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6th Iberian robotics Conference (robot)

作者： Helenon, Francois Thiery, Stephane Nyiri, Eric Gibaru, Olivier Arts & Metiers Inst Technol LISPEN Lille France

ISBN: (纸本)9783031586750;9783031586767

While industrial collaborative robot programming becomes more and more accessible, building adaptable and modular behaviors is still out of reach for most non-programmer experts. Allowing any human to teach cobots complex and personalized behaviors with natural means of communication would likely ease their acceptability and long-term co-integration in industries of all sizes. In this paper, we present a prototype of robotic cognitive architecture for interactive task learning (ITL) integrating human preferences in a human-robot collaborative industrial context. The architecture is based on integrating connectionist modules, such as deep learning modules, and symbolic semantic graphs, such as behavior trees, for modular skill representations and learning. An experimental validation was made on a real UR10e industrial collaborative robot. The cobot is taught, online and incrementally, a simple task with variations based on human preferences.

关键词： interactive robot learning Smart Industrial Collaborative robot Symbolic and Connectionist Hybrid Architecture

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3rd Workshop on Human-interactive robot learning (HIRL) 24

3rd Workshop on Human-Interactive Robot Learning (HIRL)

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19th Annual ACM/IEEE International Conference on Human-robot Interaction (HRI)

作者： Racca, Mattia Mirsky, Reuth Senft, Emmanuel Xiao, Xuesu Idrees, Ifrah Kshirsagar, Alap Prakash, Ravi NAVER LABS Europe Meylan France Bar Ilan Univ Ramat Gan Israel Idiap Res Inst Martigny Switzerland George Mason Univ Everyday Robots Fairfax VA USA Brown Univ Providence RI USA Techn Univ Darmstadt Darmstadt Germany Indian Inst Sci Bangalore India

ISBN: (纸本)9798400703232

关键词： interactive robot learning Socially interactive learning learning from human input Socially intelligent robots

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2ndWorkshop on Human-interactive robot learning (HIRL) 23

2ndWorkshop on Human-Interactive Robot Learning (HIRL)

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18th Annual ACM/IEEE International Conference on Human-robot Interaction (HRI)

作者： Mirsky, Reuth Baraka, Kim Faulkner, Taylor Kessler Hart, Justin Yedidsion, Harel Xiao, Xuesu Idrees, Ifrah Gordon, Ethan K. Bar Ilan Univ Ramat Gan Israel Vrije Univ Amsterdam Amsterdam Netherlands Univ Washington Seattle WA USA Univ Texas Austin Austin TX USA Appl Mat Inc Santa Clara CA USA George Mason Univ Everday Robots Fairfax VA USA Brown Univ Providence RI USA

ISBN: (纸本)9781450399708

With robots poised to enter our daily environments, they will not only need to work for people, but also learn from them. An active area of investigation in the robotics, machine learning, and human-robot interaction communities is the design of teachable robots that can learn interactively from human input. To refer to these research efforts, we use the umbrella term Human-interactive robot learning (HIRL). While algorithmic solutions for robots learning from people have been investigated in a variety of ways, HIRL, as a fairly new research area, is still lacking: 1) a formal set of definitions to classify related but distinct research problems or solutions, 2) benchmark tasks, interactions, and metrics to evaluate the performance of HIRL algorithms and interactions, and 3) clear long-term research challenges to be addressed by different communities. Last year we began consolidating the needed definitions and vocabulary to enable fruitful discussions between researchers from these interdisciplinary fields, and identified a preliminary list of long, medium, and short-term research problems for the community to tackle, and existing tools and frameworks that can be leveraged to this end. This workshop will build upon these discussions, focusing on promoting the specification and design of HIRL benchmarks.

关键词： interactive robot learning Socially interactive learning learning from human input Socially intelligent robots

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Human-interactive robot learning (HIRL) 22

Human-Interactive Robot Learning (HIRL)

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17th Annual ACM/IEEE International Conference on Human-robot Interaction (HRI)

作者： Mirsky, Reuth Baraka, Kim Faulkner, Taylor Kessler Hart, Justin Yedidsion, Harel Xiao, Xuesu Univ Texas Austin Austin TX 78712 USA Bar Ilan Univ Ramat Gan Israel Vrije Univ Amsterdam Amsterdam Netherlands Appl Mat Inc Austin TX USA X Moonshot Factory Mountain View CA USA

ISBN: (数字)9781665407311

ISBN: (纸本)9781665407311

With robots poised to enter our daily environments, we conjecture that they will not only need to work for people, but also learn from them. An active area of investigation in the robotics, machine learning, and human-robot interaction communities is the design of teachable robotic agents that can learn interactively from human input. To refer to these research efforts, we use the umbrella term Human-interactive robot learning (HIRL). While algorithmic solutions for robots learning from people have been investigated in a variety of ways, HIRL, as a fairly new research area, is still lacking: 1) a formal set of definitions to classify related but distinct research problems or solutions, 2) benchmark tasks, interactions, and metrics to evaluate the performance of HIRL algorithms and interactions, and 3) clear long-term research challenges to be addressed by different communities. The main goal of this workshop will be to consolidate relevant recent work falling under the HIRL umbrella into a coherent set of long, medium, and short-term research problems, and identify the most pressing future research goals in this area. As HIRL is a developing research area, this workshop is an opportunity to break the existing boundaries between relevant research communities by developing and sharing a diverse set of benchmark tasks and metrics for HIRL, inspired by other fields including neuroscience, biology, and ethics research.

关键词： interactive robot learning Socially interactive learning learning from human input Socially intelligent robots

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interactive robot learning for Multimodal Emotion Recognition 11th

Interactive Robot Learning for Multimodal Emotion Recognitio...

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11th International Conference on Social robotics (ICSR)

作者： Yu, Chuang Tapus, Adriana ENSTA Paris Inst Polytech Paris Autonomous Syst & Robot Lab U2IS 828 Blvd Marechaux F-91120 Palaiseau France

ISBN: (纸本)9783030358884;9783030358877

Interaction plays a critical role in skills learning for natural communication. In human-robot interaction (HRI), robots can get feedback during the interaction to improve their social abilities. In this context, we propose an interactive robot learning framework using multimodal data from thermal facial images and human gait data for online emotion recognition. We also propose a new decision-level fusion method for the multimodal classification using Random Forest (RF) model. Our hybrid online emotion recognition model focuses on the detection of four human emotions (i.e., neutral, happiness, angry, and sadness). After conducting offline training and testing with the hybrid model, the accuracy of the online emotion recognition system is more than 10% lower than the offline one. In order to improve our system, the human verbal feedback is injected into the robot interactive learning. With the new online emotion recognition system, a 12.5% accuracy increase compared with the online system without interactive robot learning is obtained.

关键词： interactive robot learning Multimodal emotion recognition Human-robot interaction

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Modeling Variation in Human Feedback with User Inputs: An Exploratory Methodology 24

Modeling Variation in Human Feedback with User Inputs: An Ex...

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19th Annual ACM/IEEE International Conference on Human-robot Interaction (HRI)

作者： Huang, Jindan Aronson, Reuben M. Short, Elaine Schaertl Tufts Univ Medford MA 02155 USA

ISBN: (数字)9798400703225

ISBN: (纸本)9798400703225

To expedite the development process of interactive reinforcement learning (IntRL) algorithms, prior work often uses perfect oracles as simulated human teachers to furnish feedback signals. These oracles typically derive from ground-truth knowledge or optimal policies, providing dense and error-free feedback to a robot learner without delay. However, this machine-like feedback behavior fails to accurately represent the diverse patterns observed in human feedback, which may lead to unstable or unexpected algorithm performance in real-world human-robot interaction. To alleviate this limitation of oracles in oversimplifying user behavior, we propose a method for modeling variation in human feedback that can be applied to a standard oracle. We present a model with 5 dimensions of feedback variation identifed in prior work. This model enables the modifcation of feedback outputs from perfect oracles to introduce more human-like features. We demonstrate how each model attribute can impact on the learning performance of an IntRL algorithm through a simulation experiment. We also conduct a proof-of-concept study to illustrate how our model can be populated from people in two ways. The modeling results intuitively present the feedback variation among participants and help to explain the mismatch between oracles and human teachers. Overall, our method is a promising step towards refning simulated oracles by incorporating insights from real users.

关键词： human behavior modeling reinforcement learning human feedback interactive robot learning human-centered robotics

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OrthographicNet: A Deep Transfer learning Approach for 3-D Object Recognition in Open-Ended Domains

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2021年第6期26卷 2910-2921页

作者： Kasaei, S. Hamidreza Univ Groningen Dept Artificial Intelligence NL-9700 AK Groningen Netherlands

Nowadays, service robots are appearing more and more in our daily life. For this type of robot, open-ended object category learning and recognition is necessary since no matter how extensive the training data used for batch learning, the robot might be faced with a new object when operating in a real-world environment. In this article, we present OrthographicNet, a convolutional neural network based model, for 3-D object recognition in open-ended domains. In particular, OrthographicNet generates a global rotation- and scale-invariant representation for a given 3-D object, enabling robots to recognize the same or similar objects seen from different perspectives. Experimental results show that our approach yields significant improvements over the previous state-of-the-art approaches concerning object recognition performance and scalability in open-ended scenarios. Moreover, OrthographicNet demonstrates the capability of learning new categories from very few examples on-site. Regarding real-time performance, three real-world demonstrations validate the promising performance of the proposed architecture.

关键词： Three-dimensional displays robots Object recognition Training Feature extraction Training data Visualization 3-D object perception interactive robot learning open-ended learning

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Managing learning interactions for collaborative robot learning

Managing learning interactions for collaborative robot learn...

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作者： Bullard, Kalesha Georgia Institute of Technology

学位级别：博士

robotic assistants should be able to actively engage their human partner(s) to generalize knowledge about relevant tasks within their shared environment. Yet a key challenge is not all human partners will be proficient at teaching; furthermore, humans should not be held accountable for tracking a robot's knowledge over time in a dynamically changing environment, across multiple tasks. Thus, it is important to enable these interactive robots to characterize their own uncertainty and equip them with an information gathering policy for asking the appropriate questions of their human partners to resolve that uncertainty. In this way, the robot shares the responsibility in guiding its own learning process and is a collaborator in the learning. Additionally, given the robot requires some tutelage from its partner, awareness of constraints on the teacher's time and cognitive resources available for devoting to the interaction could help the agent to use the time allotted more wisely. This thesis examines the problem of enabling a robotic agent to leverage structured interaction with a human partner for acquiring concepts relevant to a task it must later perform. To equip the agent with the desired concept knowledge, we first explore the paradigm of learning from Demonstration for the acquisition of (1) training instances as examples of task-relevant concepts and (2) informative features for appropriately representing and discriminating between task-relevant concepts. Given empirical evidence that a human partner can be helpful to the agent in solving the concept learning problem, we subsequently investigate the design of algorithms that enable the robot learner to autonomously manage interaction with its human partner, using a questioning policy to actively gather both instance and feature information. This thesis seeks to investigate the following hypothesis: In the context of robot learning from human demonstrations in changeable and resource-constrained environments, en

关键词： interactive robot learning Active learning

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Abstraction in data-sparse task transfer

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ARTIFICIAL INTELLIGENCE 2021年 300卷 103551-103551页

作者： Fitzgerald, Tesca Goel, Ashok Thomaz, Andrea Carnegie Mellon Univ Pittsburgh PA 15213 USA Georgia Inst Technol Atlanta GA 30332 USA Univ Texas Austin Austin TX 78712 USA

When a robot adapts a learned task for a novel environment, any changes to objects in the novel environment have an unknown effect on its task execution. For example, replacing an object in a pick-and-place task affects where the robot should target its actions, but does not necessarily affect the underlying action model. In contrast, replacing a tool that the robot will use to complete a task will effectively alter its end-effector pose with respect to the robot's base coordinate system, and thus the robot's motion must be replanned accordingly. These examples highlight the relationship among (i) differences between the source and target environments, (ii) the level of abstraction at which a robot's task model should be represented to enable transfer to the target environment, and (iii) the information needed to ground the abstracted task representation in the target environment. In this article, we present a taxonomy of transfer problems based on this relationship. We also describe a knowledge representation called the Tiered Task Abstraction (TTA) and demonstrate its applicability to a variety of transfer problems in the taxonomy. Our experimental results indicate a trade-off between the generality and data requirements of a task representation, and reinforce the need for multiple transfer methods that operate at different levels of abstraction. (C) 2021 The Author(s). Published by Elsevier B.V.

关键词： Cognitive robotics Task transfer Task representation interactive robot learning Abstraction

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Curiosity-Driven Acquisition of Sensorimotor Concepts Using Memory-Based Active learning

Curiosity-Driven Acquisition of Sensorimotor Concepts Using ...

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IEEE International Conference on robotics and Biomimetics (ROBIO)

作者： Roa, Sergio Kruijff, Geert-Jan M. Jacobsson, Henrik DFKI GmbH German Res Ctr Artificial Intelligence Language Technol Lab Saarbrucken Germany

ISBN: (纸本)9781424426782

Operating in real-world environments, a robot will need to continuously learn from its experience to update and extend its knowledge. The paper focuses on the specific problem of how a robot can efficiently select information that is "interesting", driving the robot's "curiosity." The paper investigates the hypothesis that curiosity can be emulated through a combination of active learning, and reinforcement learning using intrinsic and extrinsic rewards. Intrinsic rewards quantify learning progress, providing a measure for "interestingness" of observations, and extrinsic rewards direct learning using the robot's interactions with the environment and other agents. The paper describes the approach, and experimental results obtained in simulated environments. The results indicate that both intrinsic and extrinsic rewards improve learning progress, measured in the number of training cycles to achieve a goal. The approach presented here extends previous approaches to curiosity-driven learning, by including both intrinsic and extrinsic rewards, and by considering more complex sensorimotor input.

关键词： Intrinsically motivated reinforcement learning interactive robot learning developmental robotics epigenetic robotics

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