EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation

We present the Evolved Grasping Analysis Dataset (EGAD), comprising over 2000 generated objects aimed at training and evaluating robotic visual grasp detection algorithms. The objects in EGAD are geometrically diverse, filling a space ranging from simple to complex shapes and from easy to difficult to grasp, compared to other datasets for robotic grasping, which may be limited in size or contain only a small number of object classes. Additionally, we specify a set of 49 diverse 3D-printable evaluation objects to encourage reproducible testing of robotic grasping systems across a range of complexity and difficulty. The dataset, code and videos can be found at https://dougsm.github.io/egad/Comment: IEEE Robotics and Automation Letters (RA-L). Preprint Version. Accepted April, 2020. The dataset, code and videos can be found at https://dougsm.github.io/egad

Towards the targeted environment-specific evolution of robot components

This research considers the task of evolving the physical structure of a robot to enhance its performance in various environments, which is a significant problem in the field of Evolutionary Robotics. Inspired by the fields of evolutionary art and sculpture, we evolve only targeted parts of a robot, which simplifies the optimisation problem compared to traditional approaches that must simultaneously evolve both (actuated) body and brain. Exploration fidelity is emphasised in areas of the robot most likely to benefit from shape optimisation, whilst exploiting existing robot structure and control. Our approach uses a Genetic Algorithm to optimise collections of Bezier splines that together define the shape of a legged robot's tibia, and leg performance is evaluated in parallel in a high-fidelity simulator. The leg is represented in the simulator as 3D-printable file, and as such can be readily instantiated in reality. Provisional experiments in three distinct environments show the evolution of environment-specific leg structures that are both high-performing and notably different to those evolved in the other environments. This proof-of-concept represents an important step towards the environment-dependent optimisation of performance-critical components for a range of ubiquitous, standard, and already-capable robots that can carry out a wide variety of tasks

Towards the targeted environment-specific evolution of robot components

This research considers the task of evolving the physical structure of a robot to enhance its performance in various environments, which we cast as a significant problem in the field of Evolutionary Robotics. Borrowing from the fields of evolutionary art and sculpture, we evolve only unactuated components of a robot, which simplifies the optimisation problem compared to traditional approaches that must simultaneously evolve both (actuated) body and brain, whilst focusing fidelity onto areas of the robot most likely to benefit from shape optimisation. Our approach uses a Genetic Algorithm to optimise collections of Bezier splines that together create a hexapod leg, with leg performance evaluated in a high-fidelity simulator. The leg is represented in the simulator as a file that can be readily 3D-printed. Experiments are carried out in three different environments; results show different leg shapes are automatically discovered depending on the environment. This proof-of-concept represents an important step towards the environment-dependent optimisation of performance-critical components of standard, capable robots that can carry out a wide variety of tasks