Fri, Apr 08, 2022 @ 11:00 AM - 01:00 PM
PhD Candidate: Eric Heiden
Time: April 8, 11am-1pm PT
Location: RTH 406 and on Zoom (https://usc.zoom.us/j/9965174023?pwd=SzlUV1NSUlZQVUNGZTNlT2h4YWpjQT09)
Gaurav Sukhatme (chair), Jernej Barbic, S.K. Gupta, Sven Koenig, Stefanos Nikolaidis
Title: Closing the Reality Gap via Simulation-based Inference and Control
Simulators play a crucial role in robotics - serving as training platforms for reinforcement learning agents, informing hardware design decisions, or facilitating the prototyping of new perception and control pipelines, among many other applications. While their predictive power offers generalizability and accuracy, a core challenge lies in the mismatch between the simulated and the real world. This thesis addresses the reality gap in robotics simulators from three angles.
First, through the lens of robotic control, we investigate a robot learning pipeline that transfers skills acquired in simulation to the real world by composing task embeddings, offering a solution orthogonal to commonly used transfer learning approaches. Further, we develop an adaptive model-predictive controller that leverages a differentiable physics engine as a world representation that is updatable from sensor measurements.
Next, we develop two differentiable simulators that tackle particular problems in robotic perception and manipulation. To improve the accuracy of LiDAR sensing modules, we build a physically-based model that accounts for the measurement process in continuous-wave LiDAR sensors and the interaction of laser light with various surface materials. In robotic cutting, we introduce a differentiable simulator for the slicing of deformable objects, enabling applications in system identification and trajectory optimization.
Finally, we explore techniques that extend the capabilities of simulators to enable their construction and synchronization from real-world sensor measurements. To this end, we present a Bayesian inference algorithm that finds accurate simulation parameter distributions from trajectory-based observations. Next, we introduce a hybrid simulation approach that augments an analytical physics engine by neural networks to enable the learning of dynamical effects unaccounted for in a rigid-body simulator. In closing, we present an inference pipeline that finds the topology of articulated mechanisms from a depth or RGB video while estimating the dynamical parameters, yielding a comprehensive, interactive simulation of the real system.
Audiences: Everyone Is Invited
Contact: Computer Science Department