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Bundletrack: 6d pose tracking for novel objects without instance or category-level 3d models
Tracking the 6D pose of objects in video sequences is important for robot manipulation. Most
prior efforts, however, often assume that the target object's CAD model, at least at a category …
prior efforts, however, often assume that the target object's CAD model, at least at a category …
Catgrasp: Learning category-level task-relevant gras** in clutter from simulation
Task-relevant gras** is critical for industrial assembly, where downstream manipulation
tasks constrain the set of valid grasps. Learning how to perform this task, however, is …
tasks constrain the set of valid grasps. Learning how to perform this task, however, is …
You only demonstrate once: Category-level manipulation from single visual demonstration
Promising results have been achieved recently in category-level manipulation that
generalizes across object instances. Nevertheless, it often requires expensive real-world …
generalizes across object instances. Nevertheless, it often requires expensive real-world …
se (3)-tracknet: Data-driven 6d pose tracking by calibrating image residuals in synthetic domains
Tracking the 6D pose of objects in video sequences is important for robot manipulation. This
task, however, introduces multiple challenges:(i) robot manipulation involves significant …
task, however, introduces multiple challenges:(i) robot manipulation involves significant …
Robust, occlusion-aware pose estimation for objects grasped by adaptive hands
Many manipulation tasks, such as placement or within-hand manipulation, require the
object's pose relative to a robot hand. The task is difficult when the hand significantly …
object's pose relative to a robot hand. The task is difficult when the hand significantly …
Task-driven perception and manipulation for constrained placement of unknown objects
Recent progress in robotic manipulation has dealt with the case of previously unknown
objects in the context of relatively simple tasks, such as bin-picking. Existing methods for …
objects in the context of relatively simple tasks, such as bin-picking. Existing methods for …
Dual projection generative adversarial networks for conditional image generation
L Han, MR Min, A Stathopoulos… - Proceedings of the …, 2021 - openaccess.thecvf.com
Abstract Conditional Generative Adversarial Networks (cGANs) extend the standard
unconditional GAN framework to learning joint data-label distributions from samples, and …
unconditional GAN framework to learning joint data-label distributions from samples, and …
Interleaving monte carlo tree search and self-supervised learning for object retrieval in clutter
B Huang, T Guo, A Boularias… - … Conference on Robotics …, 2022 - ieeexplore.ieee.org
In this study, working with the task of object retrieval in clutter, we have developed a robot
learning framework in which Monte Carlo Tree Search (MCTS) is first applied to enable a …
learning framework in which Monte Carlo Tree Search (MCTS) is first applied to enable a …
Learning sensorimotor primitives of sequential manipulation tasks from visual demonstrations
This work aims to learn how to perform complex robot manipulation tasks that are composed
of several, consecutively executed low-level sub-tasks, given as input a few visual …
of several, consecutively executed low-level sub-tasks, given as input a few visual …
Mono-star: Mono-camera scene-level tracking and reconstruction
We present Mono-STAR, the first real-time 3D reconstruction system that simultaneously
supports semantic fusion, fast motion tracking, non-rigid object deformation, and topological …
supports semantic fusion, fast motion tracking, non-rigid object deformation, and topological …