Robust Path Planning via Learning from Demonstrations for Robotic Catheters in Deformable Environments
Politecnico di Milano（米兰理工大学）；Delft University of Technology（代尔夫特理工大学）；Istituto Italiano di Tecnologia（意大利技术研究院）；Katholieke Universiteit Leuven（鲁汶大学）；Centro Cardiologico Monzino, IRCCS（蒙奇诺心脏病学研究所）
Zhen Li, Chiara Lambranzi, Di Wu, Alice Segato, Federico De Marco, Emmanuel Vander Poorten, Jenny Dankelman, Elena De Momi
备注：
11 pages, 10 figures
发表信息：
IEEE Transactions on Biomedical Engineering (TBME) 2024
Objective: Navigation through tortuous and deformable vessels using catheters with limited steering capability underscores the need for reliable path planning. State-of-the-art path planners do not fully account for the deformable nature of the environment. Methods: This work proposes a robust path planner via a learning from demonstrations method, named Curriculum Generative Adversarial Imitation Learning (C-GAIL). This path planning framework takes into account the interaction between steerable catheters and vessel walls and the deformable property of vessels. Results: In-silico comparative experiments show that the proposed network achieves a 38% higher success rate in static environments and 17% higher in dynamic environments compared to a state-of-the-art approach based on GAIL. In-vitro validation experiments indicate that the path generated by the proposed C-GAIL path planner achieves a targeting error of 1.26$\pm$0.55mm and a tracking error of 5.18$\pm$3.48mm. These results represent improvements of 41% and 40% over the conventional centerline-following technique for targeting error and tracking error, respectively. Conclusion: The proposed C-GAIL path planner outperforms the state-of-the-art GAIL approach. The in-vitro validation experiments demonstrate that the path generated by the proposed C-GAIL path planner aligns better with the actual steering capability of the pneumatic artificial muscle-driven catheter utilized in this study. Therefore, the proposed approach can provide enhanced support to the user in navigating the catheter towards the target with greater accuracy, effectively meeting clinical accuracy requirements. Significance: The proposed path planning framework exhibits superior performance in managing uncertainty associated with vessel deformation, thereby resulting in lower tracking errors.
Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Germany（慕尼黑机器人与机器智能研究所（MIRMI），慕尼黑技术大学（TUM））；Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE（穆罕默德·本·扎耶德人工智能大学）
We present a novel Learning from Demonstration (LfD) method, Deformable Manipulation from Demonstrations (DMfD), to solve deformable manipulation tasks using states or images as inputs, given expert demonstrations. Our method uses demonstrations in three different ways, and balances the trade-off between exploring the environment online and using guidance from experts to explore high dimensional spaces effectively. We test DMfD on a set of representative manipulation tasks for a 1-dimensional rope and a 2-dimensional cloth from the SoftGym suite of tasks, each with state and image observations…
DINO-CVA: A Multimodal Goal-Conditioned Vision-to-Action Model for Autonomous Catheter Navigation
Gina Cody School of Engineering and Computer Science, Concordia University（甘娜·柯迪工程与计算机科学学院，康科迪亚大学）；The Wilfred and Joyce Posluns Centre for Image Guided Innovation & Therapeutic Intervention (PCIGITI) at the Hospital for Sick Children (SickKids)（威廉与乔伊斯·波斯卢斯影像引导创新与治疗干预中心（PCIGITI）（SickKids医院））；Electrical and Computer Engineering Department, Kettering University（电气与计算机工程系，凯特林大学）
University of Tehran（德黑兰大学）；University of Lincoln（林肯大学）；The Chinese University of Hong Kong（香港中文大学）
Many robotic tasks are still teleoperated since automating them is very time consuming and expensive. Robot Learning from Demonstrations (RLfD) can reduce programming time and cost. However, conventional RLfD approaches are not directly applicable to many robotic tasks, e.g. robotic suturing with minimally invasive robots, as they require a time-consuming process of designing features from visual information. Deep Neural Networks (DNN) have emerged as useful tools for creating complex models capturing the relationship between high-dimensional observation space and low-level action/state space.…
With the field of rigid-body robotics having matured in the last fifty years, routing, planning, and manipulation of deformable objects have recently emerged as a more untouched research area in many fields ranging from surgical robotics to industrial assembly and construction. Routing approaches for deformable objects which rely on learned implicit spatial representations (e.g., Learning-from-Demonstration methods) make them vulnerable to changes in the environment and the specific setup. On the other hand, algorithms that entirely separate the spatial representation of the deformable object …
Taming VR Teleoperation and Learning from Demonstration for Multi-Task Bimanual Table Service Manipulation
AIR, Tsinghua University（空气动力学研究所，清华大学）；Northwestern University（西北大学）；Georgia Institute of Technology（佐治亚理工学院）；HKUST (GZ)（香港科技大学（广州））；NUS (Suzhou) Research Institute（国立大学（苏州）研究所）；University of Michigan（密歇根大学）
Department of Electrical Engineering and Computer Sciences（电气工程与计算机科学系）；Toyota Research Institute（丰田研究所）；Department of Industrial Engineering and Operations Research（工业工程与运筹学系）；University of California, Berkeley（加州大学伯克利分校）
Bed-making is a universal home task that can be challenging for senior citizens due to reaching motions. Automating bed-making has multiple technical challenges such as perception in an unstructured environments, deformable object manipulation, obstacle avoidance and sequential decision making. We explore how DART, an LfD algorithm for learning robust policies, can be applied to automating bed making without fiducial markers with a Toyota Human Support Robot (HSR). By gathering human demonstrations for grasping the sheet and failure detection, we can learn deep neural network policies that lev…
University of Edinburgh（爱丁堡大学）；Monash University（莫纳什大学）
Dynamic Movement Primitives (DMPs) offer great versatility for encoding, generating and adapting complex end-effector trajectories. DMPs are also very well suited to learning manipulation skills from human demonstration. However, the reactive nature of DMPs restricts their applicability for tool use and object manipulation tasks involving non-holonomic constraints, such as scalpel cutting or catheter steering. In this work, we extend the Cartesian space DMP formulation by adding a coupling term that enforces a pre-defined set of non-holonomic constraints. We obtain the closed-form expression f…
In this paper we tackle the problem of deformable object manipulation through model-free visual reinforcement learning (RL). In order to circumvent the sample inefficiency of RL, we propose two key ideas that accelerate learning. First, we propose an iterative pick-place action space that encodes the conditional relationship between picking and placing on deformable objects. The explicit structural encoding enables faster learning under complex object dynamics. Second, instead of jointly learning both the pick and the place locations, we only explicitly learn the placing policy conditioned on …
Robot Deformable Object Manipulation via NMPC-generated Demonstrations in Deep Reinforcement Learning
Hubei Key Laboratory of Brain-inspired Intelligent Systems and the Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（湖北脑启发智能系统重点实验室和图像处理与智能控制重点实验室，人工智能与自动化学院，华中科技大学）
DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning
Shanghai Jiao Tong University（上海交通大学）；Eastern Institute of Technology, Ningbo（宁波东方理工大学）；Tsinghua University（清华大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Science and Technology of China（中国科学技术大学）；Zhongguancun Academy（中关村学院）；Microsoft Research（微软研究院）
DFKI GmbH（德国人工智能研究中心）；Scuola Superiore Sant’Anna（圣安娜高等研究院）；University of Bremen（不来梅大学）
Manipulating objects with robotic hands is a complicated task. Not only the fingers of the hand, but also the pose of the robot's end effector need to be coordinated. Using human demonstrations of movements is an intuitive and data-efficient way of guiding the robot's behavior. We propose a modular framework with an automatic embodiment mapping to transfer recorded human hand motions to robotic systems. In this work, we use motion capture to record human motion. We evaluate our approach on eight challenging tasks, in which a robotic hand needs to grasp and manipulate either deformable or small…
Toward Autonomous Soft Robotic Endovascular Navigation via Imitation Learning
Johns Hopkins University（约翰霍普金斯大学）；Stanford University（斯坦福大学）；McGill University（麦吉尔大学）；University of Maryland（马里兰大学）；Swiss Federal Institute of Technology in Lausanne (EPFL)（日内瓦联邦理工学院（EPFL））；ETH Zurich（苏黎世联邦理工学院）
Politecnico di Milano（米兰理工大学）；Technical University of Munich（慕尼黑工业大学）
Many tasks in robot-assisted surgery require planning and controlling manipulators' motions that interact with highly deformable objects. This study proposes a realistic, time-bounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion for pre-operative path planning and intra-operative guidance in keyhole surgical procedures. It maximizes the probability of success by accounting for uncertainty in deformation models, noisy sensing, and unpredictable actuation. The PBD deformation parameters were initialized on a parallelepiped-sh…
Robotic manipulation of deformable objects gains great attention due to its wide applications including medical surgery, home assistance, and automatic food preparation. The ability to deform soft objects remains a great challenge for robots due to difficulties in defining the problem mathematically. In this paper, we address the problem of shaping a piece of dough-like deformable material into a 2D target shape presented upfront. We use a 6 degree-of-freedom WidowX-250 Robot Arm equipped with a rolling pin and information collected from an RGB-D camera and a tactile sensor. We present and com…
University of California, Berkeley（加州大学伯克利分校）；Honda Research Institute（本田研究院）
Robotic fabric manipulation is challenging due to the infinite dimensional configuration space, self-occlusion, and complex dynamics of fabrics. There has been significant prior work on learning policies for specific deformable manipulation tasks, but comparatively less focus on algorithms which can efficiently learn many different tasks. In this paper, we learn visual correspondences for deformable fabrics across different configurations in simulation and show that this representation can be used to design policies for a variety of tasks. Given a single demonstration of a new task from an ini…
A Position Statement on Endovascular Models and Effectiveness Metrics for Mechanical Thrombectomy Navigation, on behalf of the Stakeholder Taskforce for AI-assisted Robotic Thrombectomy (START)
on behalf of the Stakeholder Taskforce for AI-assisted Robotic Thrombectomy (START)（人工智能辅助机器人取栓任务组成员）
Learning-based Cooperative Robotic Paper Wrapping: A Unified Control Policy with Residual Force Control
Department of Systems Innovation, Graduate School of Engineering Science, Osaka University（大阪大学系统创新部门，工学研究科）；OMRON SINIC X Corporation（OMRON SINIC X公司）；The National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）
University of California San Diego（加利福尼亚大学圣地亚哥分校）；Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）
Traditional control and task automation have been successfully demonstrated in a variety of structured, controlled environments through the use of highly specialized modeled robotic systems in conjunction with multiple sensors. However, the application of autonomy in endoscopic surgery is very challenging, particularly in soft tissue work, due to the lack of high-quality images and the unpredictable, constantly deforming environment. In this work, we propose a novel surgical perception framework, SuPer, for surgical robotic control. This framework continuously collects 3D geometric information…
