Efficient Physically-based Simulation of Soft Bodies in Embodied Environment for Surgical Robot

Surgical robot simulation platform plays a crucial role in enhancing training efficiency and advancing research on robot learning. Much effort have been made by scholars on developing open-sourced surgical robot simulators to facilitate research. We also developed SurRoL formerly, an open-source, da Vinci Research Kit (dVRK) compatible and interactive embodied environment for robot learning. Despite its advancements, the simulation of soft bodies still remained a major challenge within the open-source platforms available for surgical robotics. To this end, we develop an interactive physically based soft body simulation framework and integrate it to SurRoL. Specifically, we utilized a high-performance adaptation of the Material Point Method (MPM) along with the Neo-Hookean model to represent the deformable tissue. Lagrangian particles are used to track the motion and deformation of the soft body throughout the simulation and Eulerian grids are leveraged to discretize space and facilitate the calculation of forces, velocities, and other physical quantities. We also employed an efficient collision detection and handling strategy to simulate the interaction between soft body and rigid tool of the surgical robot. By employing the Taichi programming language, our implementation harnesses parallel computing to boost simulation speed. Experimental results show that our platform is able to simulate soft bodies efficiently with strong physical interpretability and plausible visual effects. These new features in SurRoL enable the efficient simulation of surgical tasks involving soft tissue manipulation and pave the path for further investigation of surgical robot learning. The code will be released in a new branch of SurRoL github repo.

Value-Informed Skill Chaining for Policy Learning of Long-Horizon Tasks with Surgical Robot

Reinforcement learning is still struggling with solving long-horizon surgical robot tasks which involve multiple steps over an extended duration of time due to the policy exploration challenge. Recent methods try to tackle this problem by skill chaining, in which the long-horizon task is decomposed into multiple subtasks for easing the exploration burden and subtask policies are temporally connected to complete the whole long-horizon task. However, smoothly connecting all subtask policies is difficult for surgical robot scenarios. Not all states are equally suitable for connecting two adjacent subtasks. An undesired terminate state of the previous subtask would make the current subtask policy unstable and result in a failed execution. In this work, we introduce value-informed skill chaining (ViSkill), a novel reinforcement learning framework for long-horizon surgical robot tasks. The core idea is to distinguish which terminal state is suitable for starting all the following subtask policies. To achieve this target, we introduce a state value function that estimates the expected success probability of the entire task given a state. Based on this value function, a chaining policy is learned to instruct subtask policies to terminate at the state with the highest value so that all subsequent policies are more likely to be connected for accomplishing the task. We demonstrate the effectiveness of our method on three complex surgical robot tasks from SurRoL, a comprehensive surgical simulation platform, achieving high task success rates and execution efficiency. Code is available at $\href{https://github.com/med-air/ViSkill}{\text{https://github.com/med-air/ViSkill}}$.

Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning

Surgical robot automation has attracted increasing research interest over the past decade, expecting its huge potential to benefit surgeons, nurses and patients. Recently, the learning paradigm of embodied AI has demonstrated promising ability to learn good control policies for various complex tasks, where embodied AI simulators play an essential role to facilitate relevant researchers. However, existing open-sourced simulators for surgical robot are still not sufficiently supporting human interactions through physical input devices, which further limits effective investigations on how human demonstrations would affect policy learning. In this paper, we study human-in-the-loop embodied intelligence with a new interactive simulation platform for surgical robot learning. Specifically, we establish our platform based on our previously released SurRoL simulator with several new features co-developed to allow high-quality human interaction via an input device. With these, we further propose to collect human demonstrations and imitate the action patterns to achieve more effective policy learning. We showcase the improvement of our simulation environment with the designed new features and tasks, and validate state-of-the-art reinforcement learning algorithms using the interactive environment. Promising results are obtained, with which we hope to pave the way for future research on surgical embodied intelligence. Our platform is released and will be continuously updated in the website: https://med-air.github.io/SurRoL/

CenterLoc3D: Monocular 3D Vehicle Localization Network for Roadside Surveillance Cameras

Monocular 3D vehicle localization is an important task in Intelligent Transportation System (ITS) and Cooperative Vehicle Infrastructure System (CVIS), which is usually achieved by monocular 3D vehicle detection. However, depth information cannot be obtained directly by monocular cameras due to the inherent imaging mechanism, resulting in more challenging monocular 3D tasks. Most of the current monocular 3D vehicle detection methods leverage 2D detectors and additional geometric modules, which reduces the efficiency. In this paper, we propose a 3D vehicle localization network CenterLoc3D for roadside monocular cameras, which directly predicts centroid and eight vertexes in image space, and the dimension of 3D bounding boxes without 2D detectors. To improve the precision of 3D vehicle localization, we propose a weighted-fusion module and a loss with spatial constraints embedded in CenterLoc3D. Firstly, the transformation matrix between 2D image space and 3D world space is solved by camera calibration. Secondly, vehicle type, centroid, eight vertexes, and the dimension of 3D vehicle bounding boxes are obtained by CenterLoc3D. Finally, centroid in 3D world space can be obtained by camera calibration and CenterLoc3D for 3D vehicle localization. To the best of our knowledge, this is the first application of 3D vehicle localization for roadside monocular cameras. Hence, we also propose a benchmark for this application including a dataset (SVLD-3D), an annotation tool (LabelImg-3D), and evaluation metrics. Through experimental validation, the proposed method achieves high accuracy and real-time performance.