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Montse Gonzalez Arenas

Montse Gonzalez Arenas

Montserrat currently is a Senior Research Engineer working at Google Brain, her research interest involve natural language processing techniques such as speech recognition. She also has developed projects about data analysis and statistical modeling. She is currently working at Google Brain Robotics.
Authored Publications
Google Publications
Other Publications
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    Robotic table wiping via whole-body trajectory optimizationand reinforcement learning
    Benjie Holson
    Fei Xia
    Jeffrey Bingham
    Jonathan Weisz
    Mario Prats
    Peng Xu
    Sumeet Singh
    Thomas Lew
    Xiaohan Zhang
    Yao Lu
    ICRA (2022)
    Preview abstract We propose an end-to-end framework to enablemultipurpose assistive mobile robots to autonomously wipetables and clean spills and crumbs. This problem is chal-lenging, as it requires planning wiping actions with uncertainlatent crumbs and spill dynamics over high-dimensional visualobservations, while simultaneously guaranteeing constraintssatisfaction to enable deployment in unstructured environments.To tackle this problem, we first propose a stochastic differentialequation (SDE) to model crumbs and spill dynamics and ab-sorption with the robot wiper. Then, we formulate a stochasticoptimal control for planning wiping actions over visual obser-vations, which we solve using reinforcement learning (RL). Wethen propose a whole-body trajectory optimization formulationto compute joint trajectories to execute wiping actions whileguaranteeing constraints satisfaction. We extensively validateour table wiping approach in simulation and on hardware. View details
    Preview abstract We describe a large vocabulary speech recognition system that is accurate, has low latency, and yet has a small enough memory and computational footprint to run faster than real-time on a Nexus 5 Android smartphone. We employ a quantized Long Short-Term Memory (LSTM) acoustic model trained with connectionist temporal classification (CTC) to directly predict phoneme targets, and further reduce its memory footprint using an SVD-based compression scheme. Additionally, we minimize our memory footprint by using a single language model for both dictation and voice command domains, constructed using Bayesian interpolation. Finally, in order to properly handle device-specific information, such as proper names and other context-dependent information, we inject vocabulary items into the decoder graph and bias the language model on-the-fly. Our system achieves 13.5% word error rate on an open-ended dictation task, running with a median speed that is seven times faster than real-time. View details
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