This work presents an overview of the techni-cal details behind a high-performance reinforcement learning policy deployment with the Spot RL Researcher Development Kit for low-level motor access on Boston Dynamic's Spot. This represents the first public demonstration of an end-to-end reinforcement learning policy deployed on Spot hardware with training code publicly available through Nvidia IsaacLab and deployment code available through Boston Dynamics. We utilize Wasserstein Distance and Maximum Mean Discrepancy to quantify the distributional dissimilarity of data collected on hardware and in simulation to measure our sim-to-real gap. We use these measures as a scoring function for the Covariance Matrix Adaptation Evolution Strategy to optimize simulated parameters that are unknown or difficult to measure from Spot. Our procedure for modeling and training produces high-quality reinforcement learning policies capable of multiple gaits, including a flight phase. We deploy policies capable of over 5.2m/s locomotion, more than triple Spot's default controller maximum speed, robustness to slippery surfaces, disturbance rejection, and overall agility previously unseen on Spot. We detail our method and release our code to support future work on Spot with the low-level API.

The rising need for security in the last years has led to an increased use of surveillance cameras in both public and private areas. The increasing amount of footage makes it necessary to assist human operators with automated systems to monitor and analyze the video data in reasonable time. In this paper we summarize our work of the past three years in the field of intelligent and automated surveillance. Our proposed system extends the common active monitoring of camera footage into an intelligent automated investigative person-search and walk path reconstruction of a selected person within hours of image data. Our system is evaluated and tested under life-like conditions in real-world surveillance scenarios. Our experiments show that with our system an operator can reconstruct a case in a fraction of time, compared to manually searching the recorded data.

Fast vision-based object and person tracking is important for various applications in mobile robotics and Human-Robot Interaction. While current state-of-the-art methods use descriptive features for visual tracking, we propose a novel approach using a sparse template based feature set, which is drawn from homogeneous regions on the object to be tracked. Using only a small number of simple features, without complex descriptors in combination with logarithmic-search, the tracker performs at hyper-real-time on HD-images without the use of parallelized hardware. Detailed benchmark experiments show that it outperforms most other state-of-the-art approaches for real-time object and person tracking in quality and runtime. In the experiments we also show the robustness of the tracker and evaluate the effects of different initialization methods, feature sets, and parameters on the tracker. Although we focus on the scenario of person and object tracking in robot applications, the proposed tracker can be used for a variety of other tracking tasks.

CARTRACK is a computer vision system that can reliably detect, track, and measure vehicle rears in images from a video camera in a following car. The system exploits the symmetry property typical for the rear of most vehicles on normal roads. The authors present two novel methods for detecting mirror symmetry in images, one based directly on the intensity values and another one based on a discrete representation of local orientation. CARTRACK has been used for realtime experiments with test vehicles of Volkswagen and Daimler-Benz.>