Search Results for author: Edward Adelson

Found 9 papers, 4 papers with code

TactoFind: A Tactile Only System for Object Retrieval

no code implementations23 Mar 2023 Sameer Pai, Tao Chen, Megha Tippur, Edward Adelson, Abhishek Gupta, Pulkit Agrawal

We study the problem of object retrieval in scenarios where visual sensing is absent, object shapes are unknown beforehand and objects can move freely, like grabbing objects out of a drawer.

Object Retrieval

Visuotactile Affordances for Cloth Manipulation with Local Control

no code implementations9 Dec 2022 Neha Sunil, Shaoxiong Wang, Yu She, Edward Adelson, Alberto Rodriguez

We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges.

Edge Classification Pose Estimation

Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes

1 code implementation21 Nov 2022 Tao Chen, Megha Tippur, Siyang Wu, Vikash Kumar, Edward Adelson, Pulkit Agrawal

The controller is trained using reinforcement learning in simulation and evaluated in the real world on new object shapes not used for training, including the most challenging scenario of reorienting objects held in the air by a downward-facing hand that must counteract gravity during reorientation.

Object

GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger

no code implementations16 Jun 2021 Shaoxiong Wang, Yu She, Branden Romero, Edward Adelson

Vision-based tactile sensors have the potential to provide important contact geometry to localize the objective with visual occlusion.

3D Reconstruction Pose Tracking

Slip Detection with Combined Tactile and Visual Information

2 code implementations27 Feb 2018 Jianhua Li, Siyuan Dong, Edward Adelson

Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community.

Robotics

ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition

1 code implementation21 Feb 2018 Shan Luo, Wenzhen Yuan, Edward Adelson, Anthony G. Cohn, Raul Fuentes

In this paper, addressing for the first time (to the best of our knowledge) texture recognition from tactile images and vision, we propose a new fusion method named Deep Maximum Covariance Analysis (DMCA) to learn a joint latent space for sharing features through vision and tactile sensing.

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