Euan Judd

Euan Judd

Senior Machine Learning Engineer at Xela Robotics

Posts by Tags

biomechanics

Why did bipedal locomotion emerge amongst terrestrial vertebrates?

15 minute read

Published:

Introduction

Bipedality has evolved independently in different lineages among terrestrial vertebrates (Snyder R. C., 1962). In extant mammals, it has evolved independently in primates, kangaroos, and rodents (Wu, et al., 2014) and it has also evolved once in dinosaurs (Sereno P. F., 1993). This essay will therefore be split into three parts. The first will deal with human obligate bipedalism with reference to facultative bipedality in other primates. The second will deal with obligate bipedality in dinosaurs, the origin of which is assumed to be the same for Aves, and will highlight an interesting theory that uses lizard facultative bipedalism as an analogy for early bipedal evolution. Finally, obligate saltatory gait in marsupials and rodents, which had different origins but may have evolved for the same purpose, will be discussed.

bipedal locomotion

Why did bipedal locomotion emerge amongst terrestrial vertebrates?

15 minute read

Published:

Introduction

Bipedality has evolved independently in different lineages among terrestrial vertebrates (Snyder R. C., 1962). In extant mammals, it has evolved independently in primates, kangaroos, and rodents (Wu, et al., 2014) and it has also evolved once in dinosaurs (Sereno P. F., 1993). This essay will therefore be split into three parts. The first will deal with human obligate bipedalism with reference to facultative bipedality in other primates. The second will deal with obligate bipedality in dinosaurs, the origin of which is assumed to be the same for Aves, and will highlight an interesting theory that uses lizard facultative bipedalism as an analogy for early bipedal evolution. Finally, obligate saltatory gait in marsupials and rodents, which had different origins but may have evolved for the same purpose, will be discussed.

co-evolution of morphology and control

Learning to Control Soft Robots

13 minute read

Published:

Introduction

Classically controlled robots have revolutionised assembly lines where the environment is restrictedand predictable. However, this control scheme has proven less effective in uncertain, unstructured environments due to the unmodelled nonlinearities in the morphology and its interaction with the environment [Atkeson et al., 2015]. Embodied intelligence suggests that we are not controlled centrally but rather that the morphology and environment also contribute to behaviour. Therefore, the passive dynamics of the robot could be exploited to simplify the controller by making the passive dynamics closer to the desired behaviour [Pfeifer and Bongard, 2006]. Consequently, there has been a growing interest in soft robots which have many passive degrees of freedom and are often underactuated. However, classical control necessitates exact kinematic and dynamic models which are hard to derive analytically for soft robots due to the nonlinear dynamics of the body, external forces, and uncertain, unstructured environment. Model-free approaches are a potential solution to these problems as they can approximate inverse kinematic and dynamic models that account for the unknown nonlinearities.

essay

Learning to Control Soft Robots

13 minute read

Published:

Introduction

Classically controlled robots have revolutionised assembly lines where the environment is restrictedand predictable. However, this control scheme has proven less effective in uncertain, unstructured environments due to the unmodelled nonlinearities in the morphology and its interaction with the environment [Atkeson et al., 2015]. Embodied intelligence suggests that we are not controlled centrally but rather that the morphology and environment also contribute to behaviour. Therefore, the passive dynamics of the robot could be exploited to simplify the controller by making the passive dynamics closer to the desired behaviour [Pfeifer and Bongard, 2006]. Consequently, there has been a growing interest in soft robots which have many passive degrees of freedom and are often underactuated. However, classical control necessitates exact kinematic and dynamic models which are hard to derive analytically for soft robots due to the nonlinear dynamics of the body, external forces, and uncertain, unstructured environment. Model-free approaches are a potential solution to these problems as they can approximate inverse kinematic and dynamic models that account for the unknown nonlinearities.

Why did bipedal locomotion emerge amongst terrestrial vertebrates?

15 minute read

Published:

Introduction

Bipedality has evolved independently in different lineages among terrestrial vertebrates (Snyder R. C., 1962). In extant mammals, it has evolved independently in primates, kangaroos, and rodents (Wu, et al., 2014) and it has also evolved once in dinosaurs (Sereno P. F., 1993). This essay will therefore be split into three parts. The first will deal with human obligate bipedalism with reference to facultative bipedality in other primates. The second will deal with obligate bipedality in dinosaurs, the origin of which is assumed to be the same for Aves, and will highlight an interesting theory that uses lizard facultative bipedalism as an analogy for early bipedal evolution. Finally, obligate saltatory gait in marsupials and rodents, which had different origins but may have evolved for the same purpose, will be discussed.

machine learning

Learning to Control Soft Robots

13 minute read

Published:

Introduction

Classically controlled robots have revolutionised assembly lines where the environment is restrictedand predictable. However, this control scheme has proven less effective in uncertain, unstructured environments due to the unmodelled nonlinearities in the morphology and its interaction with the environment [Atkeson et al., 2015]. Embodied intelligence suggests that we are not controlled centrally but rather that the morphology and environment also contribute to behaviour. Therefore, the passive dynamics of the robot could be exploited to simplify the controller by making the passive dynamics closer to the desired behaviour [Pfeifer and Bongard, 2006]. Consequently, there has been a growing interest in soft robots which have many passive degrees of freedom and are often underactuated. However, classical control necessitates exact kinematic and dynamic models which are hard to derive analytically for soft robots due to the nonlinear dynamics of the body, external forces, and uncertain, unstructured environment. Model-free approaches are a potential solution to these problems as they can approximate inverse kinematic and dynamic models that account for the unknown nonlinearities.