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نتیجه جستجو - humanoid robot

تعداد مقالات یافته شده: 36
ردیف عنوان نوع
1 Capturing causality and bias in human action recognition
ثبت علیت و سوگیری در تشخیص عمل انسان-2021
Human action recognition using various sensors is a mandatory component of autonomous vehicles, humanoid robots, and ambient living environments. A particular interest is the detection and recognition of falls. In this paper, we propose the use of temporal convolution networks guided by knowledge distilla- tion for detecting falls and recognizing types of falls using accelerometer data. Tri-axial accelerometers attached to the body measure the acceleration of the body joints when an action occurs. These data are used for pattern analysis and body action recognition. We demonstrate the existence of biases caused by soft biometrics when recognizing human body actions. We introduce a causal network to capture the influences of biases on system performance and illustrate how knowledge distillation can be applied to mitigate the bias effect. Crown Copyright © 2021 Published by Elsevier B.V. All rights reserved.
Keywords: Machine learning | Decision support | Human action recognition | Machine reasoning | Belief networks
مقاله انگلیسی
2 Teaching a humanoid robot to walk faster through Safe Reinforcement Learning
آموزش یک ربات انسان نما برای راه رفتن سریعتر از طریق یادگیری تقویتی ایمن-2020
Teaching a humanoid robot to walk is an open and challenging problem. Classical walking behaviors usually require the tuning of many control parameters (e.g., step size, speed). To find an initial or basic configuration of such parameters could not be so hard, but optimizing them for some goal (for instance, to walk faster) is not easy because, when defined incorrectly, may produce the fall of the humanoid, and the consequent damages. In this paper we propose the use of Safe Reinforcement Learning for improving the walking behavior of a humanoid that permits the robot to walk faster than with a pre-defined configuration. Safe Reinforcement Learning assumes the existence of a safe baseline policy that permits the humanoid to walk, and probabilistically reuse such a policy to learn a better one, which is represented following a case based approach. The proposed algorithm has been evaluated in a real humanoid robot proving that it drastically increases the learning speed while reduces the number of falls during learning when compared with state-of-the-art algorithms.
Keywords: Safe Reinforcement Learning | Biped walking
مقاله انگلیسی
3 Autistic traits, personality, and evaluations of humanoid robots by young and older adults
ویژگی های اوتیستیک ، شخصیت و ارزیابی ربات های انسان دوستانه توسط افراد جوان و بزرگتر-2020
While research with individuals on the autistic spectrum has increased strongly, there is still a lack of research on autism/autistic traits in older adults. Children with autism have been proposed to benefit from interactions with social robots; for older adults, the potential role of robotics is currently being discussed. We combined these topics by assessing both young and older (Mean age ¼ 22 vs. 69 years) neurotypical adults’ evaluations of various humanoid robots presented in video clips, on multiple dimensions (likeability, companionship, dominance, threat, human-likeness). We additionally assessed autistic traits (Autism Spectrum Questionnaire – AQ) and Big- Five personality traits. Remarkably, older adults evaluated robots as more likeable. Compared to young adults, older adults also showed significantly higher levels of autistic traits (particularly in the AQ social interaction subscale), higher levels of conscientiousness, and lower levels of openness. We found strong positive correlations between ratings of likeability and human-likeness of robots across groups, and particularly in participants with high levels of autistic trait. Across robots, data also provided evidence for the uncanny valley phenomenon. Favourable evaluations of robots by older adults suggest potential for older adults on the autistic spectrum to benefit from social robots.
Keywords: Humanoid robots | Old adults | Autistic traits | Personality | Visual appearance
مقاله انگلیسی
4 A data-efficient deep learning approach for deployable multimodal social robots
یک روش یادگیری عمیق با کارآیی داده ها برای روبات های اجتماعی چند حالته قابل استفاده-2019
The deep supervised and reinforcement learning paradigms (among others) have the potential to endow interactive multimodal social robots with the ability of acquiring skills autonomously. But it is still not very clear yet how they can be best deployed in real world applications. As a step in this direction, we propose a deep learning-based approach for efficiently training a humanoid robot to play multimodal games—and use the game of ‘Noughts and Crosses’ with two variants as a case study. Its minimum re- quirements for learning to perceive and interact are based on a few hundred example images, a few example multimodal dialogues and physical demonstrations of robot manipulation, and automatic sim- ulations. In addition, we propose novel algorithms for robust visual game tracking and for competitive policy learning with high winning rates, which substantially outperform DQN-based baselines. While an automatic evaluation shows evidence that the proposed approach can be easily extended to new games with competitive robot behaviours, a human evaluation with 130 humans playing with the Pepper robot confirms that highly accurate visual perception is required for successful game play.
Keywords: Deep reinforcement learning | Deep supervised learning | Interactive robots | Multimodal perception and interaction | Board games
مقاله انگلیسی
5 Designing an API at an appropriate abstraction level for programming social robot applications
طراحی یک API در سطح انتزاعی مناسب برای برنامه نویسی برنامه های کاربردی ربات اجتماعی-2017
Whilst robots are increasingly being deployed as social agents, it is still difficult to program them to interact socially. To create usable tools for programming these robots, tool developers need to know what abstraction levels are appropriate for programming social robot applications. We explore this through the iterative design and evaluation of an API for programming social robots. The results show that high level primitives, with a close mapping to social interaction, are suitable for programming social robot applications. However, the abstraction level should not be so high that it takes away too much control from programmers. This has the potential to enable programmers to produce high quality social robot applications with less programming effort.& 2017 Published by Elsevier Ltd.
Keywords:Application programming interfaces | API | Usability | Design | Cognitive dimensions | Human robot interaction | Social robot interaction | Humanoid robot
مقاله انگلیسی
6 Simulation modeling of pedestrian behavior in the presence of unmanned mobile robots
مدل سازی شبیه سازی رفتار عابر پیاده در حضور ربات های متحرک بدون سرنشین-2017
Article history:Received 12 August 2016Revised 3 March 2017Accepted 29 March 2017Available online 10 April 2017Keywords: Pedestrian behavior Microsimulation Mobile robotInteractions between pedestrians and robots are becoming more commonplace. In public areas, for example, robots may be used for information dissemination, security, or patrol tasks. Based upon existing literature in the field of human-robot interaction, the ISAPT simulation system was revised to model individual pedestrian behavior in the presence of a mobile robot. Using an agent-based modeling approach, pedestrians are statistically assigned one of six reported behaviors when a robot is encountered: interact, watch, curi- ous, ignore, cautious, and avoid. The modeling methods for incorporating these behaviors include modifying a pedestrian’s existing agenda and/or their perception of the threat rep- resented by the non-humanoid robot, while the pedestrian continues to make navigation decisions based on their overall utility function. This paper discusses the implementation of this capability and presents results on ISAPT’s ability to reproduce the different behav- iors reported in the literature. Data collected in a field study are used to further validate the system by comparing measures from observed behaviors to simulation output. Valida- tion measures included lateral distance to robot and lateral path deviation. These results illustrate this approach is an effective means for adding this capability to microsimulation modeling systems.© 2017 Elsevier B.V. All rights reserved.
Keywords: Pedestrian behavior | Microsimulation | Mobile robot
مقاله انگلیسی
7 Generation of rhythmic hand movements in humanoid robots by a neural imitation learning architecture
تولید حرکات دست ریتمیک در ربات انسان نما بواسطه ی معماری عصبی آموزش تقلید-2017
This paper presents a two layer system for imitation learning in humanoid robots. The first layer of this system records complicated and rhythmic movement of the trainer using a motion capture device. It solves an inverse kinematic problem with the help of an adaptive Neuro-Fuzzy Inference system. Then it can achieve angles records of any joints involved in the desired motion. The trajectory is given as input to the systems second layer. The layer deals with extracting optimal parameters of the trajectories obtained from the first layer using a network of oscillator neurons and Particle Swarm Optimization algo- rithm. This system is capable to obtain any complex motion and rhythmic trajectory via first layer and learns rhythmic trajectories in the second layer then converge towards all these movements. Moreover, this two layer system is able to provide various features of a learner model, for instance resis- tance against perturbations, modulation of trajectories amplitude and frequency. The simulation results of the learning system is performed in the robot simulator WEBOTS linked with MATLAB software. Practical implementation on an NAO robot demonstrate that the robot has learned desired motion with high accuracy. These results show that proposed system in this paper produces high convergence rate and low test error.© 2016 Published by Elsevier B.V.
Keywords:Imitation learning | Neural networks | Central pattern generator
مقاله انگلیسی
8 Demonstration learning of robotic skills using repeated suggestions learning algorithm
یادگیری مهارت های تظاهرات رباتیک با استفاده از پیشنهادات مکرر الگوریتم یادگیری-2017
In this paper a new model of nonlinear dynamical system based on adaptive frequency oscillators for learning rhythmic signals is implemented by demonstration. This model uses coupled Hopf oscillators to encode and learn any periodic input signal. Learning process is completely implemented in the dynam- ics of adaptive oscillators. One of the issues in learning in such systems is constant number of oscillators in the feedback loop. In other words, the number of adaptive frequency oscillators is one of the design factors. In this contribution, it is shown that using enough number of oscillators can help the learning process. In this paper, we address this challenge and try to solve it in order to learn the rhythmic move- ments with greater accuracy, lower error and avoid missing fundamental frequency. To reach this aim, a method for generating drumming patterns is proposed which is able to generate rhythmic and periodic trajectories for a NAO humanoid robot. To do so, a programmable central pattern generator is used which is inspired from animal’s neural systems and these programmable central pattern generators are extended to learn patterns with more accuracy for NAO humanoid robots. Successful experiments of demonstration learning are done using simulation and a NAO Real robot.© 2017 Elsevier B.V. All rights reserved.
Keywords:Imitation learning | Hopf oscillator | Adaptive frequency oscillator | Central pattern generator | Humanoid robots
مقاله انگلیسی
9 Hopfield Net spreading activation for grounding of abstract action words in cognitive robot
فعال سازی Hopfield Net گسترش برای مبادله کلمات عمل انتزاعی در ربات شناختی-2017
Human intelligence is the main inspiration for the cognitive robotics field, and language is one of the unique qualities of humans. The increasing interest in acquisition of language in cognitive agents/robots with grounded phenomenon facilitates robotic services in real scenarios in real means through stepping into the shoes of human intelligence. In this paper, a cognitive robotics model is proposed for the ground- ing of a higher-order concept in the sensorimotor experience of humanoid robot. In the proposed model, robot’s conceptual knowledge is hierarchically organized in a semantic network by using the inputted linguistic description of these words. The meaning of the higher-order word is indirectly grounded in sen- sorimotor primitives through a top-down activation process. Specifically, in the described model, the meaning of the abstract action word is acquired through the combination of already grounded action primitives. Hence, the proposed model with the strength of symbolic computation extends the grounding mechanism to the abstract words category, and also provides a tool (i.e. a practical way) to investigate the link that exists between sensorimotor representation and abstract conceptual knowledge of the cognitive robot. For verification of the proposed model, simulation- experiments have been conducted by using iCub robot data.© 2017 Elsevier B.V. All rights reserved.
Keywords:Abstract words | Cognitive robotics | Grounded cognition | Symbol grounding problem | Semantic network | Hopfield Net spreading activation
مقاله انگلیسی
10 Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots
گرفتن مهارت مداوم کنجکاوی از ورودی های ویدئویی با ابعاد بزرگ برای ربات های انسان نما-2017
Article history:Received in revised form 12 October 2014 Accepted 2 February 2015Available online 12 February 2015Keywords: Reinforcement learning Artificial curiositySkill acquisitionSlow feature analysis Continual learning Incremental learning iCubIn the absence of external guidance, how can a robot learn to map the many raw pixels of high-dimensional visual inputs to useful action sequences? We propose here Continual Curiosity driven Skill Acquisition (CCSA). CCSA makes robots intrinsically motivated to acquire, store and reuse skills. Previous curiosity-based agents acquired skills by associating intrinsic rewards with world model improvements, and used reinforcement learning to learn how to get these intrinsic rewards. CCSA also does this, but unlike previous implementations, the world model is a set of compact low-dimensional representations of the streams of high-dimensional visual information, which are learned through incremental slow feature analysis. These representations augment the robot’s state space with new information about the environment. We show how this information can have a higher- level (compared to pixels) and useful interpretation, for example, if the robot has grasped a cup in its field of view or not. After learning a representation, large intrinsic rewards are given to the robot for performing actions that greatly change the feature output, which has the tendency otherwise to change slowly in time. We show empirically what these actions are (e.g., grasping the cup) and how they can be useful as skills. An acquired skill includes both the learned actions and the learned slow feature representation. Skills are stored and reused to generate new observations, enabling continual acquisition of complex skills. We present results of experiments with an iCub humanoid robot that uses CCSA to incrementally acquire skills to topple, grasp and pick-place a cup, driven by its intrinsic motivation from raw pixel vision. 2015 Elsevier B.V. All rights reserved.
Keywords:Reinforcement learning | Artificial curiosity | Skill acquisition | Slow feature analysis | Continual learning | Incremental learning | iCub
مقاله انگلیسی
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