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Found 6 papers, 2 papers with code
SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects
Acquiring accurate depth information of transparent objects using off-the-shelf RGB-D cameras is a well-known challenge in Computer Vision and Robotics.
Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection
To address this problem, we propose a method that trains purely on synthetic data and successfully transfers to the real world using sim2real methods by creating datasets of filled food trays using high-quality 3d models of real pieces of food for the training instance segmentation models.
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrinking segment of the AI community access to their development.
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators
Developing personal robots that can perform a diverse range of manipulation tasks in unstructured environments necessitates solving several challenges for robotic grasping systems.
Hindsight policy gradients
A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy.
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms.
Computation
