arXiv AI
SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 30
SurgVLA-Bench: Towards Evaluating Vision-Language-Action Models for Laparoscopic Surgical Robotics
arXiv:2606. 29247v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models represent a promising direction for embodied intelligence in surgical robotics.
arXiv AI
Aug 12
Surgical WAM: A World-Action Model for Data-Efficient Surgical Robot Learning
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 AI
Jun 6
Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics
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.
arXiv Computer Vision
Oct 23
Revisiting Distribution Matching for State-to-Vision Policy Distillation in Laparoscopic Robotic Manipulation
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.
arXiv AI
Sep 10
AtomicVLA: Unlocking the Potential of Atomic Skill Learning in Robots
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 AI
Jun 10
RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning
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.