7 papers
ECo-MoE: Embodiment-Conditioned Mixture of Experts Increases the Evolvability of Robots
Yibin Wang, Muhan Li, Zihan Guo +1
In this paper, we introduce a model of evolution and learning in robots that co-optimizes a distribution of latent design vectors (genotypes) and a mixture of control experts (neur…
Computational Design of a Low-Visibility UAV Using a Human-Aligned Perceptual Metric
Jingxian Wang, Chen Yu, David Matthews +3
We introduce Phantom Twist, a type of single-propeller UAV designed to achieve low visibility through high-speed spinning and the exploitation of motion blur. We develop a two-stag…
Creating manufacturable blueprints for coarse-grained virtual robots
Zihan Guo, Muhan Li, Shuzhe Zhang +1
Over the past three decades, countless embodied yet virtual agents have freely evolved inside computer simulations, but vanishingly few were realized as physical robots. This is be…
Robots that redesign themselves through kinematic self-destruction
Chen Yu, Sam Kriegman
Every robot built to date was predesigned by an external process, prior to deployment. Here we show a robot that actively participates in its own design during its lifetime. Starti…
Agile legged locomotion in reconfigurable modular robots
Chen Yu, David Matthews, Jingxian Wang +4
Legged machines are becoming increasingly agile and adaptive but they have so far lacked the morphological diversity of legged animals, which have been rearranged and reshaped to f…
Accelerated co-design of robots through morphological pretraining
Luke Strgar, Sam Kriegman
The co-design of robot morphology and neural control typically requires using reinforcement learning to approximate a unique control policy gradient for each body plan, demanding m…