Learning Assembly Tasks in a Few Minutes by Combining Impedance Control and Residual Recurrent Reinforcement Learning

Kulkarni, Padmaja; Kober, Jens; Babuška, Robert; Santina, Cosimo Della

doi:10.1002/aisy.202100095

Cited by 14 publications

(8 citation statements)

References 17 publications

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“…The decision-making of robotic hands can be improved by combining with learning-level perception. 125 However, for the robotic hand, adopting the learning approach usually requires a large number of samples, which is a time-consuming and expensive process. At the same time, compared with human learning efficiency, robot learning efficiency still has great room for improvement.…”

Section: Perception For Dexterous Manipulationmentioning

confidence: 99%

A review on sensory perception for dexterous robotic manipulation

Xia¹,

Deng

Fang

et al. 2022

International Journal of Advanced Robotic Systems

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Sensory perception for dexterous robotic hands is an active research area and recent progress in robotics. Effective dexterous manipulation requires robotic hands to accurately feedback their state or perceive the surrounding environment. This article reviews the state-of-the-art of sensory perception for dexterous robotic manipulation. Two types of sensors, such as intrinsic and extrinsic sensors, are introduced according to their function and layout in robotic hands. These sensors provide rich information to a robotic hand, which contains the posture, the contact information of objects, and the physical information of the environment. Then, a comprehensive analysis of perception methods including planning-level, control-level, and learning-level perceptions is presented. The information obtained from sensory perception can help robotic hands to make decisions effectively. Previously issued reviews mainly focus on the design of tactile senor, while we analyze and discuss the relationship among sensing, perception, and dexterous manipulation. Some potential research topics on sensory perception are also summarized and discussed.

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Section: Perception For Dexterous Manipulationmentioning

confidence: 99%

A review on sensory perception for dexterous robotic manipulation

Xia¹,

Deng

Fang

et al. 2022

International Journal of Advanced Robotic Systems

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“…A much more suitable type of control for this application is force control that adjusts the motion of the robot in response to experienced contact forces. Such methods for robotic assembly are commonly known asadaptive assembly strategies (AAS) ( [2], [3], [4], [5], [6], [7], [8]). A lot of the research in this area has focused on the PiH insertion problem, as a prototypical operation for various assembly tasks.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to F/T sensors, tactile sensors have been employed, too ( [14], [6]). As the instantaneous F/T readings might not be sufficient to disambiguate the contact configuration, the use of recurrent neural nets has been proposed in [15], [7]. However, as is well known, RL often suffers from unfavorable sample complexity, making it less suitable for use on real mechanical systems.…”

Section: Related Workmentioning

confidence: 99%

“…Basically we are requesting for the time interval where the changes for all the statics are less than a predetermined threshold η th . Criterion (7) applied to the linear controller, see signals in Figures 5, 6, output that the controller converged after 25s. Analogously, for the nonlinear controller, see signals in Figures 7, 8, the controller converged after 9s.…”

Section: B Numerical Analysis For Convergencementioning

confidence: 99%

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Design of Adaptive Compliance Controllers for Safe Robotic Assembly

Jha¹,

Romeres²,

Jain³

et al. 2022

Preprint

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Insertion operations are a critical element of most robotic assembly operation, and peg-in-hole (PiH) insertion is one of the most widely studied tasks in the industrial and academic manipulation communities. PiH insertion is in fact an entire class of problems, where the complexity of the problem can depend on the type of misalignment and contact formation during an insertion attempt. In this paper, we present the design and analysis of adaptive compliance controllers which can be used in insertion-type assembly tasks, including learning-based compliance controllers which can be used for insertion problems in the presence of uncertainty in the goal location during robotic assembly. We first present the design of compliance controllers which can ensure safe operation of the robot by limiting experienced contact forces during contact formation. Consequently, we present analysis of the force signature obtained during the contact formation to learn the corrective action needed to perform insertion. Finally, we use the proposed compliance controllers and learned models to design a policy that can successfully perform insertion in novel test conditions with almost perfect success rate. We validate the proposed approach on a physical robotic test-bed using a 6-DoF manipulator arm.

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“…Several robot-led applications have been well developed including welding, machining, and assembly. [1][2][3] 3D robot measurement systems, which integrate 3D cameras, have gained popularity over coordinate measuring machines (CMMs) in workpiece measurement [4,5] and vision-guiding robot processing [6] because of its high efficiency and cost-effectiveness. [7] The working process of a 3D robot measurement system is illustrated in Figure 1.…”

mentioning

confidence: 99%

Simple and High‐Precision Hand–Eye Calibration for 3D Robot Measurement Systems

Wu,

Zang,

Bai

et al. 2023

Advanced Intelligent Systems

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Three dimensional (3D) robot measurement systems, with binocular planar structured light cameras (3D cameras) installed at the terminal flange of robots, are widely used to measure complete workpieces by stitching point clouds obtained from various sampled poses. To ensure accurate stitching, hand–eye calibration is necessary. However, 3D cameras often demonstrate low precision in measuring jumping edges and vertices, resulting in challenges when selecting appropriate calibrators and calibration methods for hand–eye calibration. We propose a simple and high‐precision hand–eye calibration method for 3D robot measurement systems. Initially, a single sphere is utilized as the calibrator, and its poses are observed using specific robotic motions. Subsequently, the hand–eye calibration problem is formulated as a well‐known equation, , relying solely on the high‐precision relative pose of the robot. Finally, a novel simultaneous solution for and the corresponding closed‐form initial solution are introduced. Simulations and experiments confirm that the proposed method exhibits high noise resistance and achieves high‐precision calibration, even with a limited calibration data quantity. Compared with traditional methods, the fitting error of the proposed method can be reduced from over 0.9 mm to less than 0.6 mm.

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Learning Assembly Tasks in a Few Minutes by Combining Impedance Control and Residual Recurrent Reinforcement Learning

Cited by 14 publications

References 17 publications

A review on sensory perception for dexterous robotic manipulation

A review on sensory perception for dexterous robotic manipulation

Design of Adaptive Compliance Controllers for Safe Robotic Assembly

Simple and High‐Precision Hand–Eye Calibration for 3D Robot Measurement Systems

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