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Fusing Body Posture With Facial Expressions for Joint Recognition of Affect in Child-Robot Interaction

IEEE robotics AND automation LETTERS 2019年第4期4卷 4011-4018页

作者： Filntisis, Panagiotis Paraskevas Efthymiou, Niki Koutras, Petros Potamianos, Gerasimos Maragos, Petros Natl Tech Univ Athens Sch Elect & Comp Engn Zografos 15780 Greece Athena Res & Innovat Ctr Maroussi 15125 Greece Univ Thessaly Dept Elect & Comp Engn Volos 38221 Greece

In this letter, we address the problem of multi-cue affect recognition in challenging scenarios such as child-robot interaction. Toward this goal we propose a method for automatic recognition of affect that leverages body expressions alongside facial ones, as opposed to traditional methods that typically focus only on the latter. Our deep-learning based method uses hierarchical multi-label annotations and multi-stage losses, can be trained both jointly and separately, and offers us computational models for both individual modalities, as well as for the whole body emotion. We evaluate our method on a challenging child-robot interaction database of emotional expressions collected by us, as well as on the GEneva multimodal emotion portrayal public database of acted emotions by adults, and show that the proposed method achieves significantly better results than facial-only expression baselines.

关键词： Gesture posture and facial expressions computer vision for other robotic applications social human-robot interaction deep learning in robotics and automation

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Toward Affordance Detection and Ranking on Novel Objects for Real-World Robotic Manipulation

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IEEE robotics AND automation LETTERS 2019年第4期4卷 4070-4077页

作者： Chu, Fu-Jen Xu, Ruinian Seguin, Landan Vela, Patricio A. Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA 30332 USA

This letter presents a framework to detect and rank affordances of novel objects to assist with robotic manipulation tasks. The framework segments the affordance map of unseen objects using region-based affordance segmentation. Detected affordances define an initial state from which to generate action primitives for manipulation via the planning domain definition language (PDDL). The proposed category-agnostic affordance segmentation approach generalizes learned affordances to unseen objects by utilizing binary classification on proposed instance masks. The predicted pixel-wise level affordances are ranked by KL-divergence, augmenting the available affordance choices for manipulation tasks with non-primary affordances of an object. Experimental results show that the proposed method achieves state-of-the-art performance on affordance segmentation of novel objects, and outperforms baselines on affordance ranking. Actual robotic manipulation scenarios demonstrate the use of affordance detection with PDDL-generated action primitives for task execution. Prediction of ranked affordances on unseen objects provides flexibility to accomplish goal-oriented tasks.

关键词： Perception for grasping and manipulation deep learning in robotics and automation RGB-D perception

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PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis

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IEEE robotics AND automation LETTERS 2019年第4期4卷 4035-4041页

作者： Pan, Liang Wang, Pengfei Chew, Chee-Meng Natl Univ Singapore Dept Mech Engn Singapore 119077 Singapore Natl Univ Singapore Temasek Labs Singapore 117411 Singapore

In this letter, we propose a permutation-invariant architecture-PointAtrousNet (PAN), which focuses on exploiting multi-scale local geometric details for point cloud analysis. Inspired by atrous convolution in image domains, we propose the point atrous convolution (PAC) operation. Our PAC can effectively enlarge the receptive field of filters without introducing more parameters or increasing computation amount. In particular, we propose a novel point atrous spatial pyramid pooling module to explicitly exploit neighboring contextual information at multiple scales. Moreover, local geometric details are captured by constructing neighborhood graphs in metric and feature spaces. Experimental results show that our PAN achieves state-of-the-art performance on various point cloud inference applications.

关键词： deep learning in robotics and automation computer vision for other robotic applications

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deep Active Localization

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IEEE robotics AND automation LETTERS 2019年第4期4卷 4394-4401页

作者： Gottipati, Sai Krishna Seo, Keehong Bhatt, Dhaivat Mai, Vincent Murthy, Krishna Paull, Liam Univ Montreal Mila Montreal PQ H3T 1J4 Canada Univ Montreal DIRO Montreal PQ H3T 1J4 Canada Samsung Elect Co Ltd SAIT Suwon 16677 Gyeonggi Do South Korea

Active localization consists of generating robot actions that allow it to maximally disambiguate its pose 1% within a reference map. Traditional approaches use an information-theoretic criterion for action selection and hand-crafted perceptual models. In this work we propose an end-to-end differentiable method for learning to take informative actions that is trainable entirely in simulation and then transferable to real robot hardware with zero refinement. The system is composed of two learned modules: a convolutional neural network for perception, and a deep reinforcement learned planning module. We leverage a multi-scale approach in the perceptual model since the accuracy needed to take actions using reinforcement learning is much less than the accuracy needed for robot control. We demonstrate that the resulting system outperforms traditional approach for either perception or planning. We also demonstrate our approach's robustness to different map configurations and other nuisance parameters through the use of domain randomization in training. The code has been released: https://***/montrealrobotics/dal and is compatible with the OpenAI gym framework, as well as the Gazebo simulator.

关键词： Localization deep learning in robotics and automation motion and path planning AI-based methods

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Simulation of Robot-Assisted Flexible Needle Insertion Using deep Q-Network

Simulation of Robot-Assisted Flexible Needle Insertion Using...

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IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Lee, Yonggu Tan, Xiaoyu Chng, Chin-Boon Chui, Chee-Kong Natl Univ Singapore Singapore 117575 Singapore

ISBN: (纸本)9781728145693

Flexible needle insertion with bevel tips is becoming a preferred method for approaching targets in the human body in the least invasive manner. However, to successfully implement needle insertion, surgeons require prolonged training processes and long-term experience to develop essential handling skills. 'this paper presents a new path planning approach with deep Reinforcement learning (DRL) to implement automatic needle insertion using a surgical robot. In this paper, deep Q-Network (DQN) algorithm is utilized to learn the control policy for flexible needle steering with needle-tissue interaction. As the human body is composed of a complex environment such as tissues, blood vessels, bones, and muscles, the uncertainty of the needle-tissue interaction should be considered during insertion. To model this complex interaction in path planning, utilizing a neural network to approximate the action-value function is more efficient than using traditional array methods in terms of time and accuracy. In our simulation, the agent (needle) can he controlled with 2 degrees of freedom (bevel direction rotation and insertion) and received negative rewards when it collides with obstacles, goes out of range, or exceeds a predefined number of rotations. During the training, the agent demonstrates the accuracy and efficiency of the learned policy through feedback scores in every episode. In addition, this system incorporates the uncertainty within flexible needle-tissue interaction using a stochastic environment. Compared with other traditional methods for flexible needle path planning, we demonstrated that motion planning of bevel-tip flexible needles in complex human bodies using DRL has better efficiency and accuracy.

关键词： Surgical robotics: deep Reinforcement learning Flexible Needle Insertion Path Planning deep learning in robotics and automation AI Based Methods

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Using Augmentation to Improve the Robustness to Rotation of deep learning Segmentation in Robotic-Assisted Surgical Data

Using Augmentation to Improve the Robustness to Rotation of ...

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IEEE International Conference on robotics and automation (ICRA)

作者： Itzkovich, Danit Sharon, Yarden Jarc, Anthony Refaely, Yael Nisky, Ilana Ben Gurion Univ Negev Dept Biomed Engn Beer Sheva Israel Ben Gurion Univ Negev Zlotowski Ctr Neurosci Beer Sheva Israel Intuit Surg Inc Med Res Norcross GA USA Soroka Med Ctr Thorac Surg Unit Beer Sheva Israel

ISBN: (纸本)9781538660263

Robotic-Assisted Minimally Invasive Surgery allows for easy recording of kinematic data, and presents excellent opportunities for data-intensive approaches to assessment of surgical skill, system design, and automation of procedures. However, typical surgical cases result in long data streams, and therefore, automated segmentation into gestures is important. The public release of the JIGSAWS dataset allowed for developing and benchmarking data-intensive segmentation algorithms. However, this dataset is small and the gestures are similar in their structure and directions. This may limit the generalization of the algorithms to real surgical data that are characterized by movements in arbitrary directions. In this paper, we use a recurrent neural network to segment a suturing task, and demonstrate one such generalization problem - limited generalization to rotation. We propose a simple augmentation that can solve this problem without collecting new data, and demonstrate its benefit using: (1) the JIGSAWS dataset, and (2) a new dataset that we recorded with a da Vinci Research Kit. Our study highlights the prospect of using data augmentation in the analysis of kinematic data in surgical data science.

关键词： Surgical robotics: Laparoscopy deep learning in robotics and automation Rotation augmentation Network generalization

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Knowledge Creation Model for Emotion Based Response Generation for AI 19

Knowledge Creation Model for Emotion Based Response Generati...

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19th International Conference on Advances in ICT for Emerging Regions (ICTer)

作者： Premasundera, Udeshi K. Farook, Mohamed C. Informat Inst Technol Dept Comp Sci 57 Ramakrishna Rd Colombo 06 Sri Lanka

ISBN: (纸本)9781728151564

The traditional view on the nature of rationality has proposed that emotions and reasons do not influence decision making. Hence, socially assistive robot (SAR) and chatbots are designed to act rationally and do not take emotions into account throughout the reasoning process. Therefore, the current conversation systems frequently use the user's linguistic message in response generation. Such systems take into account factual and behavior related conversations, where these systems are unable to grasp the dynamic situations such as the user's emotional state when generating a response. Studies by psychologists have provided contrary to popular belief where memories, both semantic, or learned knowledge, and episodic, or personal experiences, play an important role in an individual's decision making. In this paper, authors present a system to support new findings which can enhance response generation regarding conversation flow while considering the actual emotional state of the user by analyzing the user's current emotional state and emotional memories.

关键词： Cognitive Human-Robot Interaction deep learning in robotics and automation Social Human-Robot Interaction

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A Multimodal Classifier Generative Adversarial Network for Carry and Place Tasks From Ambiguous Language Instructions

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3113-3120页

作者： Magassouba, Aly Sugiura, Komei Kawai, Hisashi Natl Inst Informat & Commun Technol Kyoto 6190289 Japan

This letter focuses on a multimodal language understanding method for carry-and-place tasks with domestic service robots. We address the case of ambiguous instructions, that is, when the target area is not specified. For instance "put away the milk and cereal" is a natural instruction where there is ambiguity regarding the target area, considering environments in daily life. Conventionally, this instruction can he disambiguated from a dialogue system, but at the cost of time and cumbersome interaction. Instead, we propose a multimodal approach, in which the instructions are disambiguated using the robot's state and environment context. We develop the Multi-Modal Classifier Generative Adversarial Network (MMC-GAN) to predict the likelihood of different target areas considering the robot's physical limitation and the target clutter. Our approach, MMC-GAN, significantly improves accuracy compared with baseline methods that use instructions only or simple deep neural networks.

关键词： deep learning in robotics and automation domestic robots robot audition

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DPC-Net: deep Pose Correction for Visual Localization

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IEEE robotics AND automation LETTERS 2018年第3期3卷 2424-2431页

作者： Peretroukhin, Valentin Kelly, Jonathan Univ Toronto Inst Aerosp Studies Space & Terr Autonomous Robot Syst Lab N York ON M3H 5T6 Canada

We present a novel method to fuse the power of deep networks with the computational efficiency of geometric and probabilistic localization algorithms. In contrast to other methods that completely replace a classical visual estimator with a deep network, we propose an approach that uses a convolutional neural network to learn difficult-to-model corrections to the estimator from ground-truth training data. To this end, we derive a novel loss function for learning SE(3) corrections based on a matrix Lie groups approach, with a natural formulation for balancing translation and rotation errors. We use this loss to train a deep pose correction network (DPC-Net) that predicts corrections for a particular estimator, sensor and environment. Using the KITTI odometry dataset, we demonstrate significant improvements to the accuracy of a computationally-efficient sparse stereo visual odometry pipeline, that render it as accurate as a modern computationally-intensive dense estimator. Further, we show how DPC-Net can be used to mitigate the effect of poorly calibrated lens distortion parameters.

关键词： deep learning in robotics and automation localization

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learning Context Flexible Attention Model for Long-Term Visual Place Recognition

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IEEE robotics AND automation LETTERS 2018年第4期3卷 4015-4022页

作者： Chen, Zetao Liu, Lingqiao Sa, Inkyu Ge, Zongyuan Chli, Margarita Swiss Fed Inst Technol Vis Robot Lab CH-8092 Zurich Switzerland Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia Swiss Fed Inst Technol Autonomous Syst Lab CH-8092 Zurich Switzerland Monash Univ ERes Ctr Melbourne Vic 3800 Australia

Identifying regions of interest in an image has long been of great importance in a wide range of tasks, including place recognition. In this letter, we propose a novel attention mechanism with flexible context, which can be incorporated into existing feed-forward network architecture to learn image representations for long-term place recognition. In particular, in order to focus on regions that contribute positively to place recognition, we introduce a multiscale context-flexible network to estimate the importance of each spatial region in the feature map. Our model is trained end-to-end for place recognition and can detect regions of interest of arbitrary shape. Extensive experiments have been conducted to verify the effectiveness of our approach and the results demonstrate that our model can achieve consistently better performance than the state of the art on standard benchmark datasets. Finally, we visualize the learned attention maps to generate insights into what attention the network has learned.

关键词： Localization deep learning in robotics and automation visual-based navigation

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