Learning to manipulate a whip with simple primitive actions – A simulation study

Nah, Moses C.; Krotov, Aleksei; Russo, Marta; Sternad, Dagmar; Hogan, Neville

doi:10.1016/j.isci.2023.107395

Cited by 2 publications

(1 citation statement)

References 69 publications

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“…In robotics, manipulating flexible objects represents a significant challenge due to computational and dynamic complexities introduced by their high flexibility. While most research has concentrated on slower, general manipulations, like folding laundry [6], setting tablecloths [7], and performing surgical operations [8,9], few studies have addressed fast, goal-directed control with flexible objects [10][11][12]. Furthermore, these studies have largely overlooked the potential of reinforcement learning and optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Learning-based Control Framework for Fast and Accurate Manipulation of a Flexible Object

Wang,

Xiong,

Tolu

et al. 2024

J Bionic Eng

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This paper presents a learning-based control framework for fast (< 1.5 s) and accurate manipulation of a flexible object, i.e., whip targeting. The framework consists of a motion planner learned or optimized by an algorithm, Online Impedance Adaptation Control (OIAC), a sim2real mechanism, and a visual feedback component. The experimental results show that a soft actor-critic algorithm outperforms three Deep Reinforcement Learning (DRL), a nonlinear optimization, and a genetic algorithm in learning generalization of motion planning. It can greatly reduce average learning trials (to < 20$$\%$$ % of others) and maximize average rewards (to > 3 times of others). Besides, motion tracking errors are greatly reduced to 13.29$$\%$$ % and 22.36$$\%$$ % of constant impedance control by the OIAC of the proposed framework. In addition, the trajectory similarity between simulated and physical whips is 89.09$$\%$$ % . The presented framework provides a new method integrating data-driven and physics-based algorithms for controlling fast and accurate arm manipulation of a flexible object.

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Section: Introductionmentioning

confidence: 99%

A Learning-based Control Framework for Fast and Accurate Manipulation of a Flexible Object

Wang,

Xiong,

Tolu

et al. 2024

J Bionic Eng

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Robot control based on motor primitives: A comparison of two approaches

Nah,

Lachner,

Hogan

2024

The International Journal of Robotics Research

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Motor primitives are fundamental building blocks of a controller which enable dynamic robot behavior with minimal high-level intervention. By treating motor primitives as basic “modules,” different modules can be sequenced or superimposed to generate a rich repertoire of motor behavior. In robotics, two distinct approaches have been proposed: Dynamic Movement Primitives (DMPs) and Elementary Dynamic Actions (EDAs). While both approaches instantiate similar ideas, significant differences also exist. This paper attempts to clarify the distinction and provide a unifying view by delineating the similarities and differences between DMPs and EDAs. We provide nine robot control examples, including sequencing or superimposing movements, managing kinematic redundancy and singularity, control of both position and orientation of the robot’s end-effector, obstacle avoidance, and managing physical interaction. We show that the two approaches clearly diverge in their implementation. We also provide a real-robot demonstration to show how DMPs and EDAs can be combined to get the best of both approaches. With this detailed comparison, we enable researchers to make informed decisions to select the most suitable approach for specific robot tasks and applications.

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Learning to manipulate a whip with simple primitive actions – A simulation study

Cited by 2 publications

References 69 publications

A Learning-based Control Framework for Fast and Accurate Manipulation of a Flexible Object

A Learning-based Control Framework for Fast and Accurate Manipulation of a Flexible Object

Robot control based on motor primitives: A comparison of two approaches

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