In marine operations underwater manipulators play a primordial role. However, due to uncertainties in the dynamic model and disturbances caused by the environment, low-level control methods require great capabilities to adapt to change. Furthermore, under position and torque constraints the requirements for the control system are greatly increased. Reinforcement learning is a data driven control technique that can learn complex control policies without the need of a model. The learning capabilities of these type of agents allow for great adaptability to changes in the operative conditions. In this article we present a novel reinforcement learning low-level controller for the position control of an underwater manipulator under torque and position constraints. The reinforcement learning agent is based on an actor-critic architecture using sensor readings as state information. Simulation results using the Reach Alpha 5 underwater manipulator show the advantages of the proposed control strategy.
Underwater manipulation is one of the most remarkable ongoing research subjects in robotics. \acp{I-AUV} not only have to cope with the technical challenges associated with traditional manipulation tasks but do so while currents and waves perturb the stability of the vehicle, and low-light, turbid water conditions impede perceiving the surroundings. Certainly, the dynamic nature and our limited understanding of the marine environment hinder the autonomous performance of underwater robot manipulation. This manuscript provides a discussion on previous research and the limiting factors that impos…
The underwater environment poses a complex problem for developing autonomous capabilities for Underwater Vehicle Manipulator Systems (UVMSs). The modeling of UVMSs is a complicated and costly process due to the highly nonlinear dynamics and the presence of unknown hydrodynamical effects. This is aggravated in tasks where the manipulation of objects is necessary, as this may not only introduce external disturbances that can lead to a fast degradation of the control system performance, but also requires the coordinating with a vision system for the correct grasping and operation of the object. I…
WasserMan: Benchmark for Underwater Manipulation Policy Learning
Central University（中央大学）；Center for Engineering Systems and Sciences（工程系统与科学中心）；Sirius University of Science and Technology（天狼星科技大学）；V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences（俄罗斯科学院特拉佩兹尼科夫控制科学研究所）
University of Guelph（圭尔夫大学）；McMaster University（麦克马斯特大学）
Tracking control has been a vital research topic in robotics. This paper presents a novel hybrid control strategy for an unmanned underwater vehicle (UUV) based on a bioinspired neural dynamics model. An enhanced backstepping kinematic control strategy is first developed to avoid sharp velocity jumps and provides smooth velocity commands relative to conventional methods. Then, a novel sliding mode control is proposed, which is capable of providing smooth and continuous torque commands free from chattering. In comparative studies, the proposed combined hybrid control strategy has ensured contro…
An intelligent control strategy is proposed to eliminate the actuator saturation problem that exists in the trajectory tracking process of unmanned underwater vehicles (UUV). The control strategy consists of two parts: for the kinematic modeling part, a fuzzy logic-refined backstepping control is developed to achieve control velocities within acceptable ranges and errors of small fluctuations; on the basis of the velocities deducted by the improved kinematic control, the sliding mode control (SMC) is introduced in the dynamic modeling to obtain corresponding torques and forces that should be a…
This paper addresses the problem of cooperative object transportation for multiple Underwater Vehicle Manipulator Systems (UVMSs) in a constrained workspace with static obstacles, where the coordination relies solely on implicit communication arising from the physical interaction of the robots with the commonly grasped object. We propose a novel distributed leader-follower architecture, where the leading UVMS, which has knowledge of the object's desired trajectory, tries to achieve the desired tracking behavior via an impedance control law, navigating in this way, the overall formation towards…
This paper addresses the problem of cooperative object transportation for multiple Underwater Vehicle Manipulator Systems (UVMSs) in a constrained workspace involving static obstacles. We propose a Nonlinear Model Predictive Control (NMPC) approach for a team of UVMSs in order to transport an object while avoiding significant constraints and limitations such as: kinematic and representation singularities, obstacles within the workspace, joint limits and control input saturations. More precisely, by exploiting the coupled dynamics between the robots and the object, and using certain load sharin…
Manipulider: A Multi-Engine Buoyancy-Controlled Robot for Thrusterless Underwater Gliding and Manipulation
Dept. of Computer Science, Dartmouth College（达特茅斯学院计算机科学系）；Dept. of Mechanical and Aerospace Engineering, University of Houston（休斯顿大学机械与航空航天工程系）；Dept. of Aerospace and Ocean Engineering, Virginia Tech（弗吉尼亚理工大学航空航天与海洋工程系）；Dept. of Electrical and Computer Engineering, Clemson University（克莱姆森大学电气与计算机工程系）
University of Guelph（圭尔夫大学）；China Ship Scientific Research Center（中国船舶科学研究中心）
This paper proposes an intelligent fault-tolerant control (FTC) strategy to tackle the trajectory tracking problem of an underwater vehicle (UV) under thruster damage (power loss) cases and meanwhile resolve the actuator saturation brought by the vehicle's physical constraints. In the proposed control strategy, the trajectory tracking component is formed by a refined backstepping algorithm that controls the velocity variation and a sliding mode control deducts the torque/force outputs; the fault-tolerant component is established based on a Grasshopper Optimization Algorithm (GOA), which provid…
ICM-CSIC（西班牙加泰罗尼亚海洋科学研究所）；Khalifa University（哈利法大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；KTH Royal Institute of Technology（皇家理工学院）；Scuola Superiore Sant’Anna（圣安娜高等学院）
Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（德国航空航天中心（DLR）机器人与机电一体化研究所）；Deggendorf Institute of Technology（代根多夫应用技术大学）
We show that a purely tactile dextrous in-hand manipulation task with continuous regrasping, requiring permanent force closure, can be learned from scratch and executed robustly on a torque-controlled humanoid robotic hand. The task is rotating a cube without dropping it, but in contrast to OpenAI's seminal cube manipulation task, the palm faces downwards and no cameras but only the hand's position and torque sensing are used. Although the task seems simple, it combines for the first time all the challenges in execution as well as learning that are important for using in-hand manipulation in r…
Learning When to See and When to Feel: Adaptive Vision-Torque Fusion for Contact-Aware Manipulation
Edwardson School of Industrial Engineering, Purdue University（普渡大学爱德华森工业工程学院）；Zachry Department of Civil and Environmental Engineering, Texas A&M University（德州农工大学扎克里土木与环境工程系）
This paper proposes a task-space control protocol for the collaborative manipulation of a single object by N robotic agents. The proposed methodology is decentralized in the sense that each agent utilizes information associated with its own and the object's dynamic/kinematic parameters and no on-line communication takes place. Moreover, no feedback of the contact forces/torques is required, therefore employment of corresponding sensors is avoided. An adaptive version of the control scheme is also introduced, where the agents' and object's dynamic parameters are considered unknown. We also use …
Australian Centre for Field Robotics（澳大利亚现场机器人中心）；University of Sydney（悉尼大学）
This paper presents methods for finding optimal configurations and actuator forces/torques to maximise contact wrenches in a desired direction for Underwater Vehicles Manipulator Systems (UVMS). The wrench maximisation problem is formulated as a linear programming problem, and the optimal configuration is solved as a bi-level optimisation in the parameterised redundancy space. We additionally consider the cases of one or more manipulators with multiple contact forces, maximising wrench capability while tracking a trajectory, and generating large wrench impulses using dynamic motions. We look a…
University of Michigan（密歇根大学）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）；University of Sydney（悉尼大学）；Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）
In this report, we present the system design, operational strategy, and results of coordinated multi-vehicle field demonstrations of autonomous marine robotic technologies in search-for-life missions within the Pacific shelf margin of Costa Rica and the Santorini-Kolumbo caldera complex, which serve as analogs to environments that may exist in oceans beyond Earth. This report focuses on the automation of ROV manipulator operations for targeted biological sample-collection-and-return from the seafloor. In the context of future extraterrestrial exploration missions to ocean worlds, an ROV is an …
Toward Gripper-Integrated Active Electrosense for Pre-Contact Sensing in Underwater Soft Grippers
Intelligent Biomimetic Design Lab, School of Advanced Manufacturing and Robotics, State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University（智能仿生设计实验室，先进制造与机器人学院，湍流与复杂系统国家重点实验室，北京大学）；National Engineering Research Center of Software Engineering, Peking University（软件工程国家工程研究中心，北京大学）；Institute of Ocean Research, Peking University（海洋研究所，北京大学）
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
Precision cutting of soft-tissue remains a challenging problem in robotics, due to the complex and unpredictable mechanical behaviour of tissue under manipulation. Here, we consider the challenge of cutting along the boundary between two soft mediums, a problem that is made extremely difficult due to visibility constraints, which means that the precise location of the cutting trajectory is typically unknown. This paper introduces a novel strategy to address this task, using a binary medium classifier trained using joint torque measurements, and a closed loop control law that relies on an error…
Fine Robotic Manipulation without Force/Torque Sensor
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与宇航工程学院）；Eureka Robotics（尤里卡机器人公司）；Singapore Centre for 3D Printing (SC3DP)（新加坡3D打印中心）
TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models
Beijing Academy of Artificial Intelligence, BAAI（北京人工智能研究院）；Institute for AI Industry Research (AIR), Tsinghua Univeristy（清华大学人工智能产业研究院）；Nanyang Technological University（南洋理工大学）
Adaptive Wiping: Adaptive contact-rich manipulation through few-shot imitation learning with Force-Torque feedback and pre-trained object representations
Department of Mechano-Informatics, The University of Tokyo（东京大学机械信息学系）；National Institute of Advanced Industrial Science and Technology（国家先进工业科学与技术研究院）；Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology（东京农业大学机械系统工程系）；Department of Mechanical Engineering, The University of Tokyo（东京大学机械工程系）
Full-Body Torque-Level Non-linear Model Predictive Control for Aerial Manipulation
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学工业机器人与信息技术研究所）；NASA-Jet Propulsion Laboratory, California Institute of Technology（美国国家航空航天局喷气推进实验室，加州理工学院）
The study of dexterous manipulation has provided important insights in humans sensorimotor control as well as inspiration for manipulation strategies in robotic hands. Previous work focused on experimental environment with restrictions. Here we describe a method using the deformation and color distribution of the fingernail and its surrounding skin, to estimate the fingertip forces, torques and contact surface curvatures for various objects, including the shape and material of the contact surfaces and the weight of the objects. The proposed method circumvents limitations associated with sensor…
Harvard University（哈佛大学）；School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）
Designing reinforcement learning (RL) problems that can produce delicate and precise manipulation policies requires careful choice of the reward function, state, and action spaces. Much prior work on applying RL to manipulation tasks has defined the action space in terms of direct joint torques or reference positions for a joint-space proportional derivative (PD) controller. In practice, it is often possible to add additional structure by taking advantage of model-based controllers that support both accurate positioning and control of the dynamic response of the manipulator. In this paper, we …
Carnegie Mellon University（卡内基梅隆大学）；Intrinsic（Intrinsic（谷歌旗下机器人公司））；Arizona State University（亚利桑那州立大学）
In this paper, we discuss a framework for teaching bimanual manipulation tasks by imitation. To this end, we present a system and algorithms for learning compliant and contact-rich robot behavior from human demonstrations. The presented system combines insights from admittance control and machine learning to extract control policies that can (a) recover from and adapt to a variety of disturbances in time and space, while also (b) effectively leveraging physical contact with the environment. We demonstrate the effectiveness of our approach using a real-world insertion task involving multiple si…
Robotic manipulation of tethered tools is widely seen in robotic work cells. They may cause excess strain on the tool's cable or undesired entanglements with the robot's arms. This paper presents a manipulation planner with cable orientation constraints for tethered tools suspended by tool balancers. The planner uses orientation constraints to limit the bending of the balancer's cable while the robot manipulates a tool and places it in a desired pose. The constraints reduce entanglements and decrease the torque induced by the cable on the robot joints. Simulation and real-world experiments sho…
