Contact Skill Imitation Learning for Robot-Independent Assembly Programming

Scherzinger, Stefan; Roennau, Arne; Dillmann, Rüdiger

doi:10.1109/iros40897.2019.8967523

Cited by 28 publications

(11 citation statements)

References 68 publications

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“…Perhaps the most promising results, however, come from the sequence modeling domain. Recurrent neural networks show up in earlier scientific literature periodically, such as in predicting a time-series of robot end-effector loads in an assembly task [Scherzinger et al, 2019] and learning latent action plans from large, uncategorized play data sets [Lynch et al, 2020]. But current state-of-the-art performance across a wide variety of sequence prediction tasks -among them imitation learning in a robotics context -is given by combining a large, universal transformer model with embedding schemes specific to various data modalities [Reed et al, 2022].…”

Section: Related Workmentioning

confidence: 99%

A Motion Capture and Imitation Learning-based Approach to Robot Control

Racinskis¹,

Ārents²,

Greitāns³

2022

Preprint

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Imitation Learning is a discipline of Machine Learning primarily concerned with replicating observed behavior of agents known to perform well on a given task, collected in demonstration data sets. In this paper, we set out to introduce a pipeline for collecting demonstrations and training models that can produce motion plans for industrial robots. Object throwing is defined as the motivating use case. Multiple input data modalities are surveyed, and motion capture is selected as the most practicable. Two model architectures operating autoregressively are examined -- feedforward and recurrent neural networks. Trained models execute throws on a real robot successfully, and a battery of quantitative evaluation metrics is proposed, including extrapolated throw accuracy estimates. Recurrent neural networks outperform feedforward ones in most respects, with the best models having an assessed mean throw error on the order of 0.1...0.2 m at distances of 1.5...2.0 m. The data collection, pre-processing, and model training aspects of our proposed approach show promise, but further work is required in developing Cartesian motion planning tools before it is suitable for application in production.

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Section: Related Workmentioning

confidence: 99%

A Motion Capture and Imitation Learning-based Approach to Robot Control

Racinskis¹,

Ārents²,

Greitāns³

2022

Preprint

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show abstract

“…Nevertheless, it is difficult to generalize over multiple tasks, and trajectories are usually not reusable. Recent works in the field of kinesthetic teaching and imitation learning try to generalize demonstrations, e.g., [19,37,59]. Those methods might be important in the future for acquiring robotic skills.…”

Section: Human Demonstrations and Learningmentioning

confidence: 99%

Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution

Nottensteiner

Sachtler

Albu‐Schäffer

2021

J Intell Robot Syst

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Robotic assembly tasks are typically implemented in static settings in which parts are kept at fixed locations by making use of part holders. Very few works deal with the problem of moving parts in industrial assembly applications. However, having autonomous robots that are able to execute assembly tasks in dynamic environments could lead to more flexible facilities with reduced implementation efforts for individual products. In this paper, we present a general approach towards autonomous robotic assembly that combines visual and intrinsic tactile sensing to continuously track parts within a single Bayesian framework. Based on this, it is possible to implement object-centric assembly skills that are guided by the estimated poses of the parts, including cases where occlusions block the vision system. In particular, we investigate the application of this approach for peg-in-hole assembly. A tilt-and-align strategy is implemented using a Cartesian impedance controller, and combined with an adaptive path executor. Experimental results with multiple part combinations are provided and analyzed in detail.

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“…Successful implementations of forward dynamics-based control on industrial robots can be found e.g. in [27] for pure force control and in [9], [28] for compliance control. An application to motion control with a particular focus on sparsely sampled targets is presented in [10].…”

Section: Control Applicationsmentioning

confidence: 99%

Virtual Forward Dynamics Models for Cartesian Robot Control

Scherzinger¹,

Roennau²,

Dillmann³

2020

Preprint

Self Cite

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In industrial context, admittance control represents an important scheme in programming robots for interaction tasks with their environments. Those robots usually implement high-gain disturbance rejection on joint-level and hide direct access to the actuators behind velocity or position controlled interfaces. Using wrist force-torque sensors to add compliance to these systems, force-resolved control laws must map the control signals from Cartesian space to joint motion. Although forward dynamics algorithms would perfectly fit to that task description, their application to Cartesian robot control is not well researched. This paper proposes a general concept of virtual forward dynamics models for Cartesian robot control and investigates how the forward mapping behaves in comparison to well-established alternatives. Through decreasing the virtual system's link masses in comparison to the end effector, the virtual system becomes linear in the operational space dynamics. Experiments focus on stability and manipulability, particularly in singular configurations. Our results show that through this trick, forward dynamics can combine both benefits of the Jacobian inverse and the Jacobian transpose and, in this regard, outperforms the Damped Least Squares method.

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Contact Skill Imitation Learning for Robot-Independent Assembly Programming

Cited by 28 publications

References 68 publications

A Motion Capture and Imitation Learning-based Approach to Robot Control

A Motion Capture and Imitation Learning-based Approach to Robot Control

Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution

Virtual Forward Dynamics Models for Cartesian Robot Control

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