The trade-off between morphology and control in the co-optimized design of robots

Rosendo, André; Atzigen, Marco von; Iida, Fumiya

doi:10.1371/journal.pone.0186107

Cited by 29 publications

(21 citation statements)

References 27 publications

(35 reference statements)

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“…Similarly, big-data has been utilised for the prediction and physical understanding of complex systems include [6–10]. Other studies used evolutionary algorithms with feedback from environmental interaction to optimise robotic morphologies without any system model [11–13].…”

Section: Introductionmentioning

confidence: 99%

Physics driven behavioural clustering of free-falling paper shapes

et al. 2019

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Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour.

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Section: Introductionmentioning

confidence: 99%

Physics driven behavioural clustering of free-falling paper shapes

et al. 2019

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“…Solutions can range from model-based to learning-based approaches, but from the assumption that robot arms are manufactured by engineering companies, we can easily conclude that prior knowledge of that chosen morphology already exists. Ignorance over such prior, starting from a tabula rasa, will slow down convergence as previously demonstrated with a five-dimensional real-world-based problem taking 72 hours to converge [20].…”

Section: B Model-based Vs Learning-based Approachesmentioning

confidence: 86%

QR Code-Based Self-Calibration for a Fault-Tolerant Industrial Robot Arm

2019

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Malfunctions on industrial robots can cost factories 22 000 dollars per minute. Although the benefits of a fault-tolerant robot arm are clear, redundant sensors would steeply add to the costs of such robots while machine learning-based methods would spend too much time learning the robot's model. We propose a simple but highly effective method to infer which joint underwent failure and at which angle this joint is constrained and to, then, modify the inverse kinematics (IK) algorithm to adaptively achieve the goal. Our method involves combining the robot arm with a QR code and an inexpensive camera, building a virtual link between these three to give the relative position of the end-effector. Once one joint/encoder/motor suffers damage, we use this virtual link to calibrate this joint by coordinate transformation, to calculate the constrained angle, and to recalculate the trajectory through IK iterations with the Newton-Raphson method. We prove the efficacy of our method with pick-and-place experiments, commonly seen in industrial settings, emulating malfunctions on different joints and at different angles, and our method can successfully finish the task in most cases. We further demonstrate that our method is capable, for almost all of the six-degree-offreedom manipulators, to adapt to joint failures after suffering an actuator failure. With the steep increase of robots within factories, this paper presents an elegant approach to keep robots functional until maintenance is scheduled, reducing downtime.

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“…As shown in Fig. 1, physical experimentation is used to obtain the relevant control parameters, an approach which has previously been applied to other control problems [15,14,3]. A base cleaning algorithm is optimized using feedback which is extracted automatically using computer vision.…”

Section: Methodsmentioning

confidence: 99%

Real World Bayesian Optimization Using Robots to Clean Liquid Spills

Voysey

Hughes

Thuruthel

et al. 2020

Towards Autonomous Robotic Systems

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Developing robots that can contribute to cleaning could have a significant impact on the lives of many. Cleaning wet liquid spills is a particularly challenging task for a robotic system, and has several high impact applications. This is a hard task to physically model due to the complex interactions between cleaning materials and the surface. As such, to the authors' knowledge there has been no prior work in this area. A new method for finding optimal control parameters for the cleaning of liquid spills is required by developing a robotic system which iteratively learns to clean through physical experimentation. The robot creates a liquid spill, cleans and assesses performance and uses Bayesian optimization to find the optimal control parameters for a given size of liquid spill. The automation process enabled the experiment to be repeated more than 400 times over 20 hours to find the optimal wiping control parameters for many different conditions. We then show that these solutions can be extrapolated for different spill conditions. The optimized control parameters showed reliable and accurate performances, which in some cases, outperformed humans at the same task.

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The trade-off between morphology and control in the co-optimized design of robots

Cited by 29 publications

References 27 publications

Physics driven behavioural clustering of free-falling paper shapes

Physics driven behavioural clustering of free-falling paper shapes

QR Code-Based Self-Calibration for a Fault-Tolerant Industrial Robot Arm

Real World Bayesian Optimization Using Robots to Clean Liquid Spills

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