The high request for autonomous and flexible HRI implies the necessity of deploying Machine Learning (ML) mechanisms in the robot control. Indeed, the use of ML techniques, such as Reinforcement Learning (RL), makes the robot behaviour, during the learning process, not transparent to the observing user. In this work, we proposed an emotional model to improve the transparency in RL tasks for human-robot collaborative scenarios. The architecture we propose supports the RL algorithm with an emotional model able to both receive human feedback and exhibit emotional responses based on the learning process. The model is entirely based on the Temporal Difference (TD) error. The architecture was tested in an isolated laboratory with a simple setup. The results highlight that showing its internal state through an emotional response is enough to make a robot transparent to its human teacher. People also prefer to interact with a responsive robot because they are used to understand their intentions via emotions and social signals.
Physical activity is important for health and wellbeing, but only few fulfill the World Health Organization's criteria for physical activity. The development of a robotic exercise trainer can assist in increasing training accessibility and motivation. The acceptance and trust of users are crucial for the successful implementation of such an assistive robot. This can be affected by the transparency of the robotic system and the robot's performance, specifically, its failures. The study presents an initial investigation into the transparency levels as related to the task, human, robot, and inter…
This paper describes a Google Educator funded project devoted to the training of teachers (primary and secondary school) through an e-learning platform that will introduce them to educational robotics using Wolly, a social, educational and affective robot.
Interdepartmental Center for Advances in Robotic Surgery - ICAROS（机器人外科先进跨部门中心 - ICAROS）；University of Naples Federico II（那不勒斯费德里科二世大学）；Center for Cognitive Interaction Technology - CITEC（认知交互技术中心 - CITEC）；Bielefeld University（比勒费尔德大学）
Conservative Bias in Multi-Teacher Learning: Why Agents Prefer Low-Reward Advisors
School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Escuela de Ingeniería, Universidad Central de Chile（智利中央大学工程学院）
This study provides user-studies aimed at exploring factors influencing the interaction between older adults and a robotic table setting assistant. The in-fluence of the level of automation (LOA) and level of transparency (LOT) on the quality of the interaction was considered. Results revealed that the interaction effect of LOA and LOT significantly influenced the interaction. A lower LOA which required the user to control some of the actions of the robot influenced the older adults to participate more in the interaction when the LOT was low com-pared to situations with higher LOT (more inform…
CITEC；Bielefeld University（比勒费尔德大学）；Honda Research Institute Europe（本田欧洲研究院）
In recent years, an increased effort has been invested to improve the capabilities of robots. Nevertheless, human-robot interaction remains a complex field of application where errors occur frequently. The reasons for these errors can primarily be divided into two classes. Foremost, the recent increase in capabilities also widened possible sources of errors on the robot's side. This entails problems in the perception of the world, but also faulty behavior, based on errors in the system. Apart from that, non-expert users frequently have incorrect assumptions about the functionality and limitati…
Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Haute École Pédagogique du canton de Vaud (HEP-VD)（沃州高等教育学院）；SUPSI-DFA（瑞士南部应用科学与艺术大学-师范系）
With the introduction of educational robotics (ER) and computational thinking (CT) in classrooms, there is a rising need for operational models that help ensure that CT skills are adequately developed. One such model is the Creative Computational Problem Solving Model (CCPS) which can be employed to improve the design of ER learning activities. Following the first validation with students, the objective of the present study is to validate the model with teachers, specifically considering how they may employ the model in their own practices. The Utility, Usability and Acceptability framework wa…
A key challenge in Imitation Learning (IL) is that optimal state actions demonstrations are difficult for the teacher to provide. For example in robotics, providing kinesthetic demonstrations on a robotic manipulator requires the teacher to control multiple degrees of freedom at once. The difficulty of requiring optimal state action demonstrations limits the space of problems where the teacher can provide quality feedback. As an alternative to state action demonstrations, the teacher can provide corrective feedback such as their preferences or rewards. Prior work has created algorithms designe…
Reinforcement learning (RL) has been shown to be effective at learning control from experience. However, RL typically requires a large amount of online interaction with the environment. This limits its applicability to real-world settings, such as in robotics, where such interaction is expensive. In this work we investigate ways to minimize online interactions in a target task, by reusing a suboptimal policy we might have access to, for example from training on related prior tasks, or in simulation. To this end, we develop two RL algorithms that can speed up training by using not only the acti…
Applications of rolling diaphragm transmissions for medical and teleoperated robotics are of great interest, due to the low friction of rolling diaphragms combined with the power density and stiffness of hydraulic transmissions. However, the stiffness-enabling pressure preloads can form a tradeoff against bearing loading in some rolling diaphragm layouts, and transmission setup can be difficult. Utilization of cable drives compliment the rolling diaphragm transmission's advantages, but maintaining cable tension is crucial for optimal and consistent performance. In this paper, a coaxial opposed…
In unstructured environments, robots run the risk of unexpected collisions. How well they react to these events is determined by how transparent they are to collisions. Transparency is affected by structural properties as well as sensing and control architectures. In this paper, we propose the collision reflex metric as a way to formally quantify transparency. It is defined as the total impulse transferred in collision, which determines the collision mitigation capabilities of a closed-loop robotic system taking into account structure, sensing, and control. We analyze the effect of motor scali…
Japan Advanced Institute of Science and Technology (JAIST)（日本北陆先端科学技术大学院大学（JAIST））；Hanoi University of Industry（河内工业大学）；Japan Science and Technology Agency（日本科学技术振兴机构）
Robots have been brought to work close to humans in many scenarios. For coexistence and collaboration, robots should be safe and pleasant for humans to interact with. To this end, the robots could be both physically soft with multimodal sensing/perception, so that the robots could have better awareness of the surrounding environment, as well as to respond properly to humans' action/intention. This paper introduces a novel soft robotic link, named ProTac, that possesses multiple sensing modes: tactile and proximity sensing, based on computer vision and a functional material. These modalities co…
ReFineVLA: Multimodal Reasoning-Aware Generalist Robotic Policies via Teacher-Guided Fine-Tuning
VinRobotics；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Max Planck Research School for Intelligent Systems (IMPRS-IS)（智能系统Max Planck研究学校）；Intelligent Autonomous Systems Lab（智能自主系统实验室）；TU Darmstadt（德累斯顿技术大学）；Automation & Control Institute（自动化与控制研究所）；TU Wien（维也纳技术大学）；Austrian Institute of Technology (AIT)（奥地利技术研究所）
The Chinese University of Hong Kong（香港中文大学）；University of California, Berkeley（加州大学伯克利分校）
In this paper, we propose an iterative self-training framework for sim-to-real 6D object pose estimation to facilitate cost-effective robotic grasping. Given a bin-picking scenario, we establish a photo-realistic simulator to synthesize abundant virtual data, and use this to train an initial pose estimation network. This network then takes the role of a teacher model, which generates pose predictions for unlabeled real data. With these predictions, we further design a comprehensive adaptive selection scheme to distinguish reliable results, and leverage them as pseudo labels to update a student…
Robots can learn preferences from human demonstrations, but their success depends on how informative these demonstrations are. Being informative is unfortunately very challenging, because during teaching, people typically get no transparency into what the robot already knows or has learned so far. In contrast, human students naturally provide a wealth of nonverbal feedback that reveals their level of understanding and engagement. In this work, we study how a robot can similarly provide feedback that is minimally disruptive, yet gives human teachers a better mental model of the robot learner, a…
University of Applied Sciences and Arts of Southern Switzerland (SUPSI)（瑞士南部应用科学与艺术大学）；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Temporary school closures caused by the Covid-19 pandemic have posed new challenges for many teachers and students worldwide. Especially the abrupt shift to online distance learning posed many obstacles to be overcome and it particularly complicated the implementation of Educational Robotics activities. Such activities usually comprise a variety of different learning artifacts, which were not accessible to many students during the period of school closure. Moreover, online distance learning considerably limits the possibilities for students to interact with their peers and teachers. In an atte…
Learning to solve complex manipulation tasks from visual observations is a dominant challenge for real-world robot learning. Although deep reinforcement learning algorithms have recently demonstrated impressive results in this context, they still require an impractical amount of time-consuming trial-and-error iterations. In this work, we consider the promising alternative paradigm of interactive learning in which a human teacher provides feedback to the policy during execution, as opposed to imitation learning where a pre-collected dataset of perfect demonstrations is used. Our proposed CEILin…
DRBA: Dynamic Robotic Balance Assistant -- An assist-as-needed gait and balance rehabilitation robot for versatile training
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航天工程学院）；Guangdong Zhongxin Intelligent Rehabilitation Research Institute（广东众心智能康复研究院）；Guangdong Jianxiang Hospital Group（广东健翔医院集团）
Providing explanations of chosen robotic actions can help to increase the transparency of robotic planning and improve users' trust. Social sciences suggest that the best explanations are contrastive, explaining not just why one action is taken, but why one action is taken instead of another. We formalize the notion of contrastive explanations for robotic planning policies based on Markov decision processes, drawing on insights from the social sciences. We present methods for the automated generation of contrastive explanations with three key factors: selectiveness, constrictiveness, and respo…
CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion
School of System Design and Intelligent Manufacturing (SDIM), Southern University of Science and Technology（南方科技大学系统设计与智能制造学院）；LimX Dynamics（灵蜥动力）；Zhejiang University-University of Illinois Urbana-Champaign Institute (ZJUI)（浙江大学-伊利诺伊大学厄巴纳-香槟分校学院）
We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions obser…
In robotics, there is need of an interactive and expedite learning method as experience is expensive. Robot Learning from Demonstration (RLfD) enables a robot to learn a policy from demonstrations performed by teacher. RLfD enables a human user to add new capabilities to a robot in an intuitive manner, without explicitly reprogramming it. In this work, we present a novel interactive framework, where a collaborative robot learns skills for trajectory based tasks from demonstrations performed by a human teacher. The robot extracts features from each demonstration called as key-points and learns …
Electrical and Computer Engineering, The University of Auckland（奥克兰大学电气与计算机工程学院）；School of Computer Science, University of Birmingham（伯明翰大学计算机学院）
A robot's ability to provide descriptions of its decisions and beliefs promotes effective collaboration with humans. Providing such transparency is particularly challenging in integrated robot systems that include knowledge-based reasoning methods and data-driven learning algorithms. Towards addressing this challenge, our architecture couples the complementary strengths of non-monotonic logical reasoning, deep learning, and decision-tree induction. During reasoning and learning, the architecture enables a robot to provide on-demand relational descriptions of its decisions, beliefs, and the out…
This paper presents a novel method for transferring motion planning and control policies between a teacher and a learner robot. With this work, we propose to reduce the sim-to-real gap, transfer knowledge designed for a specific system into a different robot, and compensate for system aging and failures. To solve this problem we introduce a Schwarz-Christoffel mapping-based method to geometrically stretch and fit the control inputs from the teacher into the learner command space. We also propose a method based on primitive motion generation to create motion plans and control inputs compatible …
The Xacro XML macro language extends the Universal Robot Description Format (URDF) and is part of a critical toolchain from geometric representations to simulation, visualization, and system execution. However, members of the robotics community, especially newcomers, struggle to troubleshoot and understand the interplay between systems and the Xacro preprocessing pipeline. To better understand how system developers struggle with Xacros, we manually examine 712 Xacro-related questions from the question and answer site http://answers.ros.org and find Xacro misunderstandings fit into 10 key categ…
University of Maryland（马里兰大学）；Syracuse University（雪城大学）
Imitation learning allows social robots to learn new skills from human teachers without substantial manual programming, but it is difficult for robotic imitation learning systems to generalize demonstrated skills as well as human learners do. Contemporary neurocomputational approaches to imitation learning achieve limited generalization at the cost of data-intensive training, and often produce opaque models that are difficult to understand and debug. In this study, we explore the viability of developing purely-neural controllers for social robots that learn to imitate by reasoning about the un…
DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks
University of Luxembourg（卢森堡大学）；Interdisciplinary Centre for Security, Reliability, and Trust (SnT)（安全、可靠性与跨学科信任中心（SnT））；Faculty of Science, Technology, and Medicine（科学、技术与医学学院）
This paper describes the design, manufacture, and performance of a highly dexterous, low-profile, 7 Degree-of-Freedom (DOF) robotic arm for CT-guided percutaneous needle biopsy. Direct CT guidance allows physicians to localize tumours quickly; however, needle insertion is still performed by hand. This system is mounted to a fully active gantry superior to the patient's head and teleoperated by a radiologist. Unlike other similar robots, this robot's fully serial-link approach uses a unique combination of belt and cable drives for high-transparency and minimal-backlash, allowing for an expansiv…
Overcoming Imperfect Kinematics in Surgical Robotics Through Sim-to-Real Visuomotor Learning
Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London（伦敦帝国理工学院全球健康创新研究所哈姆林机器人手术中心）；Department of Surgery and Cancer, Imperial College London（伦敦帝国理工学院外科与癌症系）；Department of Mechanical Engineering, Imperial College London（伦敦帝国理工学院机械工程系）
