Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Serlin, Zachary"'
Autor:
Knoedler, Luzia, So, Oswin, Yin, Ji, Black, Mitchell, Serlin, Zachary, Tsiotras, Panagiotis, Alonso-Mora, Javier, Fan, Chuchu
Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a
Externí odkaz:
http://arxiv.org/abs/2410.11157
This work develops a zero-shot mechanism for an agent to satisfy a Linear Temporal Logic (LTL) specification given existing task primitives. Oftentimes, autonomous robots need to satisfy spatial and temporal goals that are unknown until run time. Pri
Externí odkaz:
http://arxiv.org/abs/2408.04215
Autor:
Beason, Jordan, Novitzky, Michael, Kliem, John, Errico, Tyler, Serlin, Zachary, Becker, Kevin, Paine, Tyler, Benjamin, Michael, Dasgupta, Prithviraj, Crowley, Peter, O'Donnell, Charles, James, John
The objective of this work is to evaluate multi-agent artificial intelligence methods when deployed on teams of unmanned surface vehicles (USV) in an adversarial environment. Autonomous agents were evaluated in real-world scenarios using the Aquaticu
Externí odkaz:
http://arxiv.org/abs/2404.17038
Autor:
So, Oswin, Serlin, Zachary, Mann, Makai, Gonzales, Jake, Rutledge, Kwesi, Roy, Nicholas, Fan, Chuchu
Control barrier functions (CBF) have become popular as a safety filter to guarantee the safety of nonlinear dynamical systems for arbitrary inputs. However, it is difficult to construct functions that satisfy the CBF constraints for high relative deg
Externí odkaz:
http://arxiv.org/abs/2310.15478
Compositionality is a critical aspect of scalable system design. Reinforcement learning (RL) has recently shown substantial success in task learning, but has only recently begun to truly leverage composition. In this paper, we focus on Boolean compos
Externí odkaz:
http://arxiv.org/abs/2306.17033
In this paper, we propose a learning-based framework to simultaneously learn the communication and distributed control policies for a heterogeneous multi-agent system (MAS) under complex mission requirements from Capability Temporal Logic plus (CaTL+
Externí odkaz:
http://arxiv.org/abs/2212.11792
We consider the problem of controlling a heterogeneous multi-agent system required to satisfy temporal logic requirements. Capability Temporal Logic (CaTL) was recently proposed to formalize such specifications for deploying a team of autonomous agen
Externí odkaz:
http://arxiv.org/abs/2210.01732
This paper explores continuous-time control synthesis for target-driven navigation to satisfy complex high-level tasks expressed as linear temporal logic (LTL). We propose a model-free framework using deep reinforcement learning (DRL) where the under
Externí odkaz:
http://arxiv.org/abs/2210.01162
Autor:
Serlin, Zachary
As autonomous systems develop an ever expanding range of capabilities, monolithic systems (systems with multiple capabilities on a single platform) become increasingly expensive to build and vulnerable to failure. A promising alternative to these mon
Externí odkaz:
https://hdl.handle.net/2144/41485
In this work we consider the multi-image object matching problem, extend a centralized solution of the problem to a distributed solution, and present an experimental application of the centralized solution. Multi-image feature matching is a keystone
Externí odkaz:
http://arxiv.org/abs/1910.13317