Proprioceptive distribution matching adapts simulators for legged robot policies by comparing observation and action distributions, reducing sim-to-real gaps with minimal real data and no external sensing.
cmaes : A simple yet practical python library for cma-es
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Controller gains affect learnability differently for behavior cloning, RL from scratch, and sim-to-real transfer, so optimal gains depend on the learning paradigm rather than desired task behavior.
KSOS-BO improves acquisition function optimization in Bayesian optimization by casting it as a kernel sum of squares semidefinite program, outperforming Sobol, DE, and CMA-ES baselines on 10/15 benchmarks with 81% average regret reduction.
An E(3)-equivariant graph neural network trained on MAGNDATA experimental structures predicts magnetic orders using a new primitive modulated structure representation that handles commensurate and incommensurate cases uniformly.
O3 uses surrogate latent spaces extracted from generative models to perform sample-efficient black-box optimization over their outputs, outperforming direct sampling and original-latent optimization on image and protein tasks.
Variational optimization of shallow probes and decoders for structured phase estimation in small qubit arrays approaches entanglement-enhanced precision bounds for both uniform and weighted encodings.
A flexible optimization framework is introduced to suppress in-plane g-tensor components in SiGe-Ge-SiGe quantum wells by tuning silicon concentration, enabling gapless single-spin qubit encoding.
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Tune to Learn: How Controller Gains Shape Robot Policy Learning
Controller gains affect learnability differently for behavior cloning, RL from scratch, and sim-to-real transfer, so optimal gains depend on the learning paradigm rather than desired task behavior.