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Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation

Reducing the reality gap through a Bayesian inference algorithm that leverages massive GPU parallelism and differentiable simulators.

DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting

We introduce a differentiable simulator for robotic cutting. It achieves highly accurate predictions of the knife forces, optimizes cutting actions & more!Best Student Paper at RSS 2021

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 …