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Learning movement primitive attractor goals and sequential skills from kinesthetic demonstrations

ROBOTICS AND AUTONOMOUS SYSTEMS 2015年第PartA期74卷 97-107页

作者： Manschitz, Simon Kober, Jens Gienger, Michael Peters, Jan Tech Univ Darmstadt Inst Intelligent Autonomous Syst D-64289 Darmstadt Germany Honda Res Inst Europe D-63073 Offenbach Germany Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands

We present an approach for learning sequential robot skills through kinesthetic teaching. In our work, finding the transitions between consecutive movement primitives is treated as multiclass classification problem. We show how the goal parameters of linear attractor movement primitives can be learned from manually segmented and labeled demonstrations and how the observed movement primitive order can help to improve the movement reproduction. The improvement is achieved by restricting the classification result to the currently activated movement primitive and its possible successors in a graph representation of the sequence, which is also learned from the demonstrations. The approach is validated with three experiments using a Barrett WAM robot. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Human-robot interaction Kinesthetic teaching Learning from demonstration programming by demonstration

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Towards easy setup of robotic assembly tasks

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ADVANCED ROBOTICS 2020年第7-8期34卷 499-513页

作者： Sloth, Christoffer Kramberger, Aljaz Iturrate, Inigo Univ Southern Denmark SDU Robot MMMI Odense Denmark Univ Southern Denmark SDU Robot Odense Denmark

There is a growing need for adaptive robotic assembly systems that are fast to setup and reprogram when new products are introduced. The World Robot Challenge at World Robot Summit 2018 was centered around the challenge of setting up a flexible robotic assembly system aiming at changeover times below 1 day. This paper presents a method for programming robotic assembly tasks that was initiated in connection with the World Robot Challenge that enables fast and easy setup of robotic insertion tasks. We propose to program assembly tasks by demonstration, but instead of using the taught behavior directly, the demonstration is merged with assembly primitives to increase robustness. In contrast to other programming by demonstration approaches, we perform not only one demonstration but a sequence of four sub-demonstrations that are used to extract the desired robot trajectory in addition to parameters for the assembly primitive. The proposed assembly strategy is compared to a standard dynamic movement primitive and experiments show that the proposed assembly strategy increases the robustness towards pose uncertainties and significantly reduces the applied forces during the execution of the assembly task.

关键词： programming by demonstration dynamic movement primitives robotic assembly learning peg-in-hole assembly

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Collaborative programming of robotic task decisions and recovery behaviors

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AUTONOMOUS ROBOTS 2023年第2期47卷 229-247页

作者： Eiband, Thomas Willibald, Christoph Tannert, Isabel Weber, Bernhard Lee, Dongheui German Aerosp Ctr DLR Inst Robot & Mechatron Wessling Germany Tech Univ Munich TUM Dept Elect & Comp Engn Munich Germany

programming by demonstration is reaching industrial applications, which allows non-experts to teach new tasks without manual code writing. However, a certain level of complexity, such as online decision making or the definition of recovery behaviors, still requires experts that use conventional programming methods. Even though, experts cannot foresee all possible faults in a robotic application. To encounter this, we present a framework where user and robot collaboratively program a task that involves online decision making and recovery behaviors. Hereby, a task-graph is created that represents a production task and possible alternative behaviors. Nodes represent start, end or decision states and links define actions for execution. This graph can be incrementally extended by autonomous anomaly detection, which requests the user to add knowledge for a specific recovery action. Besides our proposed approach, we introduce two alternative approaches that manage recovery behavior programming and compare all approaches extensively in a user study involving 21 subjects. This study revealed the strength of our framework and analyzed how users act to add knowledge to the robot. Our findings proclaim to use a framework with a task-graph based knowledge representation and autonomous anomaly detection not only for initiating recovery actions but particularly to transfer those to a robot.

关键词： Learning from demonstration programming by demonstration Collaborative programming Interactive programming Anomaly detection Recovery behavior Task-graph Conditional task Force-based tasks Execution monitoring

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Can Human-Inspired Learning Behaviour Facilitate Human-Robot Interaction?

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INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS 2020年第1期12卷 173-186页

作者： Carfi, Alessandro Villalobos, Jessica Coronado, Enrique Bruno, Barbara Mastrogiovanni, Fulvio Univ Genoa Dept Informat Bioengn Robot & Syst Engn Via Opera Pia 13 I-16145 Genoa Italy

The evolution of production systems for smart factories foresees a tight relation between human operators and robots. Specifically, when robot task reconfiguration is needed, the operator must be provided with an easy and intuitive way to do it. A useful tool for robot task reconfiguration is programming by demonstration (PbD). PbD allows human operators to teach a robot new tasks by showing it a number of examples. The article presents two studies investigating the role of the robot in PbD. A preliminary study compares standard PbD with human-human teaching and suggests that a collaborative robot should actively participate in the teaching process as human practitioners typically do. The main study uses a wizard of oz approach to determine the effects of having a robot actively participating in the teaching process, specifically by controlling the end-effector. The results suggest that active behaviour inspired by humans can lead to a more intuitive PbD.

关键词： programming by demonstration Kinesthetic teaching Human robot interaction Industry 4 0

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Autonomous assembly planning of demonstrated skills with reinforcement learning in simulation

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AUTONOMOUS ROBOTS 2021年第8期45卷 1097-1110页

作者： De Winter, Joris El Makrini, Ilias Van de Perre, Greet Nowe, Ann Verstraten, Tom Vanderborght, Bram Vrije Univ Brussel Pl Laan 2 B-1050 Elsene Belgium Vrije Univ Brussel Brubot Elsene Belgium Vrije Univ Brussel AI Lab Elsene Belgium Flanders Make Lommel Belgium IMEC Leuven Belgium

Industrial robots used to assemble customized products in small batches require a lot of reprogramming. With this work we aim to reduce the programming complexity by autonomously finding the fastest assembly plans without any collisions with the environment. First, a digital twin of the robot uses a gym in simulation to learn which assembly skills (programmed by demonstration) are physically possible (i.e. no collisions with the environment). Only from this reduced solution space will the physical twin look for the fastest assembly plans. Experiments show that the system indeed converges to the fastest assembly plans. Moreover, pre-training in simulation drastically reduces the number of interactions before convergence compared to directly learning on the physical robot. This two-step procedure allows for the robot to autonomously find correct and fast assembly sequences, without any additional human input or mismanufactured products.

关键词： Reinforcement learning Digital twin Assembly planning programming by demonstration

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On-line motion synthesis and adaptation using a trajectory database

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ROBOTICS AND AUTONOMOUS SYSTEMS 2012年第10期60卷 1327-1339页

作者： Forte, Denis Gams, Andrej Morimoto, Jun Ude, Ales Jozef Stefan Inst Dept Automat Biocybernet & Robot Ljubljana 1000 Slovenia ATR Computat Neurosci Labs Dept Brain Robot Interface Seika Kyoto 6190288 Japan

Autonomous robots cannot be programmed in advance for all possible situations. Instead, they should be able to generalize the previously acquired knowledge to operate in new situations as they arise. A possible solution to the problem of generalization is to apply statistical methods that can generate useful robot responses in situations for which the robot has not been specifically instructed how to respond. In this paper we propose a methodology for the statistical generalization of the available sensorimotor knowledge in real-time. Example trajectories are generalized by applying Gaussian process regression, using the parameters describing a task as query points into the trajectory database. We show on real-world tasks that the proposed methodology can be integrated into a sensory feedback loop, where the generalization algorithm is applied in real-time to adapt robot motion to the perceived changes of the external world. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Robot learning programming by demonstration Real-time generalization of movements Statistical methods Gaussian process regression Locally weighted regression Dynamic movement primitives Kinesthetic guiding Example movements Radial basis functions

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iRoPro: An interactive Robot programming Framework

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INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS 2022年第1期14卷 177-191页

作者： Liang, Ying Siu Pellier, Damien Fiorino, Humbert Pesty, Sylvie Univ Grenoble Alpes LIG F-38000 Grenoble France

The great diversity of end-user tasks ranging from manufacturing environments to personal homes makes pre-programming robots for general purpose applications extremely challenging. In fact, teaching robots new actions from scratch that can be reused for previously unseen tasks remains a difficult challenge and is generally left up to robotics experts. In this work, we present iRoPro, an interactive Robot programming framework that allows end-users with little to no technical background to teach a robot new reusable actions. We combine programming by demonstration and Automated Planning techniques to allow the user to construct the robot's knowledge base by teaching new actions by kinesthetic demonstration. The actions are generalised and reused with a task planner to solve previously unseen problems defined by the user. We implement iRoPro as an end-to-end system on a Baxter Research Robot to simultaneously teach low- and high-level actions by demonstration that the user can customise via a Graphical User Interface to adapt to their specific use case. To evaluate the feasibility of our approach, we first conducted pre-design experiments to better understand the user's adoption of involved concepts and the proposed robot programming process. We compare results with post-design experiments, where we conducted a user study to validate the usability of our approach with real end-users. Overall, we showed that users with different programming levels and educational backgrounds can easily learn and use iRoPro and its robot programming process.

关键词： End-User robot programming programming by demonstration Automated planning Human-Robot interaction

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Recognizing Geometric Constraints in Human demonstrations Using Force and Position Signals

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IEEE ROBOTICS AND AUTOMATION LETTERS 2018年第2期3卷 1252-1259页

作者： Subramani, Guru Gleicher, Michael Zinn, Michael Univ Wisconsin Dept Mech Engn Madison WI 53706 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

This letter introduces a method for recognizing geometric constraints from human demonstrations using both position and force measurements. Our key idea is that position information alone is insufficient to determine that a constraint is active and reaction forces must also be considered to correctly distinguish constraints from movements that just happen to follow a particular geometric shape. Our techniques can detect multiple plane, arc, and line constraints in a single demonstration. Our method uses the principle of virtual work to determine reaction forces from force and position data. It fits geometric constraints locally and clusters these over the whole motion for global constraint recognition. Experimental evaluations compare our force and position constraint inference technique with a similar position-only technique and conclude that force measurements are essential in eliminating false positive detections of constraints in free space.

关键词： Learning from demonstration recognition programming by demonstration constraint inference recognizing geometry constraint theory natural demonstrations

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Skill learning and action recognition by arc-length dynamic movement primitives

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ROBOTICS AND AUTONOMOUS SYSTEMS 2018年 100卷 225-235页

作者： Gaspar, Timotej Nemec, Bojan Morimoto, Jun Ude, Aleg Jozef Stefan Inst Dept Automat Biocybernet & Robot Humanoid & Cognit Robot Lab Jamova Cesta 39 Ljubljana 1000 Slovenia ATR Computat Neurosci Labs Dept Brain Robot Interface 2-2-2 HikaridaiSeika Cho Kyoto 6190288 Japan

Effective robot programming by demonstration requires the availability of multiple demonstrations to learn about all relevant aspects of the demonstrated skill or task. Typically, a human teacher must demonstrate several variants of the desired task to generate a sufficient amount of data to reliably learn it. Here a problem often arises that there is a large variability in the speed of execution across human demonstrations. This can cause problems when multiple demonstrations are compared to extract the relevant information for learning. In this paper we propose an extension of dynamic movement primitives called arc-length dynamic movement primitives, where spatial and temporal components of motion are well separated. We show theoretically and experimentally that the proposed representation can be effectively applied for robot skill learning and action recognition even when there are large variations in the speed of demonstrated movements. (C) 2017 Elsevier B.V. All rights reserved.

关键词： programming by demonstration Skill learning Action recognition Dynamic movement primitives

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Guided latent space regression for human motion generation

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ROBOTICS AND AUTONOMOUS SYSTEMS 2013年第4期61卷 340-350页

作者： Avizzano, Carlo Alberto Scuola Super Sant Anna Inst Telecommun Informat Technol & Perceptual Rob PERCRO Lab Pisa Italy

In the present work, we describe a mathematical model to generate human-like motion trajectories in space. We use linear regression in a latent space to find the model parameters from a set of demonstration examples. The learning procedure requires a relevant set of similar examples. The apprehended models encode both the typical shapes of motion and their variability towards specific boundary conditions (BC). We will show the added value of encoding both properties in a unique model and we apply this ability to common problems of error compensation and target tracking. The models allow us to describe human motion using expansion-function series (EFS), thus avoiding typical stability issues that arise in the use of differential equation models. To cope with variable scenarios, we show two specific algorithms that morph and adapt the evolution trajectory. In analogy to splines, the EFS preserve an analytical structure on which we develop the optimisation steps. In such a way, we managed to combine multiple single segments into complex motions that preserve continuity and may simultaneously optimise other criteria. In the present work, after having analysed similar tools, we present the basic model and its features. Then we develop a robust tool to gather the model from examples, and to achieve real-time trajectory adaptation. The achieved results will be analysed through an experimental analysis on data collected in a ball catching experiment. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Motion models Trajectory generation programming by demonstration Robust regression Trajectory adaptation

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