162 citations · 287 across the 9 of their papers we have counts for
12 papers · 1 filter
EvoRobogami: Co-designing with Humans in Evolutionary Robotics Experiments
Huang Zonghao, Quinn Wu, David Howard +1
We study the effects of injecting human-generated designs into the initial population of an evolutionary robotics experiment, where subsequent population of robots are optimised vi…
Follow the Gradient: Crossing the Reality Gap using Differentiable Physics (RealityGrad)
Jack Collins, Ross Brown, Jürgen Leitner +1
We propose a novel iterative approach for crossing the reality gap that utilises live robot rollouts and differentiable physics. Our method, RealityGrad, demonstrates for the first…
Semi-supervised Gated Recurrent Neural Networks for Robotic Terrain Classification
Ahmadreza Ahmadi, Tønnes Nygaard, Navinda Kottege +2
Legged robots are popular candidates for missions in challenging terrains due to the wide variety of locomotion strategies they can employ. Terrain classification is a key enabling…
Path Towards Multilevel Evolution of Robots
Shelvin Chand, David Howard
Multi-level evolution is a bottom-up robotic design paradigm which decomposes the design problem into layered sub-tasks that involve concurrent search for appropriate materials, co…
Real World Morphological Evolution is Feasible
Tonnes F. Nygaard, David Howard, Kyrre Glette
Evolutionary algorithms offer great promise for the automatic design of robot bodies, tailoring them to specific environments or tasks. Most research is done on simplified models o…
Traversing the Reality Gap via Simulator Tuning
Jack Collins, Ross Brown, Jurgen Leitner +1
The large demand for simulated data has made the reality gap a problem on the forefront of robotics. We propose a method to traverse the gap by tuning available simulation paramete…