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Nature Machine Intelligence 2025年 1-7页

作者： Aude Billard Alin Albu-Schaeffer Michael Beetz Wolfram Burgard Peter Corke Matei Ciocarlie Ravinder Dahiya Danica Kragic Ken Goldberg Yukie Nagai Davide Scaramuzza Learning Algorithms and Systems Laboratory (LASA) École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Institute of Robotics and Mechatronics DLR-German Aerospace Center/Department of Informatics Technical University of Munich Munich Germany Institute for Artificial Intelligence Computer Science Department University of Bremen Bremen Germany Department of Engineering University of Technology Nuremberg Nuremberg Germany Center for Robotics Queensland University of Technology Brisbane Queensland Australia Columbia University New York City NY USA Northeastern University Boston MA USA School of Computer Science and Communication Royal Institute of Technology Stockholm Sweden University of California Berkeley Berkeley CA USA International Research Center for Neurointelligence The University of Tokyo Tokyo Japan Robotics and Perception Group University of Zurich Zurich Switzerland

There is growing excitement about the potential of leveraging artificial intelligence (AI) to tackle some of the outstanding barriers to the full deployment of robots in daily lives. However, action and sensing in the physical world pose greater and different challenges for AI than analysing data in isolation and it is important to reflect on which AI approaches are most likely to be successfully applied to robots. Questions to address, among others, are how AI models can be adapted to specific robot designs, tasks and environments. This Perspective offers an assessment of what AI has achieved for robotics since the 1990s and proposes a research roadmap with challenges and promises. These range from keeping up-to-date large datasets, representatives of a diversity of tasks that robots may have to perform, and of environments they may encounter, to designing AI algorithms tailored specifically to robotics problems but generic enough to apply to a wide range of applications and transfer easily to a variety of robotic platforms. For robots to collaborate effectively with humans, they must predict human behaviour without relying on bias-based profiling. Explainability and transparency in AI-driven robot control are essential for building trust, preventing misuse and attributing responsibility in accidents. We close with describing what are, in our view, primary long-term challenges, namely, designing robots capable of lifelong learning, and guaranteeing safe deployment and usage, as well as sustainable development.

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Fast Adaptation with Meta-Reinforcement learning for Trust Modelling in Human-Robot Interaction

Fast Adaptation with Meta-Reinforcement Learning for Trust M...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yuan Gao Elena Sibirtseva Ginevra Castellano Danica Kragic Department of Information and Technology Uppsala University Sweden Robotics Perception and Learning Lab EECS at KTH Royal Institute of Technology Stockholm Sweden

In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on human-robot interaction. We present a meta-learning based policy gradient method for addressing the problem of adaptation in human-robot interaction and also investigate its role as a mechanism for trust modelling. By building an escape room scenario in mixed reality with a robot, we test our hypothesis that bi-directional trust can be influenced by different adaptation algorithms. We found that our proposed model increased the perceived trustworthiness of the robot and influenced the dynamics of gaining human's trust. Additionally, participants evaluated that the robot perceived them as more trustworthy during the interactions with the meta-learning based adaptation compared to the previously studied statistical adaptation model.

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Fast adaptation with meta-reinforcement learning for trust modelling in human-robot interaction

arXiv

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arXiv 2019年

作者： Gao, Yuan Sibirtseva, Elena Castellano, Ginevra Kragic, Danica Department of Information and Technology Uppsala University Sweden Robotics Perception and Learning Lab EECS KTH Royal Institute of Technology Stockholm Sweden

关键词： Human robot interaction

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The potential of artificial intelligence for achieving healthy and sustainable societies

arXiv

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arXiv 2022年

作者： Sirmacek, B. Gupta, S. Mallor, F. Azizpour, H. Ban, Y. Eivazi, H. Fang, H. Golzar, F. Leite, I. Melsion, G.I. Smith, K. Fuso Nerini, F. Vinuesa, Ricardo University/affiliation: Smart Cities School of Creative Technologies Saxion University of Applied Sciences Enschede Netherlands University/affiliation: Bonn Alliance for Sustainability Research University of Bonn Germany University/affiliation: FLOW Engineering Mechanics KTH Royal Institute of Technology Stockholm Sweden University/affiliation: Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden University/affiliation: Division of Geoinformatics KTH Royal Institute of Technology Stockholm Sweden University/affiliation: Division of Energy Systems Department of Energy Technology KTH Royal Institute of Technology Stockholm Sweden Climate Action Centre KTH Royal Institute of Technology Stockholm Sweden University/affiliation: Division of Energy Systems Department of Energy Technology KTH Royal Institute Sweden

In this chapter we extend earlier work (Vinuesa et al., Nature Communications 11, 2020)on the potential of artificial intelligence (AI) to achieve the 17 Sustainable Development Goals (SDGs) proposed by the United Nations (UN) for the 2030 Agenda. The present contribution focuses on three SDGs related to healthy and sustainable societies, *** 3 (on good health),SDG 11 (on sustainable cities) and SDG 13 (on climate action). This chapter extends the previous study within those three goals, and goes beyond the 2030 targets. These SDGs are selected because they are closely related to the coronavirus disease 19 (COVID-19) pandemic, and also to crises like climate change, which constitute important challenges to our society. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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Imitating by generating: Deep generative models for imitation of interactive tasks

arXiv

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arXiv 2019年

作者： Bütepage, Judith Ghadirzadeh, Ali Karadag, Özge Öztimur Björkman, Mårten Kragic, Danica Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden Intelligent Robotics Research Group Aalto University Espoo Finland Department of Computer Engineering Alanya Alaaddin Keykubat University Antalya Turkey

To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitation learning and active engagement with a skilled partner. They require the ability to predict and adapt to one's partner during an interaction. In this work we want to explore these ideas in a human-robot interaction setting in which a robot is required to learn interactive tasks from a combination of observational and kinesthetic learning. To this end, we propose a deep learning framework consisting of a number of components for (1) human and robot motion embedding, (2) motion prediction of the human partner and (3) generation of robot joint trajectories matching the human motion. To test these ideas, we collect human-human interaction data and human-robot interaction data of four interactive tasks "hand-shake", "hand-wave", "parachute fist-bump" and "rocket fist-bump". We demonstrate experimentally the importance of predictive and adaptive components as well as low-level abstractions to successfully learn to imitate human behavior in interactive social tasks. Copyright © 2019, The Authors. All rights reserved.

关键词： Human robot interaction

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Partial Caging: A Clearance-Based Definition and Deep learning

Partial Caging: A Clearance-Based Definition and Deep Learni...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Anastasiia Varava Michael C. Welle Jeffrey Mahler Ken Goldberg Danica Kragic Florian T. Pokomy Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of technology varavar AUTOLab Department of Electrical Engineenng and Computer Science University of California Berkeley

Caging grasps limit the mobility of an object to a bounded component of configuration space. We introduce a notion of partial cage quality based on maximal clearance of an escaping path. As this is a computationally demanding task even in a two-dimensional scenario, we propose a deep learning approach. We design two convolutional neural networks and construct a pipeline for real-time partial cage quality estimation directly from 2D images of object models and planar caging tools. One neural network, CageMaskNN, is used to identify caging tool locations that can support partial cages, while a second network that we call CageClearanceNN is trained to predict the quality of those configurations. A dataset of 3811 images of objects and more than 19 million caging tool configurations is used to train and evaluate these networks on previously unseen objects and caging tool configurations. Furthermore, the networks are trained jointly on configurations for both 3 and 4 caging tool configurations whose shape varies along a 1-parameter family of increasing elongation. In experiments, we study how the networks' performance depends on the size of the training dataset, as well as how to efficiently deal with unevenly distributed training data. In further analysis, we show that the evaluation pipeline can approximately identify connected regions of successful caging tool placements and we evaluate the continuity of the cage quality score evaluation along caging tool trajectories. Experiments show that evaluation of a given configuration on a GeForce GTX 1080 GPU takes less than 6 ms.

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Dual-arm in-hand manipulation and regrasping using dexterous manipulation graphs

arXiv

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arXiv 2019年

作者： Cruciani, Silvia Hang, Kaiyu Smith, Christian Kragic, Danica Division of Robotics Perception and Learning EECS KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical Engineering and Material Science Yale University New HavenCT United States

This work focuses on the problem of in-hand manipulation and regrasping of objects with parallel grippers. We propose Dexterous Manipulation Graph (DMG) as a representation on which we define planning for in-hand manipulation and regrasping. The DMG is a disconnected undirected graph that represents the possible motions of a finger along the object's surface. We formulate the in-hand manipulation and regrasping problem as a graph search problem from the initial to the final configuration. The resulting plan is a sequence of coordinated in-hand pushing and regrasping movements. We propose a dualarm system for the execution of the sequence where both hands are used interchangeably. We demonstrate our approach on an ABB Yumi robot tasked with different grasp reconfigurations. Copyright © 2019, The Authors. All rights reserved.

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learning to generate unambiguous spatial referring expressions for real-world environments

arXiv

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arXiv 2019年

作者： Doğan, Fethiye Irmak Kalkan, Sinan Leite, Iolanda The Division of Robotics Perception and Learning The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The KOVAN Research Lab The Department of Computer Engineering Middle East Technical University Ankara Turkey

Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is still mostly limited to rule-based methods. In this work, we propose a two-stage approach that relies on deep learning for estimating spatial relations to describe an object naturally and unambiguously with a referring expression. We compare our method to the state of the art algorithm in ambiguous environments (e.g., environments that include very similar objects with similar relationships). We show that our method generates referring expressions that people find to be more accurate (∼30% better) and would prefer to use (∼32% more often). © 2019, CC BY-NC-SA.

关键词： Human robot interaction

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Object Placement Planning and optimization for Robot Manipulators

Object Placement Planning and optimization for Robot Manipul...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Joshua A. Haustein Kaiyu Hang Johannes Stork Danica Kragic Division of Robotics Perception and Learning (RPL) CAS CSC KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical Engineering and Material Science Yale University New Haven USA Center for Applied Autonomous Sensor Systems (AASS) Örebro University Örebro Sweden

We address the problem of planning the placement of a rigid object with a dual-arm robot in a cluttered environment. In this task, we need to locate a collision-free pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot and c) optimizes a user-given placement objective. In addition, we need to select which robot arm to perform the placement with. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning with a novel hierarchical search for suitable placement poses. Our algorithm incrementally produces approach motions to stable placement poses, reaching placements with better objective as runtime progresses. We evaluate our approach for two different placement objectives, and observe its effectiveness even in challenging scenarios.

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Better faulty than sorry: Investigating social recovery strategies to minimize the impact of failure in human-robot interaction 1

Better faulty than sorry: Investigating social recovery stra...

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1st Workshop on Conversational Interruptions in Human-Agent Interactions, WCIHAI 2017

作者： Engelhardt, Sara Hansson, Emmeli Leite, Iolanda Department of Robotics Perception and Learning KTH Royal Institute of Technology Sweden

Failure happens in most social interactions, possibly even more so in interactions between a robot and a human. This paper investigates different failure recovery strategies that robots can employ to minimize the negative effect on people's perception of the robot. A between-subject Wizard-of-Oz experiment with 33 participants was conducted in a scenario where a robot and a human play a collaborative game. The interaction was mainly speech-based and controlled failures were introduced at specific moments. Three types of recovery strategies were investigated, one in each experimental condition: ignore (the robot ignores that a failure has occurred and moves on with the task), apology (the robot apologizes for failing and moves on) and problem-solving (the robot tries to solve the problem with the help of the human). Our results show that the apology-based strategy scored the lowest on measures such as likeability and perceived intelligence, and that the ignore strategy lead to better perceptions of perceived intelligence and animacy than the employed recovery strategies.

关键词： Recovery

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