Search Results for author: Nawid Jamali

Found 8 papers, 3 papers with code

ViHOPE: Visuotactile In-Hand Object 6D Pose Estimation with Shape Completion

no code implementations11 Sep 2023 Hongyu Li, Snehal Dikhale, Soshi Iba, Nawid Jamali

By explicitly completing the shape of the in-hand object and jointly optimizing the shape completion and pose estimation tasks, we improve the accuracy of the 6D object pose estimate.

6D Pose Estimation Generative Adversarial Network +1

Hierarchical Graph Neural Networks for Proprioceptive 6D Pose Estimation of In-hand Objects

no code implementations28 Jun 2023 Alireza Rezazadeh, Snehal Dikhale, Soshi Iba, Nawid Jamali

We evaluate our model on a diverse subset of objects from the YCB Object and Model Set, and show that our method substantially outperforms existing state-of-the-art work in accuracy and robustness to occlusion.

6D Pose Estimation 6D Pose Estimation using RGB +1

VisuoSpatial Foresight for Physical Sequential Fabric Manipulation

no code implementations19 Feb 2021 Ryan Hoque, Daniel Seita, Ashwin Balakrishna, Aditya Ganapathi, Ajay Kumar Tanwani, Nawid Jamali, Katsu Yamane, Soshi Iba, Ken Goldberg

We build upon the Visual Foresight framework to learn fabric dynamics that can be efficiently reused to accomplish different sequential fabric manipulation tasks with a single goal-conditioned policy.

Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor

1 code implementation23 Sep 2019 Daniel Seita, Aditya Ganapathi, Ryan Hoque, Minho Hwang, Edward Cen, Ajay Kumar Tanwani, Ashwin Balakrishna, Brijen Thananjeyan, Jeffrey Ichnowski, Nawid Jamali, Katsu Yamane, Soshi Iba, John Canny, Ken Goldberg

In 180 physical experiments with the da Vinci Research Kit (dVRK) surgical robot, RGBD policies trained in simulation attain coverage of 83% to 95% depending on difficulty tier, suggesting that effective fabric smoothing policies can be learned from an algorithmic supervisor and that depth sensing is a valuable addition to color alone.

Imitation Learning

Controlled Tactile Exploration and Haptic Object Recognition

no code implementations27 Jun 2017 Massimo Regoli, Nawid Jamali, Giorgio Metta, Lorenzo Natale

The method is composed of a grasp stabilization controller and two exploratory behaviours to capture the shape and the softness of an object.

Object Object Recognition +1

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