no code implementations • 15 Oct 2016 • Rika Antonova, Akshara Rai, Christopher G. Atkeson
We develop a distance metric for bipedal locomotion that enhances the sample-efficiency of Bayesian Optimization and use it to train a 16 dimensional neuromuscular model for planar walking.
1 code implementation • 1 Mar 2017 • Rika Antonova, Silvia Cruciani, Christian Smith, Danica Kragic
In this work we propose an approach to learn a robust policy for solving the pivoting task.
no code implementations • 8 Jun 2017 • Rika Antonova, Silvia Cruciani
We design a simulator for solving a pivoting task (of interest in Robotics) and demonstrate that even a simple simulator designed with RL in mind outperforms high-fidelity simulators when it comes to learning a policy that is to be deployed on a real robotic system.
no code implementations • 27 Jul 2017 • Rika Antonova, Akshara Rai, Christopher G. Atkeson
First, we demonstrate improvement in sample efficiency when optimizing a 5-dimensional controller on the ATRIAS robot hardware.
no code implementations • 10 Oct 2018 • Rika Antonova, Mia Kokic, Johannes A. Stork, Danica Kragic
Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores.
1 code implementation • 10 Jul 2019 • Rika Antonova, Akshara Rai, Tianyu Li, Danica Kragic
We propose a model and architecture for a sequential variational autoencoder that embeds the space of simulated trajectories into a lower-dimensional space of latent paths in an unsupervised way.
2 code implementations • 15 Jun 2020 • Rika Antonova, Maksim Maydanskiy, Danica Kragic, Sam Devlin, Katja Hofmann
Our second contribution is a unifying mathematical formulation for learning latent relations.
no code implementations • 23 Nov 2020 • Rika Antonova, Anastasiia Varava, Peiyang Shi, J. Frederico Carvalho, Danica Kragic
Deformable objects present a formidable challenge for robotic manipulation due to the lack of canonical low-dimensional representations and the difficulty of capturing, predicting, and controlling such objects.
1 code implementation • 9 Jul 2021 • Rika Antonova, Fabio Ramos, Rafael Possas, Dieter Fox
This paper outlines BayesSimIG: a library that provides an implementation of BayesSim integrated with the recently released NVIDIA IsaacGym.
no code implementations • 9 Dec 2021 • Rika Antonova, Jingyun Yang, Priya Sundaresan, Dieter Fox, Fabio Ramos, Jeannette Bohg
Deformable object manipulation remains a challenging task in robotics research.
no code implementations • 7 Apr 2022 • Priya Sundaresan, Rika Antonova, Jeannette Bohg
However, for highly deformable objects it is challenging to align the output of a simulator with the behavior of real objects.
no code implementations • 28 Jun 2022 • Rika Antonova, Jingyun Yang, Krishna Murthy Jatavallabhula, Jeannette Bohg
In this work, we study the challenges that differentiable simulation presents when it is not feasible to expect that a single descent reaches a global optimum, which is often a problem in contact-rich scenarios.
no code implementations • 4 Nov 2022 • Mengxi Li, Rika Antonova, Dorsa Sadigh, Jeannette Bohg
We demonstrate the effectiveness of our method for designing new tools in several scenarios, such as winding ropes, flipping a box and pushing peas onto a scoop in simulation.
1 code implementation • 9 May 2023 • Jimmy Wu, Rika Antonova, Adam Kan, Marion Lepert, Andy Zeng, Shuran Song, Jeannette Bohg, Szymon Rusinkiewicz, Thomas Funkhouser
For a robot to personalize physical assistance effectively, it must learn user preferences that can be generally reapplied to future scenarios.