Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …

Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …

Successful real-world deployment of legged robots would require them to adapt in real-time
to unseen scenarios like changing terrains, changing payloads, wear and tear. This paper …

This paper presents the results and insights from the black-box optimization (BBO)
challenge at NeurIPS2020 which ran from July–October, 2020. The challenge emphasized …

There have been significant advances in neural networks for both 3D object detection using
LiDAR and 2D object detection using video. However, it has been surprisingly difficult to …

Bayesian optimization provides sample-efficient global optimization for a broad range of
applications, including automatic machine learning, engineering, physics, and experimental …

Bayesian optimization has recently emerged as a popular method for the sample-efficient
optimization of expensive black-box functions. However, the application to high-dimensional …

Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of
complex controllers that can map sensory inputs directly to low-level actions. In the domain …

Designing agile locomotion for quadruped robots often requires extensive expertise and
tedious manual tuning. In this paper, we present a system to automate this process by …

Deep learning is a field in artificial intelligence that works well in computer vision, natural
language processing and audio recognition. Deep neural network architectures has number …