Zobrazeno 1 - 10
of 608
pro vyhledávání: '"Pappas, P. J."'
The recent introduction of large language models (LLMs) has revolutionized the field of robotics by enabling contextual reasoning and intuitive human-robot interaction in domains as varied as manipulation, locomotion, and self-driving vehicles. When
Externí odkaz:
http://arxiv.org/abs/2410.13691
A driving force behind the diverse applicability of modern machine learning is the ability to extract meaningful features across many sources. However, many practical domains involve data that are non-identically distributed across sources, and stati
Externí odkaz:
http://arxiv.org/abs/2410.11227
Flying quadrotors in tight formations is a challenging problem. It is known that in the near-field airflow of a quadrotor, the aerodynamic effects induced by the propellers are complex and difficult to characterize. Although machine learning tools ca
Externí odkaz:
http://arxiv.org/abs/2410.09727
As robots become increasingly capable, users will want to describe high-level missions and have robots fill in the gaps. In many realistic settings, pre-built maps are difficult to obtain, so execution requires exploration and mapping that are necess
Externí odkaz:
http://arxiv.org/abs/2410.03035
How many parameters are required for a model to execute a given task? It has been argued that large language models, pre-trained via self-supervised learning, exhibit emergent capabilities such as multi-step reasoning as their number of parameters re
Externí odkaz:
http://arxiv.org/abs/2409.13421
In this survey, we design formal verification and control algorithms for autonomous systems with practical safety guarantees using conformal prediction (CP), a statistical tool for uncertainty quantification. We focus on learning-enabled autonomous s
Externí odkaz:
http://arxiv.org/abs/2409.00536
Recent research endeavours have theoretically shown the beneficial effect of cooperation in multi-agent reinforcement learning (MARL). In a setting involving $N$ agents, this beneficial effect usually comes in the form of an $N$-fold linear convergen
Externí odkaz:
http://arxiv.org/abs/2407.20441
In safety-critical robot planning or control, manually specifying safety constraints or learning them from demonstrations can be challenging. In this paper, we propose a certifiable alignment method for a robot to learn a safety constraint in its mod
Externí odkaz:
http://arxiv.org/abs/2407.04216
Autor:
Wang, Sifan, Seidman, Jacob H, Sankaran, Shyam, Wang, Hanwen, Pappas, George J., Perdikaris, Paris
Operator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various physical domains. Here we introduce the Continuous Vision Transformer
Externí odkaz:
http://arxiv.org/abs/2405.13998
In this paper, we focus on the problem of shrinking-horizon Model Predictive Control (MPC) in uncertain dynamic environments. We consider controlling a deterministic autonomous system that interacts with uncontrollable stochastic agents during its mi
Externí odkaz:
http://arxiv.org/abs/2405.10875