We present a pipeline to learn simulators from depth or RGB video. The "URDF" of a mechanism is reconstructed, and the simulation parameters are inferred through Bayesian inference.
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
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 …
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 …
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 …
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. …
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 …
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 …
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 …