Fully Self-Supervised Class Awareness in Dense Object Descriptors

We address the problem of inferring self-supervised dense semantic correspondences between objects in multi-object scenes. The method introduces learning of class-aware dense object descriptors by providing either unsupervised discrete labels or confidence in object similarities. We quantitatively and qualitatively show that the introduced method outperforms previous techniques with more robust pixel-to-pixel matches. An example robotic application is also shown - grasping of objects in clutter based on corresponding points

RPBP: Rapid-prototyped remote-brain biped with 3D perception

This paper provides the design of a novel open-hardware mini-bipedal robot, named Rapid-Prototyped Remote-Brain BiPed (RPBP), that is developed to provide a low-cost and reliable platform for locomotion and perception research. The robot is made of customized 3D-printed material (ABS plastic) and electronics, and commercial Robotics Dynamixel MX-28 actuators, as well as visual RGB-D and IMU sensing systems. We show that the robot is able to perform some locomotion/visual-odometry tasks and it is easy to switch between different feet designs, providing also a novel Center-of-Pressure (CoP) sensing system, so that it can deal with various types of terrain. Moreover, we provide a description of its control and perception system architecture, as well as our opensource software packages that provide sensing and navigation tools for locomotion and visual odometry on the robot. Finally, we briefly discuss the transferability of some prototype research that has been done on the developed mini-biped, to half or fullsize humanoid robots, such as COMAN or WALK-MAN

Bayesian Optimization for Optimizing Retrieval Systems

The effectiveness of information retrieval systems heavily depends on a large number of hyperparameters that need to be tuned. Hyperparameters range from the choice of different system components, e.g., stopword lists, stemming methods, or retrieval models, to model parameters, such as k1 and b in BM25, or the number of query expansion terms. Grid and random search, the dominant methods to search for the optimal system configuration, lack a search strategy that can guide them in the hyperparameter space. This makes them inefficient and ineffective. In this paper, we propose to use Bayesian Optimization to jointly search and optimize over the hyperparameter space. Bayesian Optimization, a sequential decision making method, suggests the next most promising configuration to be tested on the basis of the retrieval effectiveness of configurations that have been examined so far. To demonstrate the efficiency and effectiveness of Bayesian Optimization we conduct experiments on TREC collections, and show that Bayesian Optimization outperforms manual tuning, grid search and random search, both in terms of retrieval effectiveness of the configuration found, and in terms of efficiency in finding this configuration