Efficient behavior learning in human–robot collaboration

Munzer, Thibaut; Toussaint, Marc; Lopes, Manuel

doi:10.1007/s10514-017-9674-5

Cited by 29 publications

(16 citation statements)

References 18 publications

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“…For this demonstrations, a dynamical system was used to capture the position and orientation of the human wrist, which was then mirrored to the robot motion. Another research development that uses both training datasets and execution behavior as sources for teaching the robot is presented in [104]. The robot used in the study could start acting autonomously based on a Markov Decision Process, where some reward functions would dictate the confidence on the task that the robot was about to perform.…”

Section: Robot Learningmentioning

confidence: 99%

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Castro

Silva

Santos

2021

Sensors

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Repetitive industrial tasks can be easily performed by traditional robotic systems. However, many other works require cognitive knowledge that only humans can provide. Human-Robot Collaboration (HRC) emerges as an ideal concept of co-working between a human operator and a robot, representing one of the most significant subjects for human-life improvement.The ultimate goal is to achieve physical interaction, where handing over an object plays a crucial role for an effective task accomplishment. Considerable research work had been developed in this particular field in recent years, where several solutions were already proposed. Nonetheless, some particular issues regarding Human-Robot Collaboration still hold an open path to truly important research improvements. This paper provides a literature overview, defining the HRC concept, enumerating the distinct human-robot communication channels, and discussing the physical interaction that this collaboration entails. Moreover, future challenges for a natural and intuitive collaboration are exposed: the machine must behave like a human especially in the pre-grasping/grasping phases and the handover procedure should be fluent and bidirectional, for an articulated function development. These are the focus of the near future investigation aiming to shed light on the complex combination of predictive and reactive control mechanisms promoting coordination and understanding. Following recent progress in artificial intelligence, learning exploration stand as the key element to allow the generation of coordinated actions and their shaping by experience.

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Section: Robot Learningmentioning

confidence: 99%

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Castro

Silva

Santos

2021

Sensors

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“…For example, in [20], the user is assembling a system of pipes; the robot has to understand in which step the user is currently at and grabs the pipe related to that step of the assembly. In [32], the robot is helping the user assembling a box, through the understanding of what it is doing. In other cases, instead, the robot predicts the next step of the assembly process and prepare the scenario for the user to perform it later.…”

Section: Human-robot Collaboration Tasksmentioning

confidence: 99%

“…A main reason for this is that vision allows the system to potentially get information from different elements of the environment at the same time. For instance, [32] uses vision to acquire information related to the robot and the users in the experiments. In fact, through vision the robot becomes aware of the user's position in the environment and physical features.…”

Section: Robot's Sensing Modalitiesmentioning

confidence: 99%

Human-robot collaboration and machine learning: a systematic review of recent research

Semeraro¹,

Griffiths²,

Cangelosi³

2021

Preprint

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Technological progress increasingly envisions the use of robots interacting with people in everyday life. Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot, during the completion of an actual physical task. Such interplay is explored both at the cognitive and physical level, by respectively analysing the mutual exchange of information and mechanical power. In HRC works, a cognitive model is typically built, which collects inputs from the environment and from the user, elaborates and translates these into information that can be used by the robot itself. HRC studies progressively employ machine learning algorithms to build the cognitive models and behavioural block that elaborates the acquired external inputs. This is a promising approach still in its early stages and with the potential of significant benefit from the growing field of machine learning. Consequently, this paper proposes a thorough literature review of the use of machine learning techniques in the context of human-robot collaboration. The collection, selection and analysis of the set of 45 key papers, selected from the wide review of the literature on robotics and machine learning, allowed the identification of the current trends in HRC. In particular, a clustering of works based on the type of collaborative tasks, evaluation metrics and cognitive variables modelled is proposed. With these premises, a deep analysis on different families of machine learning algorithms and their properties, along with the sensing modalities used, was carried out. The salient aspects of the analysis are discussed to show trends and suggest possible challenges to tackle in the future research. Highlights• Machine learning is assuming a progressively important role in human-robot collaboration.• Categorizations of human-collaboration experiments based on the type of tasks, evaluation metrics, and cognitive variables considered are proposed.• Reinforcement learning is primarily used, although many researchers still use machine learning to produce an output used to select routines for the robot to perform.• It is crucial to make machine learning algorithms sensitive to time dependencies.

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“…The procedure of the shared task with its designated roles was then modeled by adopting the Relational Action Processes (RAP) established by Toussaint et al (2016) . Through the usage of the relational Markov Decision Process, which is commonly implemented for decision processes of agents performing within an environment, the model enabled the simultaneous operation of several actions, either sequential or asynchronously, depending on the current requirement ( Munzer et al, 2018 ). Additional information from two agents monitoring the states of the pin-back button press tracked the current progress of the task and the speed at which the participant conducted it, were used to enable the robot arm to adapt its movement speed to the working pace of the human partner.…”

Section: Experimental Studymentioning

confidence: 99%

A Virtual Sandbox Approach to Studying the Effect of Augmented Communication on Human-Robot Collaboration

2021

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Human-Robot Collaboration (HRC) has the potential for a paradigm shift in industrial production by complementing the strengths of industrial robots with human staff. However, exploring these scenarios in physical experimental settings is costly and difficult, e.g., due to safety considerations. We present a virtual reality application that allows the exploration of HRC work arrangements with autonomous robots and their effect on human behavior. Prior experimental studies conducted using this application demonstrated the benefits of augmenting an autonomous robot arm with communication channels on subjective aspects such as perceived stress. Motivated by current safety regulations that hinder HRC to expand its full potential, we explored the effects of the augmented communication on objective measures (collision rate and produced goods) within a virtual sandbox application. Explored through a safe and replicable setup, the goal was to determine whether communication channels that provide guidance and explanation on the robot can help mitigate safety hazards without interfering with the production effectiveness of both parties. This is based on the theoretical foundation that communication channels enable the robot to explain its action, helps the human collaboration partner to comprehend the current state of the shared task better, and react accordingly. Focused on the optimization of production output, reduced collision rate, and increased perception of safety, a between-subjects experimental study with two conditions (augmented communication vs non-augmented) was conducted. The results revealed a statistically significant difference in terms of production quantity output and collisions with the robot, favoring the augmented conditions. Additional statistically significant differences regarding self-reported perceived safety were found. The results of this study provide an entry point for future research regarding the augmentation of industrial robots with communication channels for safety purposes.

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Efficient behavior learning in human–robot collaboration

Cited by 29 publications

References 18 publications

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Human-robot collaboration and machine learning: a systematic review of recent research

A Virtual Sandbox Approach to Studying the Effect of Augmented Communication on Human-Robot Collaboration

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