Bio-inspired Robot Design Considering Load-Bearing and Kinematic Ontogeny of Chelonioidea Sea Turtles

Jansen, M.; Luck, Kevin Sebastian; Campbell, Joseph; Amor, Heni Ben; Aukes, Daniel M.

doi:10.1007/978-3-319-63537-8_19

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…Each agent has six leg segments to be optimized independently for their length. The Hopper (13,4,5) agent has a single leg with four leg segments as well as a nose-like feature and has to learn to move forward as well. All three agents are restricted to movements in a 2D plane.…”

Section: Experimental Settingmentioning

confidence: 99%

“…Hopper (13,4,5) In the planar Hopper task a one-legged agent has to learn jumping motions in order to move forward. The state space of this task has thirteen dimensions and four dimensions in the action space.…”

Section: Simulation Environmentsmentioning

confidence: 99%

“…Without loss of generality, we focus in particular on legged locomotion. The goal of legged robots in such locomotion tasks is to transform as much electric energy as possible into directional movement [2,3,4,5]. To this end, two approaches exist: 1) optimization of the behavioural policy, and 2) optimization of the robot design, which affects the achievable locomotion efficiency [2,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

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Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning

Luck¹,

Amor²,

Calandra³

2019

Preprint

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Humans and animals are capable of quickly learning new behaviours to solve new tasks. Yet, we often forget that they also rely on a highly specialized morphology that co-adapted with motor control throughout thousands of years. Although compelling, the idea of co-adapting morphology and behaviours in robots is often unfeasible because of the long manufacturing times, and the need to redesign an appropriate controller for each morphology. In this paper, we propose a novel approach to automatically and efficiently co-adapt a robot morphology and its controller. Our approach is based on recent advances in deep reinforcement learning, and specifically the soft actor critic algorithm. Key to our approach is the possibility of leveraging previously tested morphologies and behaviors to estimate the performance of new candidate morphologies. As such, we can make full use of the information available for making more informed decisions, with the ultimate goal of achieving a more data-efficient co-adaptation (i.e., reducing the number of morphologies and behaviors tested). Simulated experiments show that our approach requires drastically less design prototypes to find good morphology-behaviour combinations, making this method particularly suitable for future co-adaptation of robot designs in the real world.

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Section: Experimental Settingmentioning

confidence: 99%

Section: Simulation Environmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning

Luck¹,

Amor²,

Calandra³

2019

Preprint

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“…Recent studies have explored various aspects, from the mechanics of sea turtle-inspired robots (21) to exploring the impact of different gaits of locomotion in sea turtle robots (22)(23)(24). Mazouchova (21) found that a sea turtle-inspired robot's terrestrial movement is influenced by flipper penetration and (26) as well as the load-bearing capacity associated with each locomotive gait (27) has also been investigated. More recently, Baines et al (28) took inspiration from chelonian environmental adaptations to develop a morphing amphibious robotic limb that can switch between a streamlined flipper and a load-bearing leg, enhancing performance in both aquatic and terrestrial environments.…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Flipper-Based Robotic Locomotion in Complex Terrains

Aydin,

Chikere,

Mcelroy

et al. 2024

Preprint

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Robots are becoming increasingly essential for traversing complex environments such as disaster areas, extraterrestrial terrains, and marine environ-ments. Yet, their potential is often limited by mobility and adaptability constraints. In nature, various animals have evolved finely tuned designs and anatomical features that enable efficient locomotion in diverse environments.Sea turtles, for instance, possess specialized flippers that facilitate both longdistance underwater travel and adept maneuvers across a range of coastal terrains. Building on the principles of embodied intelligence and drawing inspiration from sea turtle hatchings, this paper examines the critical interplay between a robot's physical form and its environmental interactions, focusing on how morphological traits and locomotive behaviors affect terrestrial navigation. We present a bio-inspired robotic system and study the impacts of flipper/body morphology and gait patterns on its terrestrial mobility across di-verse terrains ranging from sand to rocks. Evaluating key performance metrics such as speed and cost of transport, our experimental results highlight adaptive designs as crucial for multi-terrain robotic mobility to achieve not only speed and efficiency but also the versatility needed to tackle the varied and complex terrains encountered in real-world applications.

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Design of Beaver-like Hind Limb and Analysis of Two Swimming Gaits for Underwater Narrow Space Exploration

Chen

Shi

et al. 2022

J Intell Robot Syst

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Bio-inspired Robot Design Considering Load-Bearing and Kinematic Ontogeny of Chelonioidea Sea Turtles

Cited by 4 publications

References 15 publications

Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning

Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning

Factors Affecting Flipper-Based Robotic Locomotion in Complex Terrains

Design of Beaver-like Hind Limb and Analysis of Two Swimming Gaits for Underwater Narrow Space Exploration

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