Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware

Learning instead of designing robot controllers can greatly reduce engineering effort required, while also emphasizing robustness. Despite considerable progress in simulation, applying learning directly in hardware is still challenging, in part due to the necessity to explore potentially unstable parameters. We explore the concept of shaping the reward landscape with training wheels: temporary modifications of the physical hardware that facilitate learning. We demonstrate the concept with a robot leg mounted on a boom learning to hop fast. This proof of concept embodies typical challenges such as instability and contact, while being simple enough to empirically map out and visualize the reward landscape. Based on our results we propose three criteria for designing effective training wheels for learning in robotics. A video synopsis can be found at https://youtu.be/6iH5E3LrYh8.Comment: Accepted to the IEEE International Conference on Robotics and Automation (ICRA) 2018, 6 pages, 6 figure

Placement, visibility and coverage analysis of dynamic pan/tilt/zoom camera sensor networks

Multi-camera vision systems have important application in a number of fields, including robotics and security. One interesting problem related to multi-camera vision systems is to determine the effect of camera placement on the quality of service provided by a network of Pan/Tilt/Zoom (PTZ) cameras with respect to a specific image processing application. The goal of this work is to investigate how to place a team of PTZ cameras, potentially used for collaborative tasks, such as surveillance, and analyze the dynamic coverage that can be provided by them. Computational Geometry approaches to various formulations of sensor placement problems have been shown to offer very elegant solutions; however, they often involve unrealistic assumptions about real-world sensors, such as infinite sensing range and infinite rotational speed. Other solutions to camera placement have attempted to account for the constraints of real-world computer vision applications, but offer solutions that are approximations over a discrete problem space. A contribution of this work is an algorithm for camera placement that leverages Computational Geometry principles over a continuous problem space utilizing a model for dynamic camera coverage that is simple, yet representative. This offers a balance between accounting for real-world application constraints and creating a problem that is tractable

Intrinsically Legal-For-Trade Objects by Digital Signatures

The established techniques for legal-for-trade registration of weight values meet the legal requirements, but in praxis they show serious disadvantages. We report on the first implementation of intrinsically legal-for-trade objects, namely weight values signed by the scale, that is accepted by the approval authority. The strict requirements from both the approval- and the verification-authority as well as the limitations due to the hardware of the scale were a special challenge. The presented solution fulfills all legal requirements and eliminates the existing practical disadvantages.Comment: 4 pages, 0 figure

Properties of the phi meson at high temperatures and densities

We calculate the spectral density of the phi meson in a hot bath of nucleons and pions using a general formalism relating self-energy to the forward scattering amplitude (FSA). In order to describe the low energy FSA, we use experimental data along with a background term. For the high energy FSA, a Regge parameterization is employed. We verify the resulting FSA using dispersion techniques. We find that the position of the peak of the spectral density is slightly shifted from its vacuum position and that its width is considerably increased. The width of the spectral density at a temperature of 150 MeV and at normal nuclear density is more than 90 MeV.Comment: 4 pages, 5 figures, Poster presented at Quark Matter 200