121:(ELMs) in a guaranteed stable manner. Furthermore, the paper won the best student paper award. The networks represent movements, where asymptotic stability is incorporated through constraints derived from Lyapunov stability theory. It is shown that this approach successfully performs stable and smooth point-to-point movements learned from human handwriting movements.
89:
crucial point. To address this issue, the first attempts at generalizing the skill were mainly based on the help of the user through queries about the user's intentions. Then, different levels of abstractions were proposed to resolve the generalization issue, basically dichotomized in learning methods at a symbolic level or at a trajectory level.
48:(PbD) appeared in software development research as early as the mid 1980s to define a way to define a sequence of operations without having to learn a programming language. The usual distinction in literature between these terms is that in PbE the user gives a prototypical product of the computer execution, such as
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is stored in a database. Getting easier access to the raw data is realized with parameterized skills. A skill is requesting a database and generates a trajectory. For example, at first the skill “opengripper(slow)” is sent to the motion database and in response, the stored movement of the robotarm is
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It is also possible to learn the
Lyapunov candidate that is used for stabilization of the dynamical system. For this reason, neural learning scheme that estimates stable dynamical systems from demonstrations based on a two-stage process are needed: first, a data-driven Lyapunov function candidate is
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Research in PbD also progressively departed from its original purely engineering perspective to adopt an interdisciplinary approach, taking insights from neuroscience and social sciences to emulate the process of imitation in humans and animals. With the increasing consideration of this body of work
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These two terms were first undifferentiated, but PbE then tended to be mostly adopted by software development researchers while PbD tended to be adopted by robotics researchers. Today, PbE refers to an entirely different concept, supported by new programming languages that are similar to simulators.
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Diffeomorphic transformations turn out to be particularly suitable for substantially increasing the learnability of dynamical systems for robotic motions. The stable estimator of dynamical systems (SEDS) is an interesting approach to learn time invariant systems to control robotic motions. However,
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The PbD paradigm is first attractive to the robotics industry due to the costs involved in the development and maintenance of robot programs. In this field, the operator often has implicit knowledge on the task to achieve (he/she knows how to do it), but does not have usually the programming skills
88:
However, these PbD methods still used direct repetition, which was useful in industry only when conceiving an assembly line using exactly the same product components. To apply this concept to products with different variants or to apply the programs to new robots, the generalization issue became a
84:
methods that consisted basically in moving the robot (through a dedicated interface or manually) through a set of relevant configurations that the robot should adopt sequentially (position, orientation, state of the gripper). The method was then progressively ameliorated by focusing principally on
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naturally brought a growing interest in robot programming by demonstration. As a humanoid robot is supposed by its nature to adapt to new environments, not only the human appearance is important but the algorithms used for its control require flexibility and versatility. Due to the continuously
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estimated. Second, stability is incorporated by means of a novel method to respect local constraints in the neural learning. This allows for learning stable dynamics while simultaneously sustaining the accuracy of the dynamical system and robustly generate complex movements.
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movement in the 3d space, which consists of points. Single skills can be combined into a task for defining longer motion sequences from a high level perspective. For practical applications, different actions are stored in a
97:
changing environments and to the huge varieties of tasks that a robot is expected to perform, the robot requires the ability to continuously learn new skills and adapt the existing skills to new contexts.
69:(or the time) required to reconfigure the robot. Demonstrating how to achieve the task through examples thus allows to learn the skill without explicitly programming each detail.
117:
Neurally-imprinted Stable Vector Fields (NiVF) was introduced as a novel learning scheme during ESANN 2013 and show how to imprint vector fields into neurals networks such as
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this is restricted to dynamical systems with only quadratic
Lyapunov functions. The new approach Tau-SEDS overcomes this limitations in a mathematical elegant manner.
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technique for teaching a computer or a robot new behaviors by demonstrating the task to transfer directly instead of programming it through machine commands.
171:(DMP). They generate a robot trajectory on the fly which was unknown at the time of the demonstration. This helps to increase the flexibility of the solver.
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52:; while in PbD the user performs a sequence of actions that the computer must repeat, generalizing it to be used in different data sets.
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Alizadeh, Tohid; Saduanov, Batyrkhan (2017). "Robot programming by demonstration of multiple tasks within a common environment".
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384:"Learning Robot Motions with Stable Dynamical Systems under Diffeomorphic Transformations"
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provided. The parameters of a skill allow to modify the policy to fulfill external
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the teleoperation control and by using different interfaces such as vision.
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Schaal, S (1999), "Is imitation learning the route to humanoid robots?",
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2013 IEEE/RSJ International
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Machine
Learning techniques for Robot Programming by Demonstration
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Technical
Committee on Human-Robot Interaction & Coordination
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For final users, to automate a workflow in a complex tool (e.g.
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Billard, Aude (2008), "Robot
Programming by Demonstration",
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The first PbD strategies proposed in robotics were based on
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A. Lemme, K. Neumann, R. F. Reinhart, J. J. Steil (2013).
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Technique for teaching a computer or a robot new behaviors
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Reinforcement
Learning and Learning of Motor Primitives
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After a task was demonstrated by a human operator, the
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A skill is an interface between task names, given in
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675:IEEE Transactions on Systems, Man, and Cybernetics
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844:A robot that learns to unscrew a bottle of coke:
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537:Your Wish is My Command: Programming By Example
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785:, IEEE Robotics and Automation, archived from
776:Community activities on closely related topics
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318:A. Lemme, K. Neumann, and J. J. Steil (2013).
113:Neurally-imprinted Stable Vector Fields (NiVF)
728:, Lausanne, VD, CH: EPFL LASA, archived from
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642:Billard, A, "Imitation", in Arbib, MA (ed.),
516:Watch What I Do: Programming by Demonstration
644:Handbook of Brain Theory and Neural Networks
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416:Pervez, Affan and Lee, Dongheui (2018).
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764:Teaching air hockey to a humanoid robot
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183:), the most simple case of PbD is the
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56:This framework can be contrasted with
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382:K. Neumann and J. J. Steil (2015).
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64:Robot programming by demonstration
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129:Diffeomorphic Transformations
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425:Intelligent Service Robotics
46:programming by demonstration
25:programming by demonstration
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717:Key laboratories and people
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243:. U.C. Berkeley (PhD diss.)
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169:dynamic movement primitives
101:in robotics, the notion of
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667:Special issues in journals
636:Robots that imitate humans
476:. IEEE. pp. 608–613.
58:Bayesian program synthesis
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767:, JP: ATR, archived from
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534:Lieberman, Henry (2001),
437:10.1007/s11370-017-0235-8
337:10.1109/IROS.2013.6696505
119:Extreme Learning Machines
482:10.1109/mfi.2017.8170389
238:"Programming by Example"
849:"Unscrew Coke Bottle",
743:, SC, USA: USC CLMC Lab
202:Intentional programming
612:10.1098/rstb.2002.1258
512:Cypher, Allen (1993),
431:(1). Springer: 61–78.
331:. pp. 1216–1222.
263:Cite journal requires
197:Programming by example
41:programming by example
876:Programming paradigms
685:RSJ Advanced Robotics
207:Inductive programming
107:Learning by imitation
761:Bentivegna, Darrin,
138:Parameterized skills
33:end-user development
217:Supervised learning
158:and the underlying
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820:"Short Version",
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38:The terms
363:cite book
181:Photoshop
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