Author name cluster

Patricio Vela

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2026 Conference Paper

Schema-Guided Scene-Graph Reasoning Based on Multi-Agent Large Language Model System

Yiye Chen
Harpreet S. Sawhney
Nicholas Gydé
Yanan Jian
Jack Saunders
Patricio Vela
Benjamin E Lundell

Scene graphs have emerged as a structured and serializable environment representation for grounded spatial reasoning with Large Language Models (LLMs). In this work, we propose SG2, an iterative Schema-Guided Scene-Graph reasoning framework based on multi-agent LLMs. The agents are grouped into two modules: a (1) Reasoner module for abstract task planning and graph information queries generation, and a (2) Retriever module for extracting corresponding graph information based on code-writing following the queries. Two modules collaborate iteratively, enabling sequential reasoning and adaptive attention to graph information. The scene graph schema, prompted to both modules, serves to not only streamline both reasoning and retrieval process, but also guide the cooperation between two modules. This eliminates the need to prompt LLMs with full graph data, reducing the chance of hallucination due to irrelevant information. Through experiments in multiple simulation environments, we show that our framework surpasses existing LLM-based approaches and baseline single-agent, tool-based Reason-while-Retrieve strategy in numerical Q&A and planning tasks.

PDF Details DOI

RLDM Conference 2013 Conference Abstract

Bayesian Nonparametric Adaptive Control using Gaussian Processes

Girish Chowdhary
Hassan Kingravi
Jonathan How
Patricio Vela

The problem of making control decisions over time for acheiving a desired behavior goal for a dynamical systems has been widely studied in control systems literature. The paradigm of Model Reference Adaptive control is concerned with guaranteeing stability of the dynamical system being controlled and en- suring that it behaves like a designer chosen reference model in presence of uncertainty. Most current model reference adaptive control methods rely on parametric adaptive elements, in which the number of parame- ters of the adaptive element are fixed a-priori, often through expert judgment. Examples of such adaptive elements are the commonly used Radial Basis Function (RBF) Neural Networks (NNs) with pre- allocated centers allocated based on the expected operating domain. If the system operates outside of the expected operating domain, such adaptive elements can become non-effective, thus rendering the adaptive controller only semi-global in nature. This paper investigates Gaussian Process based adaptive elements which gen- eralize the notion of Gaussian distributions to function approximation. We show that these nonparametric adaptive elements guarantee good closed loop performance with minimal prior domain knowledge of the un- certainty through stochastic stability arguments. Online implementable GP inference method are evaluated in simulations and compared with RBF-NN adaptive controllers with pre-allocated centers.

PDF Details