One unresolved issue is how to scale model-based inverse reinforcement learning (IRL) to actual robotic manipulation tasks with unpredictable dynamics. The ability to learn from both visual and proprioceptive examples, creating algorithms that scale to high-dimensional state-spaces, and mastering strong dynamics models are the main obstacles. In this work, we provide a gradient-based inverse reinforcement learning framework that learns cost functions purely from visual human demonstrations. The shown behavior and the trajectory is then optimized using TD visual model predictive control(MPC) and the learned cost functions. We test our system using fundamental object manipulation tasks on hardware.
This paper presents a reactive controller for planar manipulation tasks that leverages machine learning to achieve real-time performance. The approach is based on a Model Predictive Control (MPC) formulation, where the goal is to find an optimal sequence of robot motions to achieve a desired object motion. Due to the multiple contact modes associated with frictional interactions, the resulting optimization program suffers from combinatorial complexity when tasked with determining the optimal sequence of modes. To overcome this difficulty, we formulate the search for the optimal mode sequences …
Imperial College London（伦敦帝国理工学院）；University of Pennsylvania（宾夕法尼亚大学）
Robotic dual-arm twisting is a common but very challenging task in both industrial production and daily services, as it often requires dexterous collaboration, a large scale of end-effector rotating, and good adaptivity for object manipulation. Meanwhile, safety and efficiency are preliminary concerns for robotic dual-arm coordinated manipulation. Thus, the normally adopted fully automated task execution approaches based on environmental perception and motion planning techniques are still inadequate and problematic for the arduous twisting tasks. To this end, this paper presents a novel strate…
In this paper, we present a real-time whole-body planner for collision-free legged mobile manipulation. We enforce both self-collision and environment-collision avoidance as soft constraints within a Model Predictive Control (MPC) scheme that solves a multi-contact optimal control problem. By penalizing the signed distances among a set of representative primitive collision bodies, the robot is able to safely execute a variety of dynamic maneuvers while preventing any self-collisions. Moreover, collision-free navigation and manipulation in both static and dynamic environments are made viable th…
The University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
Contact adaption is an essential capability when manipulating objects. Two key contact modes of non-prehensile manipulation are sticking and sliding. This paper presents a Trajectory Optimization (TO) method formulated as a Mathematical Program with Complementarity Constraints (MPCC), which is able to switch between these two modes. We show that this formulation can be applicable to both planning and Model Predictive Control (MPC) for planar manipulation tasks. We numerically compare: (i) our planner against a mixed integer alternative, showing that the MPCC planer converges faster, scales bet…
Whole-Body Inverse Dynamics MPC for Legged Loco-Manipulation
Department of Mechanical and Process Engineering, ETH Zurich（机械与过程工程系，苏黎世联邦理工学院）；Computational Robotics Lab, Department of Computer Science, ETH Zurich（计算机器人实验室，计算机科学系，苏黎世联邦理工学院）
Task and Motion Planning has made great progress in solving hard sequential manipulation problems. However, a gap between such planning formulations and control methods for reactive execution remains. In this paper we propose a model predictive control approach dedicated to robustly execute a single sequence of constraints, which corresponds to a discrete decision sequence of a TAMP plan. We decompose the overall control problem into three sub-problems (solving for sequential waypoints, their timing, and a short receding horizon path) that each is a non-linear program solved online in each MPC…
Autonomous dual-arm manipulation is an essential skill to deploy robots in unstructured scenarios. However, this is a challenging undertaking, particularly in terms of perception and planning. Unstructured scenarios are full of objects with different shapes and appearances that have to be grasped in a very specific manner so they can be functionally used. In this paper we present an integrated approach to perform dual-arm pick tasks autonomously. Our method consists of semantic segmentation, object pose estimation, deformable model registration, grasp planning and arm trajectory optimization. …
Real-World Deployment of Massively Parallel Sampling-Based MPC for Contact-Rich Manipulation
Intelligent Autonomous Systems Lab, TU Darmstadt（达姆施塔特工业大学智能自主系统实验室）；Interactive Robot Perception & Learning Lab, TU Darmstadt（达姆施塔特工业大学交互式机器人感知与学习实验室）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Robotics Institute Germany (RIG)（德国机器人研究所）；College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）
NVIDIA（英伟达）；University of Washington（华盛顿大学）；University of Sydney（悉尼大学）
Sampling-based model-predictive control (MPC) is a promising tool for feedback control of robots with complex, non-smooth dynamics, and cost functions. However, the computationally demanding nature of sampling-based MPC algorithms has been a key bottleneck in their application to high-dimensional robotic manipulation problems in the real world. Previous methods have addressed this issue by running MPC in the task space while relying on a low-level operational space controller for joint control. However, by not using the joint space of the robot in the MPC formulation, existing methods cannot d…
Recent studies on quadruped robots have focused on either locomotion or mobile manipulation using a robotic arm. Legged robots can manipulate heavier and larger objects using non-prehensile manipulation primitives, such as planar pushing, to drive the object to the desired location. In this paper, we present a novel hierarchical model predictive control (MPC) for contact optimization of the manipulation task. Using two cascading MPCs, we split the loco-manipulation problem into two parts: the first to optimize both contact force and contact location between the robot and the object, and the se…
This paper presents a novel method to control humanoid robot dynamic loco-manipulation with multiple contact modes via multi-contact Model Predictive Control (MPC) framework. The proposed framework includes a multi-contact dynamics model capable of capturing various contact modes in loco-manipulation, such as hand-object contact and foot-ground contacts. Our proposed dynamics model represents the object dynamics as an external force acting on the system, which simplifies the model and makes it feasible for solving the MPC problem. In numerical validations, our multi-contact MPC framework only …
Data-Driven MPC with Data Selection for Flexible Cable-Driven Robotic Arms
Center for Artificial Intelligence and Robotics, Shenzhen International Graduate School, Tsinghua University（人工智能与机器人中心，深圳国际研究生院，清华大学）；Department of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University（先进制造系，深圳国际研究生院，清华大学）
MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm
School of Mechanical Engineering, Shanghai Jiao Tong University（上海交通大学机械工程学院）；Shanghai AI Laboratory（上海人工智能实验室）；Lenovo Corporation（联想公司）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））
Generative Models From and For Sampling-Based MPC: A Bootstrapped Approach For Adaptive Contact-Rich Manipulation
Oxford Robotics Institute, University of Oxford, UK（牛津大学机器人研究所）；Robotics and AI Institute (RAI), Boston, USA（机器人与人工智能研究所）；Cornell University, Ithaca, NY, USA（康奈尔大学）
ETH Zurich（苏黎世联邦理工学院）；Robotic Systems Lab（机器人系统实验室）
In this paper, we propose a whole-body planning framework that unifies dynamic locomotion and manipulation tasks by formulating a single multi-contact optimal control problem. We model the hybrid nature of a generic multi-limbed mobile manipulator as a switched system, and introduce a set of constraints that can encode any pre-defined gait sequence or manipulation schedule in the formulation. Since the system is designed to actively manipulate its environment, the equations of motion are composed by augmenting the robot's centroidal dynamics with the manipulated-object dynamics. This allows us…
Do My Samples Help? Compute Budget Allocation for Real-World Sampling-Based MPC in Contact-Rich Manipulation
TU Darmstadt（达姆施塔特工业大学）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Robotics Institute Germany (RIG)（德国机器人研究所）；School of Computing, DePaul University（德保罗大学计算学院）
Competitive Task Allocation with Temporal Proposal Windows and Improved Artificial Potential Fields for Dual-Arm Industrial Manipulation
School of Mechanical and Materials Engineering, IIT Mandi（印度理工大学曼迪分校机械与材料工程学院）；R&D, Addverb Technologies Limited（Addverb科技有限公司研发部）；Centre for Artificial Intelligence and Robotics, IIT Mandi（印度理工大学曼迪分校人工智能与机器人中心）；R&D 4IR, Dubai Electricity & Water Authority（迪拜水电局第四次工业革命研发部）
Adaptive-MHE : A Sampling-Based Adaptive MPC for Legged Loco-Manipulation via Moving Horizon Estimation
University of Calgary（卡尔加里大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM)（慕尼黑工业大学机器人与机器智能研究所（MIRMI））
Shenzhen University（深圳大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））
In this paper, a novel switching pushing skill algorithm is proposed to improve the efficiency of planar non-prehensile manipulation, which draws inspiration from human pushing actions and comprises two sub-problems, i.e., discrete decision-making of pushing point and continuous feedback control of pushing action. In order to solve the sub-problems above, a combination of Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL) method is employed. Firstly, the selection of pushing point is modeled as a Markov decision process,and an off-policy DRL method is used by reshaping the re…
Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a whole-body optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is performed using a Model Predictive Control (MPC) approach; the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in the MPC planner, and skillfully planning for end-effector contac…
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）；Universitat Politècnica de Catalunya - BarcelonaTECH（加泰罗尼亚理工大学-巴塞罗那理工学院）
Robotic cloth manipulation is a relevant challenging problem for autonomous robotic systems. Highly deformable objects as textile items can adopt multiple configurations and shapes during their manipulation. Hence, robots should not only understand the current cloth configuration but also be able to predict the future possible behaviors of the cloth. This paper addresses the problem of indirectly controlling the configuration of certain points of a textile object, by applying actions on other parts of the object through the use of a Model Predictive Control (MPC) strategy, which also allows to…
We present a method for system identification of flexible objects by measuring forces and displacement during interaction with a manipulating arm. We model the object's structure and flexibility by a chain of rigid bodies connected by torsional springs. Unlike previous work, the proposed optimal control approach using variational integrators allows identification of closed loops, which include the robot arm itself. This allows using the resulting models for planning in configuration space of the robot. In order to solve the resulting problem efficiently, we develop a novel method for fast disc…
We begin this paper by presenting our approach to robot manipulation, which emphasizes the benefits of making contact with the world across the entire manipulator. We assume that low contact forces are benign, and focus on the development of robots that can control their contact forces during goal-directed motion. Inspired by biology, we assume that the robot has low-stiffness actuation at its joints, and tactile sensing across the entire surface of its manipulator. We then describe a novel controller that exploits these assumptions. The controller only requires haptic sensing and does not nee…
RoLoMa: Robust Loco-Manipulation for Quadruped Robots with Arms
Ocado Technology（奥凯多科技）；Touchlab Limited（触控实验室有限公司）；Oxford Robotics Institute, University of Oxford（牛津大学牛津机器人研究所）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
Contact-implicit trajectory optimization offers an appealing method of automatically generating complex and contact-rich behaviors for robot manipulation and locomotion. The scalability of such techniques has been limited, however, by the challenge of ensuring both numerical reliability and physical realism. In this paper, we present preliminary results suggesting that the Iterative Linear Quadratic Regulator (iLQR) algorithm together with the recently proposed pressure-field-based hydroelastic contact model enables reliable and physically realistic trajectory optimization through contact. We …
Legged robots have shown remarkable advantages in navigating uneven terrain. However, realizing effective locomotion and manipulation tasks on quadruped robots is still challenging. In addition, object and terrain parameters are generally unknown to the robot in these problems. Therefore, this paper proposes a hierarchical adaptive control framework that enables legged robots to perform loco-manipulation tasks without any given assumption on the object's mass, the friction coefficient, or the slope of the terrain. In our approach, we first present an adaptive manipulation control to regulate t…
A Nonlinear MPC Framework for Loco-Manipulation of Quadrupedal Robots with Non-Negligible Manipulator Dynamics
Department of Mechanical Engineering, Virginia Tech（弗吉尼亚理工大学机械工程系）；California Institute of Technology（加州理工学院）；School of Neuroscience, Virginia Tech（弗吉尼亚理工大学神经科学学院）
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 …
Nara Institute of Science and Technology (NAIST)（奈良先端科学技术大学院大学）；Advanced Telecommunications Research Institute International (ATR)（国际高级电信研究所）；Kyoto University（京都大学）
Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must be collected once to perform the sim-to-real transfer, which remains a significant human effort in transferring the models learned in simulations to new domains in the real world. To alleviate this problem, we first propose a novel model-learning framework called Kalman Randomized-to-Canonical Model (KRC-model). This framework is capable of extracting task-relevant intrinsic features and their dynamics from randomized…
Existing studies for environment interaction with an aerial robot have been focused on interaction with static surroundings. However, to fully explore the concept of an aerial manipulation, interaction with moving structures should also be considered. In this paper, a multirotor-based aerial manipulator opening a daily-life moving structure, a hinged door, is presented. In order to address the constrained motion of the structure and to avoid collisions during operation, model predictive control (MPC) is applied to the derived coupled system dynamics between the aerial manipulator and the door …
Modern robotic systems are endowed with superior mobility and mechanical skills that make them suited to be employed in real-world scenarios, where interactions with heavy objects and precise manipulation capabilities are required. For instance, legged robots with high payload capacity can be used in disaster scenarios to remove dangerous material or carry injured people. It is thus essential to develop planning algorithms that can enable complex robots to perform motion and manipulation tasks accurately. In addition, online adaptation mechanisms with respect to new, unknown environments are n…
KTH Royal Institute of Technology（瑞典皇家理工学院）；Yale University（耶鲁大学）
This work focuses on the problem of in-hand manipulation and regrasping of objects with parallel grippers. We propose Dexterous Manipulation Graph (DMG) as a representation on which we define planning for in-hand manipulation and regrasping. The DMG is a disconnected undirected graph that represents the possible motions of a finger along the object's surface. We formulate the in-hand manipulation and regrasping problem as a graph search problem from the initial to the final configuration. The resulting plan is a sequence of coordinated in-hand pushing and regrasping movements. We propose a dua…
