Active Force Control (AFC) is an important scheme for tackling high-precision robotic assembly. Classical force controllers are highly surface-dependent: the controller must be carefully tuned for each type of surface in contact, in order to avoid instabilities and to achieve a reasonable performance level. Here, we build upon the recently-developed Convex Controller Synthesis (CCS) to enable high-precision assembly across a wide range of surface stiffnesses without any surface-dependent tuning. Specifically, we demonstrate peg-in-hole assembly with 100 micron clearance, initial position uncertainties up to 2 cm, and for four types of peg and hole materials -- rubber, plastic, wood, aluminum -- whose stiffnesses range from 10 to 100 N/mm, using a single controller.
Controlling contacts is truly challenging, and this has been a major hurdle to deploying industrial robots into unstructured/human-centric environments. More specifically, the main challenges are: (i) how to ensure stability at all times; (ii) how to satisfy task-specific performance specifications; (iii) how to achieve (i) and (ii) under environment uncertainty, robot parameters uncertainty, sensor and actuator time delays, external perturbations, etc. Here, we propose a new approach -- Convex Controller Synthesis (CCS) -- to tackle the above challenges based on robust control theory and conv…
Mitsubishi Electric Research Laboratories（三菱电机研究实验室）
Insertion operations are a critical element of most robotic assembly operation, and peg-in-hole (PiH) insertion is one of the most widely studied tasks in the industrial and academic manipulation communities. PiH insertion is in fact an entire class of problems, where the complexity of the problem can depend on the type of misalignment and contact formation during an insertion attempt. In this paper, we present the design and analysis of adaptive compliance controllers which can be used in insertion-type assembly tasks, including learning-based compliance controllers which can be used for inse…
University of California, Berkeley（加州大学伯克利分校）；Siemens Corporate Technology（西门子公司技术部）；Technion（以色列理工学院）
Precise robotic manipulation skills are desirable in many industrial settings, reinforcement learning (RL) methods hold the promise of acquiring these skills autonomously. In this paper, we explicitly consider incorporating operational space force/torque information into reinforcement learning; this is motivated by humans heuristically mapping perceived forces to control actions, which results in completing high-precision tasks in a fairly easy manner. Our approach combines RL with force/torque information by incorporating a proper operational space force controller; where we also exploit diff…
With the feature of multi-master bus access, nondestructive contention-based arbitration and flexible configuration, Controller Area Network (CAN) bus is applied into the control system of Wire Harness Assembly Machine (WHAM). To accomplish desired goal, the specific features of the CAN bus is analyzed by compared with other field buses and the functional performances in the CAN bus system of WHAM is discussed. Then the application layer planning of CAN bus for dynamic priority is presented. The critical issue for the use of CAN bus system in WHAM is the data transfer rate between different no…
Robotic force-based compliance control is a preferred approach to achieve high-precision assembly tasks. When the geometric features of assembly objects are asymmetric or irregular, reinforcement learning (RL) agents are gradually incorporated into the compliance controller to adapt to complex force-pose mapping which is hard to model analytically. Since force-pose mapping is strongly dependent on geometric features, a compliance controller is only optimal for current geometric features. To reduce the learning cost of assembly objects with different geometric features, this paper is devoted to…
Peg-in-hole assembly is a challenging contact-rich manipulation task. There is no general solution to identify the relative position and orientation between the peg and the hole. In this paper, we propose a novel method to classify the contact poses based on a sequence of contact measurements. When the peg contacts the hole with pose uncertainties, a tilt-then-rotate strategy is applied, and the contacts are measured as a group of patterns to encode the contact pose. A convolutional neural network (CNN) is trained to classify the contact poses according to the patterns. In the end, an admittan…
Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室）
We present the design of a learning-based compliance controller for assembly operations for industrial robots. We propose a solution within the general setting of learning from demonstration (LfD), where a nominal trajectory is provided through demonstration by an expert teacher. This can be used to learn a suitable representation of the skill that can be generalized to novel positions of one of the parts involved in the assembly, for example the hole in a peg-in-hole (PiH) insertion task. Under the expectation that this novel position might not be entirely accurately estimated by a vision or …
In this work, motivated by recent manufacturing trends, we investigate autonomous robotic assembly. Industrial assembly tasks require contact-rich manipulation skills, which are challenging to acquire using classical control and motion planning approaches. Consequently, robot controllers for assembly domains are presently engineered to solve a particular task, and cannot easily handle variations in the product or environment. Reinforcement learning (RL) is a promising approach for autonomously acquiring robot skills that involve contact-rich dynamics. However, RL relies on random exploration f…
Many high precision (dis)assembly tasks are still being performed by humans, whereas this is an ideal opportunity for automation. This paper provides a framework which enables a non-expert human operator to teach a robotic arm to do complex precision tasks. The framework uses a variable Cartesian impedance controller to execute trajectories learned from kinesthetic human demonstrations. Feedback can be given to interactively reshape or speed up the original demonstration. Board localization is done through a visual estimation of the task board position and refined through haptic feedback. Our …
Parallel Self-assembly for a Multi-USV System on Water Surface with Obstacles
Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）
Autonomous Integration and Improvement of Robotic Assembly using Skill Graph Representations
Department of Electrical and Computer Engineering, Carnegie Mellon University（卡内基梅隆大学电气与计算机工程系）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
The robotic assembly represents a group of benchmark problems for reinforcement learning and variable compliance control that features sophisticated contact manipulation. One of the key challenges in applying reinforcement learning to physical robot is the sample complexity, the requirement of large amounts of experience for learning. We mitigate this sample complexity problem by incorporating an iteratively refitted model into the learning process through model-guided exploration. Yet, fitting a local model of the physical environment is of major difficulties. In this work, a Kalman filter is…
School of Electrical Engineering, Aalto University, Finland（阿尔托大学电气工程学院，芬兰）；University of Oulu, Finland（奥卢大学，芬兰）；Aalto University, Finland（阿尔托大学，芬兰）
We present a Learning from Demonstration method for teaching robots to perform search strategies imitated from humans in scenarios where alignment tasks fail due to position uncertainty. The method utilizes human demonstrations to learn both a state invariant dynamics model and an exploration distribution that captures the search area covered by the demonstrator. We present two alternative algorithms for computing a search trajectory from the exploration distribution, one based on sampling and another based on deterministic ergodic control. We augment the search trajectory with forces learnt t…
NVIDIA Corporation（英伟达公司）；University of Washington（华盛顿大学）
Robotic assembly is one of the oldest and most challenging applications of robotics. In other areas of robotics, such as perception and grasping, simulation has rapidly accelerated research progress, particularly when combined with modern deep learning. However, accurately, efficiently, and robustly simulating the range of contact-rich interactions in assembly remains a longstanding challenge. In this work, we present Factory, a set of physics simulation methods and robot learning tools for such applications. We achieve real-time or faster simulation of a wide range of contact-rich scenes, inc…
Ochanomizu University（御茶水女子大学）；Sorbonne Université（索邦大学）；CNRS Institut des Systèmes Intelligents et de Robotique (ISIR)（法国国家科学研究中心智能系统与机器人研究所）
Swarms of molecular robots are a promising approach to create specific shapes at the microscopic scale through self-assembly. However, controlling their behavior is a challenging problem as it involves complex non-linear dynamics and high experimental variability. Hand-crafting a molecular controller will often be time-consuming and give sub-optimal results. Optimization methods, like the bioNEAT algorithm, were previously employed to partially overcome these difficulties, but they still had to cope with deceptive high-dimensional search spaces and computationally expensive simulations. Here, …
AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation
IAS Lab, Computer Science Department, TU Darmstadt（德累斯顿技术大学计算机科学系IAS实验室）；AICOR Institute for Artificial Intelligence, University of Bremen（不莱梅大学人工智能研究所）；Institute for Material Handling and Logistics (IFL), Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院物流与物料搬运研究所）；ArtiMinds Robotics（ArtiMinds机器人技术公司）
提出融合力控制、多模态 transformer 与自动数据优化的 AI 框架，实现机器人线束连接器插接，并在中控台任务中显著提升速度与鲁棒性。
Watchung Hills Regional High School（瓦钦山地区高中）；Stevens Institute of Technology（史蒂文斯理工学院）
In recent years, there is an increasing interest in high school robotics extracurriculars such as robotics clubs and robotics competitions. The growing demand is a result of more ubiquitous open-source software and affordable off-the-shelf hardware kits, which significantly help lower the barrier for entry-level robotics hobbyists. In this project, we present an open-source, low-cost, and lightweight robotic manipulator designed and developed by a high school researcher under the guidance of a university faculty and a Ph.D. student. We believe the presented project is suitable for high school …
OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation
TARS Robotics（TARS机器人实验室）；National University of Singapore（新加坡国立大学）；Fudan University（复旦大学）；CASIA（中国科学院自动化研究所）；Tsinghua University（清华大学）；Zhongguancun Academy（中关村学院）；Beihang University（北京航空航天大学）
