Benchmark

We propose a low-cost compact easily replicable hardware stack for manipulation tasks, that can be assembled within a few hours. We also provide implementations of robot learning algorithms for grasping (supervised learning) and reaching (reinforcement learning). Contributions invited!

We propose a low-cost compact easily replicable hardware stack for manipulation tasks, that can be assembled within a few hours. We also provide implementations of robot learning algorithms for grasping (supervised learning) and reaching (reinforcement learning). Contributions invited!

Image quality assessment datasets have heretofore focused on specific individual distortions rather than a more natural mix of different degrees of various kinds of distortions. We synthesize a dataset to study this latter setting, and compare various existing algorithms.