research
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PatchGraph: In-hand tactile tracking with learned surface normals
Paloma Sodhi, Michael Kaess, Mustafa Mukadam, Stuart Anderson IEEE Intl. Conf. on Robotics and Automation (ICRA), 2022 paper / video / code Exploit local decompositions to track unseen objects during in-hand manipulations using vision-based tactile sensors. Leverage image translation GAN models for sim-to-real transfer. |
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Learning Energy-based models in Factor Graph Optimization
Paloma Sodhi, Eric Dexheimer, Mustafa Mukadam, Stuart Anderson, Michael Kaess Conference on Robot Learning (CoRL), 2021 paper / video / code / project page Learning observation models in factor graphs can be viewed as shaping cost functions in energy-based learning. This enables us to learn models end-to-end efficiently even with non-differentiable optimizers in the loop. |
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Ground Encoding: Learned Factor Graph-based Models for Localizing Ground Penetrating Radar
Alex Baikovitz, Paloma Sodhi, Michael Dille, Michael Kaess IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2021 paper / video / code Off-the-shelf ground penetrating radars (GPR) to localize in unknown environments. Learn GPR factors in a sparse graph optimizer. Best Conference Paper Finalist (0.4% of accepted papers) |
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Learning Tactile Models for Factor Graph-based Estimation
Paloma Sodhi, Michael Kaess, Mustafa Mukadam, and Stuart Anderson IEEE Intl. Conf. on Robotics and Automation (ICRA), 2021 paper / video / code / project page Learn vision-based tactile observation models to be integrated within a sparse graph optimizer. Track objects during planar pushing. Contributed talk at WiML workshop (4% of accepted posters) |
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ICS: Incremental Constrained Smoothing for State Estimation
Paloma Sodhi, Sanjiban Choudhury, Joshua Mangelson, and Michael Kaess IEEE Intl. Conf. on Robotics and Automation (ICRA), 2020 paper / video Leverage primal-dual methods such as Augmented Lagrangian to solve for a constrained optimization objective online. |
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Online and Consistent Occupancy Grid Mapping for Planning in Unknown Environments
Paloma Sodhi, Bing-Jui Ho, and Michael Kaess IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2019 paper / video Online occupancy map that maintains free space for planning while adapting efficiently to dynamically changing poses from SLAM. |
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Virtual Occupancy Grid Map for Submap-based Pose Graph SLAM and Planning in 3D Environments
Bing-Jui Ho, Paloma Sodhi, Pedro V. Teixeira, Ming Hsiao, Tushar Kusnur, and Michael Kaess IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2018 paper Planning using deformable local submaps that are efficient to update using a sparse graph-based SLAM optimizer. |
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Robust Plant Phenotyping via Model-Based Optimization
Paloma Sodhi, Hanqi Sun, Barnabas Poczos, and David Wettergreen IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2018 paper / code Phenotyping can be formulated as an optimization in the space of plant models. Apply cross-entropy to obtain most likely samples. |
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In-field segmentation and identification of plant structures using 3D imaging
Paloma Sodhi, Srinivasan Vijayarangan, and David Wettergreen IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2017 paper / video Learn a mapping from multi-view in-field plant images to reconstructed 3D plant units segmented into different classes. Best Application Paper Finalist (0.25% of accepted papers) |
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Vision-based localization and control for distributed mapping
Paloma Sodhi, Ashish Budhiraja, Achal Arvind, and Debasish Ghose video / code Modular multi-robot testbed implementing evolutionary optimization for distributed boundary coverage of a light source. |
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Autonomous leader-follower quadrotor control using GPS feedback
Paloma Sodhi, Ashish Budhiraja, and Debasish Ghose video / code Design, system ID and control for autonomous leader-follower quadrator control using GPS feedback. |