34,360 research outputs found
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Domain Randomization and Generative Models for Robotic Grasping
Deep learning-based robotic grasping has made significant progress thanks to
algorithmic improvements and increased data availability. However,
state-of-the-art models are often trained on as few as hundreds or thousands of
unique object instances, and as a result generalization can be a challenge.
In this work, we explore a novel data generation pipeline for training a deep
neural network to perform grasp planning that applies the idea of domain
randomization to object synthesis. We generate millions of unique, unrealistic
procedurally generated objects, and train a deep neural network to perform
grasp planning on these objects.
Since the distribution of successful grasps for a given object can be highly
multimodal, we propose an autoregressive grasp planning model that maps sensor
inputs of a scene to a probability distribution over possible grasps. This
model allows us to sample grasps efficiently at test time (or avoid sampling
entirely).
We evaluate our model architecture and data generation pipeline in simulation
and the real world. We find we can achieve a 90% success rate on previously
unseen realistic objects at test time in simulation despite having only been
trained on random objects. We also demonstrate an 80% success rate on
real-world grasp attempts despite having only been trained on random simulated
objects.Comment: 8 pages, 11 figures. Submitted to 2018 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2018
Outcomes of a virtual-reality simulator-training programme on basic surgical skills in robot-assisted laparoscopic surgery
Background The utility of the virtual-reality robotic simulator in training
programmes has not been clearly evaluated. Our aim was to evaluate the
impact of a virtual-reality robotic simulator-training programme on basic
surgical skills.
Methods A simulator-training programme in robotic surgery, using the da
Vinci Skills Simulator, was evaluated in a population including junior and seasoned
surgeons, and non-physicians. Their performances on robotic dots and
suturing-skin pod platforms before and after virtual-simulation training were
rated anonymously by surgeons experienced in robotics.
Results 39 participants were enrolled: 14 medical students and residents in
surgery, 14 seasoned surgeons, 11 non-physicians. Junior and seasoned
surgeons’ performances on platforms were not significantly improved after
virtual-reality robotic simulation in any of the skill domains, in contrast to
non-physicians.
Conclusions The benefits of virtual-reality simulator training on several
tasks to basic skills in robotic surgery were not obvious among surgeons
in our initial and early experience with the simulator
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