7 citations · 24 across the 14 of their papers we have counts for
5 papers · 1 filter
SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks
Zirui Zang, Ahmad Amine, Nick-Marios T. Kokolakis +3
Robots executing iterative tasks in complex, uncertain environments require control strategies that balance robustness, safety, and high performance. This paper introduces a safe i…
MLNav: Learning to Safely Navigate on Martian Terrains
Shreyansh Daftry, Neil Abcouwer, Tyler Del Sesto +7
We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars.…
Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control
Ivan D. Jimenez Rodriguez, Ugo Rosolia, Aaron D. Ames +1
We present a straightforward and efficient way to control unstable robotic systems using an estimated dynamics model. Specifically, we show how to exploit the differentiability of…
Reactive motion planning with probabilistic safety guarantees
Yuxiao Chen, Ugo Rosolia, Chuchu Fan +2
Motion planning in environments with multiple agents is critical to many important autonomous applications such as autonomous vehicles and assistive robots. This paper considers th…
Decentralized Task and Path Planning for Multi-Robot Systems
Yuxiao Chen, Ugo Rosolia, Aaron D. Ames
We consider a multi-robot system with a team of collaborative robots and multiple tasks that emerges over time. We propose a fully decentralized task and path planning (DTPP) frame…