Robotic assembly solution by human-in-the-loop teaching method based on real-time stiffness modulation

Peternel, Luka; Petrič, Tadej; Babič, Jan

doi:10.1007/s10514-017-9635-z

Cited by 85 publications

(88 citation statements)

References 51 publications

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“…Learning of force-based robot skills from demonstrations has been shown in a variety of applications like door-opening and ironing [7], pouring from a bottle [8], screw driving [9], grasping [10], engraving [11], wood planing [12] or writing [13]. Further, in collaborative setups such as assembly [14] or transportation tasks [15], [16].…”

Section: Related Workmentioning

confidence: 99%

Learning Haptic Exploration Schemes for Adaptive Task Execution

Eiband

Saveriano

Lee

2019

2019 International Conference on Robotics and Automation (ICRA)

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The recent generation of compliant robots enables kinesthetic teaching of novel skills by human demonstration. This enables strategies to transfer tasks to the robot in a more intuitive way than conventional programming interfaces. Programming physical interactions can be achieved by manually guiding the robot to learn the behavior from the motion and force data. To let the robot react to changes in the environment, force sensing can be used to identify constraints and act accordingly. While autonomous exploration strategies in the whole workspace are time consuming, we propose a way to learn these schemes from human demonstrations in an object targeted manner. The presented teaching strategy and the learning framework allow to generate adaptive robot behaviors relying on the robot's sense of touch in a systematically changing environment. A generated behavior consists of a hierarchical representation of skills, where haptic exploration skills are used to touch the environment with the end effector, and relative manipulation skills, which are parameterized according to previous exploration events. The effectiveness of the approach has been proven in a manipulation task, where the adaptive task structure is able to generalize to unseen object locations. The robot autonomously manipulates objects without relying on visual feedback.

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Section: Related Workmentioning

confidence: 99%

Learning Haptic Exploration Schemes for Adaptive Task Execution

Eiband

Saveriano

Lee

2019

2019 International Conference on Robotics and Automation (ICRA)

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“…Inverse reinforcement learning (IRL), on the other hand, estimates a reward function based on the expert's demonstration [17,18], then utilizes the learned reward function to obtain the policy. Existing demonstration methods include visual demonstration [19][20][21][22][23], force demonstration [24][25][26], visual and force demonstration [27,28], and trajectory demonstration [29][30][31][32][33][34][35].…”

Section: Robot Learning From Demonstrationmentioning

confidence: 99%

Teaching Robots to Perform Construction Tasks via Learning from Demonstration

Liang¹,

Kamat²,

Menassa³

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Robots are expected to be widely used on future construction sites to assist human workers in the performance of repetitive physically-demanding tasks. Unlike typical manufacturing assembly lines, where parts are delivered to robots and workers in stationary workstations, construction robots and human workers must accumulate all necessary resources and repeatedly navigate to desired assembly locations on-site to perform useful work. The condition of such resources and the geometry of the environment are constantly changing and generally unstructured. As a result, the motion trajectories of any robot arms cannot be programmed beforehand. The robots must define the trajectory based on the encountered workspace geometry. Learning from Demonstration (LfD) methods have the potential to be used in teaching robots specific skills through human demonstration such that the robots can repeat the same process in different conditions. In this research, we explore the LfD method to teach robots to perform repetitive but geometrically adaptive construction tasks of installing suspended ceiling tiles within pre-assembled ceiling grids. The developed method translates the work context from the set of training videos demonstrated by humans to the target scene, then applies the reinforcement learning method to generate the policy for the robot to perform the subsequent ceiling tile installation. The first phase of the proposed method, i.e., the context translation model, is implemented and evaluated by characterizing whether robot-installed ceiling tiles are successfully moved to the grid area. The experiments demonstrate promising results that show the applicability of the LfD method in teaching robots to perform geometrically-adaptive construction tasks.

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“…In Schmidts et al (2011), authors used a teleoperation setup for teaching human grasping skills, by utilizing motion and force data. Teleoperation can be also utilized for online human-in-the-loop robot learning schemes (Peternel et al 2016(Peternel et al , 2018. In Peternel and Babic (2013), the authors employ Locally Weighted Projection Regression to gradually transfer control from a human demonstrator to an incrementally built autonomous controller.…”

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confidence: 99%

Motion encoding with asynchronous trajectories of repetitive teleoperation tasks and its extension to human-agent shared teleoperation

et al. 2019

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Teleoperating a robot for complex and intricate tasks demands a high mental workload from a human operator. Deploying multiple operators can mitigate this problem, but it can be also a costly solution. Learning from Demonstrations can reduce the human operator's burden by learning repetitive teleoperation tasks. Yet, the demonstrations via teleoperation tend to be inconsistent compared to other modalities of human demonstrations. In order to handle less consistent and asynchronous demonstrations effectively, this paper proposes a learning scheme based on Dynamic Movement Primitives. In particular, a new Expectation Maximization algorithm which synchronizes and encodes demonstrations with high temporal and spatial variances is proposed. Furthermore, we discuss two shared teleoperation architectures, where, instead of multiple human operators, a learned artificial agent and a human operator share authority over a task while teleoperating cooperatively. The agent controls the more mundane and repetitive motion in the task whereas human takes charge of the more critical and uncertain motion. The proposed algorithm together with the two shared teleoperation architectures (human-synchronized and agent-synchronized shared teleoperation) has been tested and validated through simulation and experiments on 3 Degrees-of-Freedom Phantom-to-Phantom teleoperation. Conclusively, the both proposed shared teleoperation architectures have shown superior performance when compared with the human-only teleoperation for a peg-in-hole task.

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Robotic assembly solution by human-in-the-loop teaching method based on real-time stiffness modulation

Cited by 85 publications

References 51 publications

Learning Haptic Exploration Schemes for Adaptive Task Execution

Learning Haptic Exploration Schemes for Adaptive Task Execution

Teaching Robots to Perform Construction Tasks via Learning from Demonstration

Motion encoding with asynchronous trajectories of repetitive teleoperation tasks and its extension to human-agent shared teleoperation

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