replace-cross Abstract: Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision.
The paper proposes a method for distilling a vision‑based policy from a state‑based teacher policy in laparoscopic robotic manipulation. By using privileged state observations as a teacher, the vision policy learns from RGB images and proprioception, requiring fewer expert demonstrations than imitation learning and outperforming pure reinforcement learning. Experiments in simulation show that this distillation approach yields more consistent and stable performance across various surgical tasks.
AtomicVLA is a unified planning-and-execution framework that generates task-level plans, atomic skill abstractions, and fine-grained actions for robotic manipulation. It builds a scalable atomic skill library using a Skill‑Guided Mixture‑of‑Experts (SG‑MoE) and a flexible routing encoder that assigns new skills to dedicated experts, enabling continual learning. Experiments show that AtomicVLA outperforms baseline models on both simulated and real‑world long‑horizon tasks, achieving significant improvements in task performance and learning efficiency.
arXiv:2510. 14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks successfully.
arXiv:2603.29962v4 Announce Type: replace Abstract: Surgical procedures are inherently complex and risky, requiring extensive expertise and constant focus to navigate evolving intraoperative scenes....
arXiv:2608. 07876v1 Announce Type: new Abstract: Autonomous laparoscopic camera control requires continuous understanding of the surgeon's operative intent in dynamic surgical scenes, where the target operative region is not a stable physical object but a latent and temporally evolving attention state.
arXiv:2610.34210v1 Announce Type: cross Abstract: Modern surgical AI increasingly requires multimodal understanding rather than isolated perception, yet existing datasets remain largely limited to si...
SurgAtlas is the largest surgical video‑language dataset, containing 15,291 videos (2,391 hours) across 18 specialties and over 5,000 procedure types, all sourced from public YouTube. It uniquely includes open‑surgery videos at scale (6,182) alongside more than 9,000 minimally invasive recordings, and introduces standardized benchmarks for open‑surgery video understanding. The dataset offers a rich, multi‑tier annotation schema—segment‑level captions, step/phase descriptions, video‑level surgical narratives, and reasoning‑oriented VQA pairs—validated by experts and built through an automated LLM‑enriched pipeline. "whyItMatters":"SurgAtlas provides an unprecedentedly large, diverse, and clinically validated resource that can train and benchmark multimodal surgical AI models, advancing the development of next‑generation foundation models for surgery."
The paper proposes a neuro‑symbolic framework that augments vision‑language‑action (VLA) models with explicit task graphs and multimodal procedural memory to handle long‑horizon manipulation tasks. Task graphs encode action dependencies, valid transitions, and branch conditions, while memory tracks the active step, completed actions, textual context, and relevant visual evidence. Human demonstrations provide spatial and temporal guidance via gaze or saliency cues, which are annotated in robot‑view teleoperation videos and used to fine‑tune VLA models. The approach is evaluated on workspace clearing and surgical‑instrument handling tasks, measuring object and destination selection, subtask completion, task progress, step‑order consistency, overall success, and procedural or execution mistakes.
arXiv:2608. 05970v1 Announce Type: cross Abstract: Embodied visuomotor models, including Diffusion Policy (DP) and Vision-Language-Action (VLA) models, have demonstrated promising performance on robotic manipulation benchmarks.