35 citations · 48 across the 4 of their papers we have counts for
9 papers
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym
Rika Antonova, Fabio Ramos, Rafael Possas +1
BayesSim is a statistical technique for domain randomization in reinforcement learning based on likelihood-free inference of simulation parameters. This paper outlines BayesSimIG:…
Sequential Topological Representations for Predictive Models of Deformable Objects
Rika Antonova, Anastasiia Varava, Peiyang Shi +2
Deformable objects present a formidable challenge for robotic manipulation due to the lack of canonical low-dimensional representations and the difficulty of capturing, predicting,…
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
Rika Antonova, Maksim Maydanskiy, Danica Kragic +2
We address the problem of learning reusable state representations from streaming high-dimensional observations. This is important for areas like Reinforcement Learning (RL), which…
Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
Rika Antonova, Akshara Rai, Tianyu Li +1
Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In this work we focus on robotics problems with a budget of only 10-20 trials. This is a ve…
Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
Rika Antonova, Mia Kokic, Johannes A. Stork +1
We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optim…
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots
Akshara Rai, Rika Antonova, Franziska Meier +1
Learning for control can acquire controllers for novel robotic tasks, paving the path for autonomous agents. Such controllers can be expert-designed policies, which typically requi…