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NeuralSim: Augmenting Differentiable Simulators with Neural Networks

Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the use of efficient, gradient-based optimization algorithms to find the simulation parameters that best fit the observed sensor readings. Nonetheless, these …

LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments

Simultaneous Localization and Mapping (SLAM) in large-scale, unknown, and complex subterranean environments is a challenging problem. Sensors must operate in off-nominal conditions; uneven and slippery terrains make wheel odometry inaccurate, while …

Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking

Light Detection and Ranging (LIDAR) sensors play an important role in the perception stack of autonomous robots, supplying mapping and localization pipelines with depth measurements of the environment. While their accuracy outperforms other types of …

Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robotic Manipulation Skills

Personal robots assisting humans must perform complex manipulation tasks that are typically difficult to specify in traditional motion planning pipelines, where multiple objectives must be met and the high-level context be taken into consideration. …

Heterogeneous Sensor Fusion via Confidence-rich 3D Grid Mapping: Application to Physical Robots

Autonomous navigation of intelligent physical systems largely depend on the ability of the system to generate an accurate map of its environment. Confidence-rich grid mapping algorithm provides a novel representation of the map based on range data by …

Scaling simulation-to-real transfer by learning composable robot skills

We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simulation-reality gap, we learn a set of diverse policies that are …

Gradient-Informed Path Smoothing for Wheeled Mobile Robots

Planning smooth trajectories is important for the safe, efficient and comfortable operation of mobile robots, such as wheeled robots moving in crowded environments or cars moving at high speed. Asymptotically optimal sampling-based motion planners …

Confidence-rich Grid Mapping

Occupancy grids are a common framework in robotics for creating a spatial map of the environment. Traditional grid mapping algorithms assume that map voxel occupancies are independent of each other. In addition, they use a map representation where …

Planning High-speed Safe Trajectories in Confidence-rich Maps

Planning safe, high-speed trajectories in unknown environments remains a major roadblock on the way toward achieving fast autonomous flight. Current state-of-the-art planning approaches use sampling-based methods or trajectory optimization to obtain …

Concept and Realization of a Diagnostic System for Smart Environments

Automatically diagnosing a complex system containing heterogeneous hard- and software components is a challenging task. To analyze the problem, we first describe different scenarios a diagnostic engine might be confronted with. Based on those …