4 papers
A Probabilistic Rotation Representation for Symmetric Shapes With an Efficiently Computable Bingham Loss Function
Hiroya Sato, Takuya Ikeda, Koichi Nishiwaki
In recent years, a deep learning framework has been widely used for object pose estimation. While quaternion is a common choice for rotation representation, it cannot represent the…
Probabilistic Rotation Representation With an Efficiently Computable Bingham Loss Function and Its Application to Pose Estimation
Hiroya Sato, Takuya Ikeda, Koichi Nishiwaki
In recent years, a deep learning framework has been widely used for object pose estimation. While quaternion is a common choice for rotation representation of 6D pose, it cannot re…
Sim2Real Instance-Level Style Transfer for 6D Pose Estimation
Takuya Ikeda, Suomi Tanishige, Ayako Amma +3
In recent years, synthetic data has been widely used in the training of 6D pose estimation networks, in part because it automatically provides perfect annotation at low cost. Howev…
Soft-Bubble grippers for robust and perceptive manipulation
Naveen Kuppuswamy, Alex Alspach, Avinash Uttamchandani +3
Manipulation in cluttered environments like homes requires stable grasps, precise placement and robustness against external contact. We present the Soft-Bubble gripper system with…