A system for learning continuous human-robot interactions from human-human demonstrations

Vogt, David; Stepputtis, Simon; Grehl, Steve; Jung, Bernhard; Amor, Heni Ben

doi:10.1109/icra.2017.7989334

Cited by 65 publications

(43 citation statements)

References 17 publications

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“…Regarding the operational scenarios where LfD has so far shown significant advances, these involve the use of both manipulator (e.g. manufacturing [32], assistive [33], healthcare [34], social [35], etc.) and mobile (e.g.…”

Section: Challenges In Lfd Learningmentioning

confidence: 99%

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

2021

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Learning from Demonstration (LfD) constitutes one of the most robust methodologies for constructing efficient cognitive robotic systems. Despite the large body of research works already reported, current key technological challenges include those of multi-agent learning and long-term autonomy. Towards this direction, a novel cognitive architecture for multi-agent LfD robotic learning is introduced in this paper, targeting to enable the reliable deployment of open, scalable and expandable robotic systems in large-scale and complex environments. In particular, the designed architecture capitalizes on the recent advances in the Artificial Intelligence (AI) (and especially the Deep Learning (DL)) field, by establishing a Federated Learning (FL)-based framework for incarnating a multi-human multi-robot collaborative learning environment. The fundamental conceptualization relies on employing multiple AI-empowered cognitive processes (implementing various robotic tasks) that operate at the edge nodes of a network of robotic platforms, while global AI models (underpinning the aforementioned robotic tasks) are collectively created and shared among the network, by elegantly combining information from a large number of human-robot interaction instances. Regarding pivotal novelties, the designed cognitive architecture a) introduces a new FL-based formalism that extends the conventional LfD learning paradigm to support large-scale multi-agent operational settings, b) elaborates previous FL-based self-learning robotic schemes so as to incorporate the human in the learning loop and c) consolidates the fundamental principles of FL with additional sophisticated AI-enabled learning methodologies for modelling the multi-level inter-dependencies among the robotic tasks. The applicability of the proposed framework is explained using an example of a realworld industrial case study (subject to ongoing research activities) for agile production-based Critical Raw Materials (CRM) recovery. INDEX TERMSLearning from demonstration, human-robot interaction, artificial intelligence, federated learning VOLUME XX, XXXX This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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Section: Challenges In Lfd Learningmentioning

confidence: 99%

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

2021

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“…Combining the idea of human and robot models with the approach of Bourgin et al (2019) to pretrain contemporary machine learning models with cognitive models, we argue that humanoid robots could produce more human-like sensorimotor behavior that fosters interaction and adapts to the human partner. Considering the example of a human-robot handshake, cognitive models could be used to predict a user's movement selection (behavior prediction) and control the humanoid's motion execution (behavior generation) and also to align the robot's actions to the human partner, spatially and temporally (interaction adaptation; Wang et al, 2013;Vogt et al, 2017).…”

Section: Application Examples and Pitfallsmentioning

confidence: 99%

Personalizing Human-Agent Interaction Through Cognitive Models

Schürmann

Beckerle

2020

Front. Psychol.

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Cognitive modeling of human behavior has advanced the understanding of underlying processes in several domains of psychology and cognitive science. In this article, we outline how we expect cognitive modeling to improve comprehension of individual cognitive processes in human-agent interaction and, particularly, human-robot interaction (HRI). We argue that cognitive models offer advantages compared to data-analytical models, specifically for research questions with expressed interest in theories of cognitive functions. However, the implementation of cognitive models is arguably more complex than common statistical procedures. Additionally, cognitive modeling paradigms typically have an explicit commitment to an underlying computational theory. We propose a conceptual framework for designing cognitive models that aims to identify whether the use of cognitive modeling is applicable to a given research question. The framework consists of five external and internal aspects related to the modeling process: research question, level of analysis, modeling paradigms, computational properties, and iterative model development. In addition to deriving our framework from a concise literature analysis, we discuss challenges and potentials of cognitive modeling. We expect cognitive models to leverage personalized human behavior prediction, agent behavior generation, and interaction pretraining as well as adaptation, which we outline with application examples from personalized HRI.

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“…Based on the similarity of varying demonstrations, the learned forcebased skills were modularized and can be further combined for more complicated tasks. For other fine manipulation tasks such as assembly and surface-surface alignment, kinesthetic teaching with manual corrections was used to capture the important spatial relationships [25] or to encode the forcevelocity correlations [26]. In these researches, the relationship and distributions of position and force were used to guide the design of the task planner but not to adjust parameters of the force controller.…”

Section: Demonstrationsmentioning

confidence: 99%

Learning Force‐Relevant Skills from Human Demonstration

et al. 2019

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Many human manipulation skills are force relevant, such as opening a bottle cap and assembling furniture. However, it is still a difficult task to endow a robot with these skills, which largely is due to the complexity of the representation and planning of these skills. This paper presents a learning-based approach of transferring force-relevant skills from human demonstration to a robot. First, the force-relevant skill is encapsulated as a statistical model where the key parameters are learned from the demonstrated data (motion, force). Second, based on the learned skill model, a task planner is devised which specifies the motion and/or the force profile for a given manipulation task. Finally, the learned skill model is further integrated with an adaptive controller that offers task-consistent force adaptation during online executions. The effectiveness of the proposed approach is validated with two experiments, i.e., an object polishing task and a peg-in-hole assembly.

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A system for learning continuous human-robot interactions from human-human demonstrations

Cited by 65 publications

References 17 publications

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

Personalizing Human-Agent Interaction Through Cognitive Models

Learning Force‐Relevant Skills from Human Demonstration

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