no code implementations • 20 Dec 2023 • Sudharshan Suresh, Haozhi Qi, Tingfan Wu, Taosha Fan, Luis Pineda, Mike Lambeta, Jitendra Malik, Mrinal Kalakrishnan, Roberto Calandra, Michael Kaess, Joseph Ortiz, Mustafa Mukadam
Our neural representation driven by multimodal sensing can serve as a perception backbone towards advancing robot dexterity.
1 code implementation • 19 Jul 2022 • Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky T. Q. Chen, Joseph Ortiz, Daniel DeTone, Austin Wang, Stuart Anderson, Jing Dong, Brandon Amos, Mustafa Mukadam
We present Theseus, an efficient application-agnostic open source library for differentiable nonlinear least squares (DNLS) optimization built on PyTorch, providing a common framework for end-to-end structured learning in robotics and vision.
no code implementations • NeurIPS 2021 • Brandon Cui, Hengyuan Hu, Luis Pineda, Jakob N. Foerster
The standard problem setting in cooperative multi-agent settings is self-play (SP), where the goal is to train a team of agents that works well together.
1 code implementation • 30 Mar 2022 • Tim Bakker, Matthew Muckley, Adriana Romero-Soriano, Michal Drozdzal, Luis Pineda
Most current approaches to undersampled multi-coil MRI reconstruction focus on learning the reconstruction model for a fixed, equidistant acquisition trajectory.
2 code implementations • NeurIPS 2021 • Edward J. Smith, David Meger, Luis Pineda, Roberto Calandra, Jitendra Malik, Adriana Romero, Michal Drozdzal
In this paper, we focus on this problem and introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2)a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile signals; and 3) a set of data-driven solutions with either tactile or visuotactile priors to guide the shape exploration.
3 code implementations • 20 Apr 2021 • Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra
MBRL-Lib is designed as a platform for both researchers, to easily develop, debug and compare new algorithms, and non-expert user, to lower the entry-bar of deploying state-of-the-art algorithms.
Model-based Reinforcement Learning reinforcement-learning +1
5 code implementations • 6 Mar 2021 • Hengyuan Hu, Adam Lerer, Brandon Cui, David Wu, Luis Pineda, Noam Brown, Jakob Foerster
Policies learned through self-play may adopt arbitrary conventions and implicitly rely on multi-step reasoning based on fragile assumptions about other agents' actions and thus fail when paired with humans or independently trained agents at test time.
1 code implementation • 26 Feb 2021 • Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra
We demonstrate that this problem can be tackled effectively with automated HPO, which we demonstrate to yield significantly improved performance compared to human experts.
Hyperparameter Optimization Model-based Reinforcement Learning +2
2 code implementations • 20 Jul 2020 • Luis Pineda, Sumana Basu, Adriana Romero, Roberto Calandra, Michal Drozdzal
Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition.
1 code implementation • 11 Jul 2019 • Terrance DeVries, Adriana Romero, Luis Pineda, Graham W. Taylor, Michal Drozdzal
We show that FJD can be used as a promising single metric for cGAN benchmarking and model selection.
no code implementations • 25 Jun 2019 • Amy Zhang, Zachary C. Lipton, Luis Pineda, Kamyar Azizzadenesheli, Anima Anandkumar, Laurent Itti, Joelle Pineau, Tommaso Furlanello
In this paper, we propose an algorithm to approximate causal states, which are the coarsest partition of the joint history of actions and observations in partially-observable Markov decision processes (POMDP).
1 code implementation • 11 Apr 2019 • Luis Pineda, Amaia Salvador, Michal Drozdzal, Adriana Romero
In this paper, we identify an important reproducibility challenge in the image-to-set prediction literature that impedes proper comparisons among published methods, namely, researchers use different evaluation protocols to assess their contributions.
no code implementations • 18 Oct 2018 • Sandhya Saisubramanian, Kyle Hollins Wray, Luis Pineda, Shlomo Zilberstein
The framework extends the stochastic shortest path (SSP) model to dynamic environments in which it is impossible to determine the exact goal states ahead of plan execution.
no code implementations • 21 May 2017 • Luis Pineda, Shlomo Zilberstein
The stochastic shortest path problem (SSP) is a highly expressive model for probabilistic planning.