The estimation of inertial parameters of a robotic system is crucial for better trajectory tracking performance, specially when model-based controllers are used for carrying out precise tasks. In this paper, we consider the scenario of grasping an object of unknown properties by a free-flyer space robot with limited actuation. The problem is to find the inertial parameters of the complete system after grasping has been performed. Excitation is provided in inertial space, and the excitation trajectories are found by optimization. Truncated Fourier series are used to represent the reference as well as tracked trajectory. An approach based on the energy balance between the actuation work and the rate of change of kinetic energy is introduced to calculate the number of harmonics in the Fourier series used to represent the executed trajectory, while trying to find a balance between accounting for saturation effects and keeping out noise. The effect of input saturation on parameter estimation is also studied. Simulation results using the Space CoBot free-flyer robot are presented to show the feasibility of the approach.
Stanford University（斯坦福大学）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）
Assistive free-flying robots are a promising platform for supporting and working alongside astronauts in carrying out tasks that require interaction with the environment. However, current free-flying robot platforms are limited by existing manipulation technologies in being able to grasp and manipulate surrounding objects. Instead, gecko-inspired adhesives offer many advantages for an alternate grasping and manipulation paradigm for use in assistive free-flyer applications. In this work, we present the design of a gecko-inspired adhesive gripper for performing perching and grasping maneuvers f…
MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Qiyuan Lab（启元实验室）
A Novel Reconfigurable Dexterous Hand Based on Triple-Symmetric Bricard Parallel Mechanism
Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University（人工智能与机器人研究所，工程与技术学院，复旦大学）；Department of Mechanical Engineering, The Hong Kong Polytechnic University（机械工程系，香港理工大学）
6-DoF Grasp Detection in Clutter with Enhanced Receptive Field and Graspable Balance Sampling
School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications（北京邮电大学智能工程与自动化学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
EHC-MM: Embodied Holistic Control for Mobile Manipulation
Beijing Samsung Telecom R&D Center（北京三星通信技术研发中心）；National Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；PKU-WUHAN Institute for Artificial Intelligence（北京大学-武汉人工智能研究院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Tsinghua University（清华大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
Shandong University（山东大学）；Shandong Youbaote Intelligent Robot Co., Ltd（山东优宝特智能机器人有限公司）；China North Vehicle Research Institute（中国北方车辆研究所）
An intuitive control method for the flying trot, which combines offline trajectory planning with real-time balance control, is presented. The motion features of running animals in the vertical direction were analysed using the spring-load-inverted-pendulum (SLIP) model, and the foot trajectory of the robot was planned, so the robot could run similar to an animal capable of vertical flight, according to the given height and speed of the trunk. To improve the robustness of running, a posture control method based on a foot acceleration adjustment is proposed. A novel kinematic based CoM observati…
University of Liverpool（利物浦大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Indian Institute of Science（印度科学学院）；The University of Tokyo（东京大学）；Zürcher Hochschule für Angewandte Wissenschaften（苏黎世应用科技大学）；University of Arkansas（阿肯色大学）；Physical Intelligence（物理智能公司）；Huazhong University of Science and Technology（华中科技大学）
Keep it Upright: Model Predictive Control for Nonprehensile Object Transportation with Obstacle Avoidance on a Mobile Manipulator
Technical University of Munich（慕尼黑工业大学）；University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；University of Toronto Robotics Institute（多伦多大学机器人研究所）；Munich Institute of Robotics and Machine Intelligence（慕尼黑机器人与机器智能研究所）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）
Southern University of Science and Technology（南方科技大学）；Guangdong University of Technology（广东工业大学）
This paper studies real-time motion planning and control for ball bumping motion with quadruped robots. To enable the quadruped to bump the flying ball with different initializations, we develop a nonlinear trajectory optimization-based planning scheme that jointly identifies the take-off time and state to achieve accurate ball hitting during the flight phase. Such a planning scheme employs a two-dimensional single rigid body model that achieves a satisfactory balance between accuracy and efficiency for the highly time-sensitive task. To precisely execute the planned motion, the tracking contr…
HeLoM: Hierarchical Learning for Whole-Body Loco-Manipulation by a Hexapod Robot
MARMoT Lab, Department of Mechanical Engineering, National University of Singapore（机械工程系，新加坡国立大学MARMoT实验室）；Center for X-mechanics, Zhejiang University（浙江大学X力学中心）
Although Trajectory Optimization (TO) is one of the most powerful motion planning tools, it suffers from expensive computational complexity as a time horizon increases in cluttered environments. It can also fail to converge to a globally optimal solution. In this paper, we present Lazy Trajectory Optimization (LTO) that unifies local short-horizon TO and global Graph-Search Planning (GSP) to generate a long-horizon global optimal trajectory. LTO solves TO with the same constraints as the original long-horizon TO with improved time complexity. We also propose a TO-aware cost function that can b…
