Publications

Experimental Comparison of Global Motion Planning Algorithms for Wheeled Mobile Robots

Planning smooth and energy-efficient motions for wheeled mobile robots is a central task for applications ranging from autonomous …

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

We present a strategy for simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than …

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. …

Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics

A key ingredient to achieving intelligent behavior is physical understanding that equips robots with the ability to reason about the …

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 …

Real2Sim Transfer using Differentiable Physics

Accurate simulations allow modern machine learning techniques to be applied to robotics problems, with sample-collection runtimes …

Interactive Differentiable Simulation

Intelligent agents need a physical understanding of the world to predict the impact of their actions in the future. While …

Confidence-rich Grid Mapping

Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments. In this work, we …

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 …

Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing

Simulation-to-real transfer is an important strategy for making reinforcement learning practical with real robots. Successful …

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 …

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 …

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 …

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 …

Confidence-rich Grid Mapping

Occupancy grids are a common framework in robotics for creating a spatial map of the environment. Traditional grid mapping algorithms …

Web Text-based Network Industry Classifications: Preliminary Results

Studies of market structure and product market competition are important in many disciplines, such as economics, finance, accounting …

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 …

High-speed Safe Trajectory Planning in Confidence-rich Maps

Confidence-aware Occupancy Grids