For tasks where the dynamics of multiple agents are physically coupled, e.g., in cooperative manipulation, the coordination between the individual agents becomes crucial, which requires exact knowledge of the interaction dynamics. This problem is typically addressed using centralized estimators, which can negatively impact the flexibility and robustness of the overall system. To overcome this shortcoming, we propose a novel distributed learning framework for the exemplary task of cooperative manipulation using Bayesian principles. Using only local state information each agent obtains an estimate of the object dynamics and grasp kinematics. These local estimates are combined using dynamic average consensus. Due to the strong probabilistic foundation of the method, each estimate of the object dynamics and grasp kinematics is accompanied by a measure of uncertainty, which allows to guarantee a bounded prediction error with high probability. Moreover, the Bayesian principles directly allow iterative learning with constant complexity, such that the proposed learning method can be used online in real-time applications. The effectiveness of the approach is demonstrated in a simulated cooperative manipulation task.
University of Colorado Boulder（科罗拉多大学博尔德分校）；Vrije Universiteit Brussel（布鲁塞尔自由大学）；Flanders Make（佛兰德斯制造）
We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate the proposed approaches in the setting of cooperative object manipulation with two simulated robot arms. Although we focus on a small system of two agents in this paper, both DA-RL and GT-RL app…
This paper addresses the problem of cooperative manipulation of a single object by N robotic agents under local goal specifications given as Metric Interval Temporal Logic (MITL) formulas. In particular, we propose a distributed model-free control protocol for the trajectory tracking of the cooperatively manipulated object without necessitating feedback of the contact forces/torques or inter-agent communication. This allows us to abstract the motion of the coupled object-agents system as a finite transition system and, by employing standard automata-based methodologies, we derive a hybrid cont…
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 …
HCLM: A Hierarchical Framework for Cooperative Loco-Manipulation with Dual Quadrupeds
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China（深圳国际研究生院，清华大学，深圳，中国）；Department of Electronic Engineering, and the Institute for Embodied Intelligence and Robotics, Tsinghua University, Beijing, China（电子工程系，以及 embodied intelligence and robotics 院，清华大学，北京，中国）
SynAgent: Generalizable Cooperative Humanoid Manipulation via Solo-to-Cooperative Agent Synergy
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Department of Automation, Tsinghua University（清华大学自动化系）；College of Artificial Intelligence, Nanjing Forestry University（南京林业大学人工智能学院）
RoboDuet: Learning a Cooperative Policy for Whole-body Legged Loco-Manipulation
Tsinghua University（清华大学）；University of San Diego（圣地亚哥大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Shanghai Qi Zhi Institute（上海期智研究院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Jianghuai Advance Technology Center（江淮先进技术中心）
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…
VLM-SFD: VLM-Assisted Siamese Flow Diffusion Framework for Dual-Arm Cooperative Manipulation
Department of Computer Science, The University of Manchester（计算机科学系，曼彻斯特大学）；School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）
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…
Italian Institute of Technology（意大利技术研究院）；Politecnico di Milano（米兰理工大学）；Xi’an Jiaotong University（西安交通大学）；Hanyang University ERICA（汉阳大学ERICA校区）
This article proposes a method for learning and robotic replication of dynamic collaborative tasks from offline videos. The objective is to extend the concept of learning from demonstration (LfD) to dynamic scenarios, benefiting from widely available or easily producible offline videos. To achieve this goal, we decode important dynamic information, such as the Configuration Dependent Stiffness (CDS), which reveals the contribution of arm pose to the arm endpoint stiffness, from a three-dimensional human skeleton model. Next, through encoding of the CDS via Gaussian Mixture Model (GMM) and deco…
We present a modular Bayesian optimization framework that efficiently generates time-optimal trajectories for a cooperative multi-agent system, such as a team of UAVs. Existing methods for multi-agent trajectory generation often rely on overly conservative constraints to reduce the complexity of this high-dimensional planning problem, leading to suboptimal solutions. We propose a novel modular structure for the Bayesian optimization model that consists of multiple Gaussian process surrogate models that represent the dynamic feasibility and collision avoidance constraints. This modular structur…
Carnegie Mellon University（卡内基梅隆大学）；Stanford University（斯坦福大学）；NVIDIA（英伟达）；University of Washington（华盛顿大学）；The University of Sydney（悉尼大学）
Deformable object manipulation remains a challenging task in robotics research. Conventional techniques for parameter inference and state estimation typically rely on a precise definition of the state space and its dynamics. While this is appropriate for rigid objects and robot states, it is challenging to define the state space of a deformable object and how it evolves in time. In this work, we pose the problem of inferring physical parameters of deformable objects as a probabilistic inference task defined with a simulator. We propose a novel methodology for extracting state information from …
Tsinghua University（清华大学）；UC Berkeley（加州大学伯克利分校）；UC San Diego（加州大学圣地亚哥分校）；Shanghai Qi Zhi Institute（上海期智研究院）
We address the problem of safely solving complex bimanual robot manipulation tasks with sparse rewards. Such challenging tasks can be decomposed into sub-tasks that are accomplishable by different robots concurrently or sequentially for better efficiency. While previous reinforcement learning approaches primarily focus on modeling the compositionality of sub-tasks, two fundamental issues are largely ignored particularly when learning cooperative strategies for two robots: (i) domination, i.e., one robot may try to solve a task by itself and leaves the other idle; (ii) conflict, i.e., one robot…
University of Padova（帕多瓦大学）；ETH Zurich（苏黎世联邦理工学院）；University of Twente（特文特大学）；LAAS-CNRS；Université de Toulouse（图卢兹大学）；CNRS（法国国家科学研究中心）
In this work we consider the problem of mobile robots that need to manipulate/transport an object via cables or robotic arms. We consider the scenario where the number of manipulating robots is redundant, i.e. a desired object configuration can be obtained by different configurations of the robots. The objective of this work is to show that communication can be used to implement cooperative local feedback controllers in the robots to improve disturbance rejection and reduce structural stress in the object. In particular we consider the realistic scenario where measurements are sampled and tran…
PA-BiCoop: A Primary-Auxiliary Cooperative Framework for General Bimanual Manipulation
SGIT AI Lab, State Grid Corporation of China（国家电网公司SGIT人工智能实验室）；The Hong Kong University of Science and Technology, Guangzhou（香港科技大学（广州））；Baidu Research, Beijing, China（百度研究院）；Zhejiang University of Technology, Hangzhou（浙江工业大学）
In this work we investigate and demonstrate benefits of a Bayesian approach to imitation learning from multiple sensor inputs, as applied to the task of opening office doors with a mobile manipulator. Augmenting policies with additional sensor inputs, such as RGB + depth cameras, is a straightforward approach to improving robot perception capabilities, especially for tasks that may favor different sensors in different situations. As we scale multi-sensor robotic learning to unstructured real-world settings (e.g. offices, homes) and more complex robot behaviors, we also increase reliance on sim…
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty
Oxford Robotics Institute, Dept. Engineering Science, University of Oxford, UK（牛津大学机器人研究所）；Microsoft Research, Redmond, WA, USA（微软研究院）；Bristol Robotics Laboratory, School of Engineering, University of the West of England, Bristol, UK（布里斯托尔机器人实验室）
KTH Royal Institute of Technology（KTH皇家理工学院）；School of Electrical Engineering and Computer Science（电气工程与计算机科学学院）
Control and planning of multi-agent systems is an active and increasingly studied topic of research, with many practical applications such as rescue missions, security, surveillance, and transportation. This thesis addresses the planning and control of multi-agent systems under temporal logic tasks. The considered systems concern complex, robotic, manipulator-endowed systems, which can coordinate in order to execute complicated tasks, including object manipulation/transportation. Motivated by real-life scenarios, we take into account high-order dynamics subject to model uncertainties and unkno…
To perform dynamic cable manipulation to realize the configuration specified by a target image, we formulate dynamic cable manipulation as a stochastic forward model. Then, we propose a method to handle uncertainty by maximizing the expectation, which also considers estimation errors of the trained model. To avoid issues like multiple local minima and requirement of differentiability by gradient-based methods, we propose using a black-box optimization (BBO) to optimize joint angles to realize a goal image. Among BBO, we use the Tree-structured Parzen Estimator (TPE), a type of Bayesian optimiz…
Department of Electromechanical, Systems and Metal Engineering, Ghent University（根特大学机电、系统与金属工程系）；Core Lab MIRO, Flanders Make Strategic Research Centre for the Manufacturing Industry（弗拉芒制造战略研究中心MIRO核心实验室）
We introduce a framework for cooperative manipulation, applied on an underactuated manipulation problem. Two stationary robotic manipulators are required to cooperate in order to reposition an object within their shared work space. Control of multi-agent systems for manipulation tasks cannot rely on individual control strategies with little to no communication between the agents that serve the common objective through swarming. Instead a coordination strategy is required that queries subtasks to the individual agents. We formulate the problem in a Task And Motion Planning (TAMP) setting, while…
The ability to communicate intention enables decentralized multi-agent robots to collaborate while performing physical tasks. In this work, we present spatial intention maps, a new intention representation for multi-agent vision-based deep reinforcement learning that improves coordination between decentralized mobile manipulators. In this representation, each agent's intention is provided to other agents, and rendered into an overhead 2D map aligned with visual observations. This synergizes with the recently proposed spatial action maps framework, in which state and action representations are …
Cooperative Payload Estimation by a Team of Mocobots
Department of Mechanical Engineering, Center for Robotics and Biosystems, Northwestern University（机械工程系、机器人与生物系统中心、西北大学）；Center for Robotics and Biosystems, Department of Electrical and Computer Engineering, Northwestern Institute on Complex Systems, Northwestern University（机器人与生物系统中心、电气与计算机工程系、复杂系统研究所、西北大学）；Department of Mechanical Engineering, Center for Robotics and Biosystems, Northwestern Institute on Complex Systems, Northwestern University（机械工程系、机器人与生物系统中心、复杂系统研究所、西北大学）
Cooperative Modular Manipulation with Numerous Cable-Driven Robots for Assistive Construction and Gap Crossing
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；US Army Corps of Engineers Construction Engineering Research Laboratory（美国陆军工兵团建筑工程研究实验室）；Istituto Italiano di Tecnologia (IIT)（意大利技术研究院）；Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校机械科学与工程学院）
Cooperative Long Rope Skipping via Multi-Agent Reinforcement Learning
National Key Laboratory of Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；Beijing Academy of Artificial Intelligence, BAAI（北京智源人工智能研究院）
Mobile manipulation in robotics is challenging due to the need of solving many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-principles system description of the robot is available, thus motivating the use of model-based controllers. However, the robot dynamics and its interaction with an object are affected by uncertainty, limiting the controller's performance. To tackle this problem, we propose a Bayesian multi-task learning model that uses trigonometric basis functions to identify the error in the dynamics. In this way, data from different …
University of California San Diego（加州大学圣地亚哥分校）；MIT Lincoln Laboratory（麻省理工学院林肯实验室）
Location-aware networks will introduce new services and applications for modern convenience, surveillance, and public safety. In this paper, we consider the problem of cooperative localization in a wireless network where the position of certain anchor nodes can be controlled. We introduce an active planning method that aims at moving the anchors such that the information gain of future measurements is maximized. In the control layer of the proposed method, control inputs are calculated by minimizing the traces of approximate inverse Bayesian Fisher information matrixes (FIMs). The estimation l…
Humans demonstrate a variety of interesting behavioral characteristics when performing tasks, such as selecting between seemingly equivalent optimal actions, performing recovery actions when deviating from the optimal trajectory, or moderating actions in response to sensed risks. However, imitation learning, which attempts to teach robots to perform these same tasks from observations of human demonstrations, often fails to capture such behavior. Specifically, commonly used learning algorithms embody inherent contradictions between the learning assumptions (e.g., single optimal action) and actu…
Chalmers University of Technology（查尔姆斯理工大学）；University College London（伦敦大学学院）
In this paper, we propose to use a nonlinear adaptive PID controller to regulate the joint variables of a mobile manipulator. The motion of the mobile base forces undue disturbances on the joint controllers of the manipulator. In designing a conventional PID controller, one should make a trade-off between the performance and agility of the closed-loop system and its stability margins. The proposed nonlinear adaptive PID controller provides a mechanism to relax the need for such a compromise by adapting the gains according to the magnitude of the error without expert tuning. Therefore, we can a…
Agile and Cooperative Aerial Manipulation of a Cable-Suspended Load
Delft University of Technology（代尔夫特理工大学）；Sorbonne Université（索邦大学）；University of Twente（埃因霍温理工大学）；Sapienza University of Rome（罗马萨皮恩扎大学）；Univ Rennes, CNRS, Inria, IRISA, France（里昂大学，CNRS，Inria，IRISA，法国）
Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室（MERL））；Northeastern University（东北大学）
One of the most essential prerequisites behind a successful task execution of a team of agents is to accurately estimate and track their poses. We consider a cooperative multi-agent positioning problem where each agent performs single-agent positioning until it encounters some other agent. Upon the encounter, the two agents measure their relative pose, and exchange particle clouds representing their poses. We propose a cooperative positioning algorithm which fuses the received information with the locally available measurements and infers an agent's pose within Bayesian framework. The algorith…
Many robot manipulation skills can be represented with deterministic characteristics and there exist efficient techniques for learning parameterized motor plans for those skills. However, one of the active research challenge still remains to sustain manipulation capabilities in situation of a mechanical failure. Ideally, like biological creatures, a robotic agent should be able to reconfigure its control policy by adapting to dynamic adversaries. In this paper, we propose a method that allows an agent to survive in a situation of mechanical loss, and adaptively learn manipulation with compromi…
At present, the research on robot team cooperation is still in qualitative analysis phase and lacks the description model that can quantitatively describe the dynamical evolution of team cooperative relationships with constantly changeable task demand in Multi-robot field. First this paper whole and static describes organization model HWROM of robot team, then uses Markov course and Bayesian theorem for reference, dynamical describes the team cooperative relationships building. Finally from cooperative entity layer, ability layer and relative layer we research team formation and cooperative me…
