no code implementations • 29 May 2025 • Jingyun Yang, Isabella Huang, Brandon Vu, Max Bajracharya, Rika Antonova, Jeannette Bohg
In this work, we formulate the policy mobilization problem: find a mobile robot base pose in a novel environment that is in distribution with respect to a manipulation policy trained on a limited set of camera viewpoints.
no code implementations • 28 May 2025 • Carlota Parés-Morlans, Michelle Yi, Claire Chen, Sarah A. Wu, Rika Antonova, Tobias Gerstenberg, Jeannette Bohg
Tasks that involve complex interactions between objects with unknown dynamics make planning before execution difficult.
no code implementations • 1 Jul 2024 • Jingyun Yang, Zi-ang Cao, Congyue Deng, Rika Antonova, Shuran Song, Jeannette Bohg
Building effective imitation learning methods that enable robots to learn from limited data and still generalize across diverse real-world environments is a long-standing problem in robot learning.
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.
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.
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 • 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 • 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.
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 • 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.
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.
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.
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.
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 • 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.
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 • 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.