The Importance of Models in Data Analysis with Small Human Movement Datasets -- Inspirations from Neurorobotics Applied to Posture Control of Humanoids and Humans
Neurological University Clinic, University of Freiburg（弗莱堡大学神经大学诊所）
Vittorio Lippi, Christoph Maurer, Thomas Mergner
备注：
Presented at ICPRAM 2021, The International Conference on Pattern Recognition Applications and Methods
发表信息：
roceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, (2021) ISBN 978-989-758-486-2, pages 579-585
This work presents a system identification procedure based on Convolutional Neural Networks (CNN) for human posture control using the DEC (Disturbance Estimation and Compensation) parametric model. The modular structure of the proposed control model inspired the design of a modular identification procedure, in the sense that the same neural network is used to identify the parameters of the modules controlling different degrees of freedom. In this way the presented examples of body sway induced by external stimuli provide several training samples at once.
Learning Humanoid Standing-up Control across Diverse Postures
Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；The University of Hong Kong（香港大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）
MuJoCo MPC for Humanoid Control: Evaluation on HumanoidBench
Technical University of 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）；Centre for Cognitive Science（认知科学中心）；Hessian Center for Artificial Intelligence (Hessian.AI)（黑森州人工智能中心）
Pose estimation commonly refers to computer vision methods that recognize people's body postures in images or videos. With recent advancements in deep learning, we now have compelling models to tackle the problem in real-time. Since these models are usually designed for human images, one needs to adapt existing models to work on other creatures, including robots. This paper examines different state-of-the-art pose estimation models and proposes a lightweight model that can work in real-time on humanoid robots in the RoboCup Humanoid League environment. Additionally, we present a novel dataset …
Waseda University（早稻田大学）；University of Plymouth（普利茅斯大学）；Cortexica Vision Systems（Cortexica视觉系统公司）；Korea Advanced Institute of Science and Technology（韩国科学技术院）
The paper presents a neurorobotics cognitive model to explain the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with different motor behaviours, following a learning approach inspired by developmental language acquisition in infants. This cognitive model is based on Multiple Time-scale Recurrent Neural Networks (MTRNN).With the data obtained from object manipulation task…
Posture control and balance are basic requirements for a humanoid robot performing motor tasks like walking and interacting with the environment. For this reason, posture control is one of the elements taken into account when evaluating the performance of humanoids. In this work, we describe and analyze a performance indicator based on the comparison between the body sway of a robot standing on a moving surface and the one of healthy subjects performing the same experiment. This approach is here oriented to the evaluation of human likeness. The measure is tested with three human-inspired human…
This work presents a system to benchmark humanoid posture control and balance performances under perturbed conditions. The specific benchmarking scenario consists, for example, of balancing upright stance while performing voluntary movements on moving surfaces. The system includes a motion platform used to provide the perturbation, an innovative body-tracking system suitable for robots, humans and exoskeletons, control software and a set of predefined perturbations, a humanoid robot used to test algorithms, and analysis software providing state of the art data analysis used to provide quantita…
Similarly to humans, humanoid robots require posture control and balance to walk and interact with the environment. In this work posture control in perturbed conditions is evaluated as a performance test for humanoid control. A specific performance indicator is proposed: the score is based on the comparison between the body sway of the tested humanoid standing on a moving surface and the sway produced by healthy subjects performing the same experiment. This approach is here oriented to the evaluation of a human-likeness. The measure is tested using a humanoid robot in order to demonstrate a ty…
Bio-inspired sensorimotor control systems may be appealing to roboticists who try to solve problems of multiDOF humanoids and human-robot interactions. This paper presents a simple posture control concept from neuroscience, called disturbance estimation and compensation, DEC concept [1]. It provides human-like mechanical compliance due to low loop gain, tolerance of time delays, and automatic adjustment to changes in external disturbance scenarios. Its outstanding feature is that it uses feedback of multisensory disturbance estimates rather than 'raw' sensory signals for disturbance compensati…
University of Granada（格拉纳达大学）；University Pierre & Marie Curie（皮埃尔和玛丽·居里大学）
We embed a spiking cerebellar model within an adaptive real-time (RT) control loop that is able to operate a real robotic body (iCub) when performing different vestibulo-ocular reflex (VOR) tasks. The spiking neural network computation, including event- and time-driven neural dynamics, neural activity, and spike-timing dependent plasticity (STDP) mechanisms, leads to a nondeterministic computation time caused by the neural activity volleys encountered during cerebellar simulation. This nondeterministic computation time motivates the integration of an RT supervisor module that is able to ensure…
Technical University Berlin（柏林工业大学）；Neurological University Clinic（大学神经科诊所）；University of Freiburg（弗赖堡大学）
In this work we present a system identification procedure based on Convolutional Neural Networks (CNN) for human posture control models. A usual approach to the study of human posture control consists in the identification of parameters for a control system. In this context, linear models are particularly popular due to the relative simplicity in identifying the required parameters and to analyze the results. Nonlinear models, conversely, are required to predict the real behavior exhibited by human subjects and hence it is desirable to use them in posture control analysis. The use of CNN aims …
Explainable artificial intelligence (XAI) can help foster trust in and acceptance of intelligent and autonomous systems. Moreover, understanding the motivation for an agent's behavior results in better and more successful collaborations between robots and humans. However, not only can humans benefit from a robot's explanation but the robot itself can also benefit from explanations given to him. Currently, most attention is paid to explaining deep neural networks and black-box models. However, a lot of these approaches are not applicable to humanoid robots. Therefore, in this position paper, cu…
Institute of Psychology（心理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Hamburg（汉堡大学）
To enhance human-robot social interaction, it is essential for robots to process multiple social cues in a complex real-world environment. However, incongruency of input information across modalities is inevitable and could be challenging for robots to process. To tackle this challenge, our study adopted the neurorobotic paradigm of crossmodal conflict resolution to make a robot express human-like social attention. A behavioural experiment was conducted on 37 participants for the human study. We designed a round-table meeting scenario with three animated avatars to improve ecological validity.…
Fondazione Istituto Italiano di Tecnologia（意大利技术研究院基金会）；Università di Genova（热那亚大学）
Humanoid robots are machines built with an anthropomorphic shape. Despite decades of research into the subject, it is still challenging to tackle the robot locomotion problem from an algorithmic point of view. For example, these machines cannot achieve a constant forward body movement without exploiting contacts with the environment. The reactive forces resulting from the contacts are subject to strong limitations, complicating the design of control laws. As a consequence, the generation of humanoid motions requires to exploit fully the mathematical model of the robot in contact with the envir…
Whole-body Multi-contact Motion Control for Humanoid Robots Based on Distributed Tactile Sensors
CNRS-AIST JRL (Joint Robotics Laboratory), IRL and National Institute of Advanced Industrial Science and Technology (AIST)（CNRS-AIST联合机器人实验室、IRL和国家先进工业科学与技术研究所）
Towards a General Humanoid Loco-Manipulation Model via Egocentric Whole-Body Human Data Pretraining
Alibaba Group（阿里巴巴集团）；Sichuan University（四川大学）；Shanghai Innovation Institute（上海创新研究院）；Beihang University（北京航空航天大学）；Hong Kong University of Science and Technology（香港科技大学）
Istituto Italiano di Tecnologia（意大利理工学院）；The University of Manchester（曼彻斯特大学）；INAIL（意大利国家工伤事故保险研究所）；Honda R&D Co., Ltd.（本田研发有限公司）
Humans show specialized strategies for efficient collaboration. Transferring similar strategies to humanoid robots can improve their capability to interact with other agents, leading the way to complex collaborative scenarios with multiple agents acting on a shared environment. In this paper we present a control framework for robot-robot collaborative lifting. The proposed shared controller takes into account the joint action of both the robots thanks to a centralized controller that communicates with them, and solves the whole-system optimization. Efficient collaboration is ensured by taking …
Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction
Department of Electronic Engineering and Information Science (EEIS), University of Science and Technology of China（电子工程与信息科学系，中国科学技术大学）；LimX Dynamics（LimX动力学）；Hong Kong University of Science and Technology（香港科技大学）；School of Mechanics Engineering, Harbin Institute of Technology (HIT)（机械工程学院，哈尔滨工业大学）；Department of Computing, The Hong Kong Polytechnic University（计算学院，香港理工大学）；Zhejiang University-University of Illinois Urbana-Champaign Institute (ZJUI)（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究所）
HuMam: Humanoid Motion Control via End-to-End Deep Reinforcement Learning with Mamba
College of Graduate and Professional Studies, Trine University（特灵大学研究生与专业研究学院）；Department of Civil Engineering, University of Hong Kong（香港大学土木工程系）；Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼大学工程与信息技术学院）；National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University（吉林大学汽车底盘集成与生物仿生国家重点实验室）；School of Computer Science and Statistics, Trinity College Dublin（都柏林信任学院计算机科学与统计学系）
Bionic Human-Motion Style Transfer for Physically Executable Whole-Body Control of Humanoid Robots
Institute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China（人形机器人研究院，精密机械与精密仪器系，中国科学技术大学）；School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University（计算机科学与技术学院，电子与信息工程学院，西安交通大学）；School of Information Science and Engineering, Lanzhou University（信息科学与工程学院，兰州大学）
