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MOVE: Multi-skill Omnidirectional Legged Locomotion with Limited View in 3D Environments

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arXiv 2024年

作者： Li, Songbo Luo, Shixin Wu, Jun Zhu, Qiuguo Institute of Cyber-Systems and Control Zhejiang University 310027 China State Key Laboratory of Industrial Control Technology 310027 China

Legged robots possess inherent advantages in traversing complex 3D terrains. However, previous work on low-cost quadruped robots with egocentric vision systems has been limited by a narrow front-facing view and exteroceptive noise, restricting omnidirectional mobility in such environments. While building a voxel map through a hierarchical structure can refine exteroception processing, it introduces significant computational overhead, noise, and delays. In this paper, we present MOVE, a one-stage end-to-end learning framework capable of multi-skill omnidirectional legged locomotion with limited view in 3D environments, just like what a real animal can do. When movement aligns with the robot's line of sight, exteroceptive perception enhances locomotion, enabling extreme climbing and leaping. When vision is obstructed or the direction of movement lies outside the robot's field of view, the robot relies on proprioception for tasks like crawling and climbing stairs. We integrate all these skills into a single neural network by introducing a pseudo-siamese network structure combining supervised and contrastive learning which helps the robot infer its surroundings beyond its field of view. Experiments in both simulations and real-world scenarios demonstrate the robustness of our method, broadening the operational environments for robotics with egocentric vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

A Time-delay Approach for Stabilization of Linear systems with Unknown control Direction

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A Time-delay Approach for Stabilization of Linear Systems wi...

IEEE Conference on Decision and control

作者： Gaofeng Pan Yang Zhu Zhengguang Wu State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China and Ningbo Innovation Center Zhejiang University Ningbo China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We propose a novel stability analysis for linear systems with unknown control directions, focusing on stabilization through extremum seeking to minimize a Lyapunov function. Our approach draws inspiration from Lie bracket systems, employing a transformation using time-delay analysis to convert the closed-loop system into a nominal time-delay system. Leveraging techniques from Input-to-state Stability (ISS), we provide a proof of semiglobal practical asymptotic stability. Compared to the preceding analysis based on Lie bracket systems, the use of time-delay methods transforms intricate mathematical discussions and proofs into a structured computational framework, facilitating a more intuitive understanding. Also, we propose a method for selecting controller parameters and can provide a more comprehensive explanation for the current high-gain parameter choices. Finally, we present a new control parameter to reduce the requirements of the frequency of the sinusoidal signals.

关键词： Linear systems Asymptotic stability Focusing Transforms Stability analysis Robustness Closed loop systems Frequency control Lyapunov methods

Toward Understanding key Estimation in Learning Robust Humanoid Locomotion

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arXiv 2024年

作者： Wang, Zhicheng Wei, Wandi Yu, Ruiqi Wu, Jun Zhu, Qiuguo Institute of Cyber-Systems and Control Zhejiang University 310027 China State Key Laboratory of Industrial Control Technology 310027 China

Accurate state estimation plays a critical role in ensuring the robust control of humanoid robots, particularly in the context of learning-based control policies for legged robots. However, there is a notable gap in analytical research concerning estimations. Therefore, we endeavor to further understand how various types of estimations influence the decision-making processes of policies. In this paper, we provide quantitative insight into the effectiveness of learned state estimations, employing saliency analysis to identify key estimation variables and optimize their combination for humanoid locomotion tasks. Evaluations assessing tracking precision and robustness are conducted on comparative groups of policies with varying estimation combinations in both simulated and real-world environments. Results validated that the proposed policy is capable of crossing the sim-to-real gap and demonstrating superior performance relative to alternative policy configurations. © 2024, CC BY.

关键词： Anthropomorphic robots

PIE: Parkour with Implicit-Explicit Learning Framework for Legged Robots

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arXiv 2024年

作者： Luo, Shixin Li, Songbo Yu, Ruiqi Wang, Zhicheng Wu, Jun Zhu, Qiuguo Institute of Cyber-Systems and Control Zhejiang University 310027 China State Key Laboratory of Industrial Control Technology 310027 China

Parkour presents a highly challenging task for legged robots, requiring them to traverse various terrains with agile and smooth locomotion. This necessitates comprehensive understanding of both the robot’s own state and the surrounding terrain, despite the inherent unreliability of robot perception and actuation. Current state-of-the-art methods either rely on complex pre-trained high-level terrain reconstruction modules or limit the maximum potential of robot parkour to avoid failure due to inaccurate perception. In this paper, we propose a one-stage end-to-end learning-based parkour framework: Parkour with Implicit-Explicit learning framework for legged robots (PIE) that leverages dual-level implicit-explicit estimation. With this mechanism, even a low-cost quadruped robot equipped with an unreliable egocentric depth camera can achieve exceptional performance on challenging parkour terrains using a relatively simple training process and reward function. While the training process is conducted entirely in simulation, our real-world validation demonstrates successful zero-shot deployment of our framework, showcasing superior parkour performance on harsh terrains. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

Walking with Terrain Reconstruction: Learning to Traverse Risky Sparse Footholds

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arXiv 2024年

作者： Yu, Ruiqi Wang, Qianshi Wang, Yizhen Wang, Zhicheng Wu, Jun Zhu, Qiuguo Institute of Cyber-Systems and Control Zhejiang University 310027 China State Key Laboratory of Industrial Control Technology 310027 China

Traversing risky terrains with sparse footholds presents significant challenges for legged robots, requiring precise foot placement in safe areas. To acquire comprehensive exteroceptive information, prior studies have employed motion capture systems or mapping techniques to generate heightmap for locomotion policy. However, these approaches require specialized pipelines and often introduce additional noise. While depth images from egocentric vision systems are cost-effective, their limited field of view and sparse information hinder the integration of terrain structure details into implicit features, which are essential for generating precise actions. In this paper, we demonstrate that end-to-end reinforcement learning relying solely on proprioception and depth images is capable of traversing risky terrains with high sparsity and randomness. Our method introduces local terrain reconstruction, leveraging the benefits of clear features and sufficient information from the heightmap, which serves as an intermediary for visual feature extraction and motion generation. This allows the policy to effectively represent and memorize critical terrain information. We deploy the proposed framework on a low-cost quadrupedal robot, achieving agile and adaptive locomotion across various challenging terrains and showcasing outstanding performance in real-world scenarios. Video at: ***/Rj9v5EZsn-M. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

Learning adaptive manipulation of objects with revolute joint: A case study on varied cabinet doors opening

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Learning adaptive manipulation of objects with revolute join...

第42届中国控制会议

作者： Hongxiang Yu Dashun Guo Zhongxiang Zhou Yue Wang Rong Xiong the State Key Laboratory of Industrial Control and Technology and the Institute of Cyber-Systems and Control Zhejiang University

This paper introduces a learning-based framework for robot adaptive manipulating the object with a revolute joint in unstructured environments. We concentrate our discussion on various cabinet door opening tasks. To improve the performance of Deep Reinforcement Learning in this scene, we analytically provide an efficient sampling manner utilizing the constraints of the objects. To open various kinds of doors, we add encoded environment parameters that define the various environments to the input of out policy. To transfer the policy into the real world, we train an adaptation module in simulation and fine-tune the adaptation module to cut down the impact of the policy-unaware environment parameters. We design a series of experiments to validate the efficacy of our framework. Additionally, we testify to the model's performance in the real world compared to the traditional door opening method.

关键词：

Review of data security within energy blockchain:A comprehensive analysis of storage,management,and utilization

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High-Confidence Computing 2024年第3期4卷 82-102页

作者： Yunhua He Zhihao Zhou Yan Pan Fahui Chong Bin Wu Ke Xiao Hong Li School of Information Science and Technology North China University of TechnologyBeijing 100144China China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China Key Laboratory in Blockchain and Data Security by Ministry of Industry and Information Technology Beijing 100040China Institute of Information Engineering Chinese Academy of Sciences Beijing 100195China

Energy systems are currently undergoing a transformation towards new paradigms characterized by decarbonization,decentralization,democratization,and *** this evolving context,energy blockchain,aiming to enhance efficiency,transparency,and security,emerges as an integrated technological solution designed to address the diverse challenges in this *** security is essential for the reliable and efficient functioning of energy *** pressing need to address challenges related to secure data storage,effective data management,and efficient data utilization is increasingly *** paper offers a comprehensive survey of academic discourse on energy blockchain data security over the past five years,adopting an all-encompassing perspective that spans data storage,management,and *** work systematically evaluates and contrasts the strengths and weaknesses of various research ***,this paper proposes an integrated hierarchical on-chain and off-chain security energy blockchain architecture,specifically designed to meet the complex security requirements of multi-blockchain business ***,this paper identifies key directions for future research,particularly in advancing the integration of storage,management,and utilization of energy blockchain data security.

关键词： Energy blockchain Data security Storage Management Utilization

Adaptive control of Nonlinear systems with Sinusoidal Uncertainties via Internal Model Principle

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IFAC-PapersOnLine 2023年第1期56卷 270-275页

作者： Qi Han Lei Wang Lorenzo Marconi Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China CASY - DEI University of Bologna Italy

In this paper, an adaptive control scheme is established for nonlinear systems with sinusoidal uncertainties from the perspective of internal model principle. We propose to embed the controller with a dynamic compensator which plays the role of an internal model for reproducing the unknown sinusoidal parameter. For linearly parameterized systems, under some full-information stabilizability assumptions, internal-model-based controllers are developed, rendering the closed-loop system trajectories to be bounded and the plant states to be asymptotically convergent to zero. Further, we extend the proposed internal-model-based control scheme to nonlinearly parameterized systems with a (strictly) monotonic assumption. Two simulation examples are given to verify the effectiveness of the proposed schemes.

关键词： Adaptive control internal model sinusoidal parameter

Trajectory Tracking control for Autonomous Parking Based on Adaptive Reduced-horizon Model Predictive control 19

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Trajectory Tracking Control for Autonomous Parking Based on ...

19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Cai, Minghan Wu, Weimin Zhou, Xiaoling Technology Institute of Cyber-Systems and Control Zhejiang University The State Key Laboratory of Industrial Control Hangzhou310027 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665472432

In this paper, an adaptive reduced-horizon model predictive control is proposed for autonomous parking trajectory tracking. Given the reference trajectory, the discrete linear time varying model is obtained by linearizing and discretizing along the reference trajectory point. Furthermore, the model is reformulated into a combined incremental form. Then, a standard quadratic programming problem is established, and the optimal control strategy is obtained by solving the problem online at every time instant. Meanwhiles, the prediction horizon will reduce adaptively by solving the constrained optimization problem, and it will minimize the computation time complexity of the MPC-based controller. The actual parking scenarios are co-simulated in Simulink and Carsim, which shows the effectiveness and feasibility of the proposed method. © 2022 IEEE.

关键词： Model predictive control