arXiv subjects

Gabriel Gomes

Publications and source records attributed to Gabriel Gomes.

12 recordsLinked to original sources

arXiv2026

Vision-Based Hand Shadowing for Robotic Manipulation via Inverse Kinematics

Teleoperation of low-cost robotic manipulators remains challenging due to the difficulty of retargeting human hand motion to robot joint commands. We present an offline hand-shadowing inverse-kinematics (IK) retargeting pipeline driven by a single egocentric RGB-D camera mounted on 3D-printed glasses. The pipeline detects 21 hand landmarks per hand using MediaPipe Hands, deprojects them into 3D via depth sensing, transforms them into the robot coordinate frame, and solves a damped-least-squares IK problem to produce joint commands for the SO-ARM101 robot (5 arm + 1 gripper joints). A gripper controller maps thumb-index finger geometry to grasp aperture with a multi-level fallback hierarchy. Actions are previewed in a physics simulation before replay on the physical robot. We evaluate the pipeline on a structured pick-and-place benchmark (5-tile grid, 10 grasps per tile, 3 independent runs) achieving an 86.7% +/- 4.2% success rate, and compare it against four vision-language-action (VLA) policies (ACT, SmolVLA, pi_0.5, GR00T N1.5) trained on leader-follower teleoperation data. We provide a quantitative error analysis of the pipeline, reporting a mean IK position error of 36.4 mm, trajectory smoothness metrics showing 57-68% jerk reduction from EMA smoothing, and an ablation study over the smoothing parameter. We also test the pipeline in unstructured real-world environments (grocery store, pharmacy) and find that success is reduced to 9.3% due to hand occlusion by surrounding objects. To mitigate this, we integrate WiLoR as an alternative hand detector, achieving an 8% improvement in hand detection rate over MediaPipe, highlighting both the promise and current limitations of marker-free analytical retargeting.

cs.RO↗

arXiv2025

Industrial Robot Motion Planning with GPUs: Integration of cuRobo for Extended DOF Systems

Efficient motion planning remains a key challenge in industrial robotics, especially for multi-axis systems operating in complex environments. This paper addresses that challenge by integrating GPU-accelerated motion planning through NVIDIA's cuRobo library into Vention's modular automation platform. By leveraging accurate CAD-based digital twins and real-time parallel optimization, our system enables rapid trajectory generation and dynamic collision avoidance for pick-and-place tasks. We demonstrate this capability on robots equipped with additional degrees of freedom, including a 7th-axis gantry, and benchmark performance across various scenarios. The results show significant improvements in planning speed and robustness, highlighting the potential of GPU-based planning pipelines for scalable, adaptable deployment in modern industrial workflows.

cs.RO↗

arXiv2020

Distributed macroscopic traffic simulation with Open Traffic Models

This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. Macroscopic simulations are appropriate for studying regional traffic scenarios when aggregate trends are of interest, rather than individual vehicle traces. They are also appropriate for studying the routing behavior of classes of vehicles, such as app-informed vehicles. The network partitioning was performed with METIS. Inter-process communication was done with MPI (message-passing interface). Results are provided for two networks: one realistic network which was obtained from Open Street Maps for Chattanooga, TN, and another larger synthetic grid network. The software recorded a speed-up ratio of 198 using 256 cores for Chattanooga, and 475 with 1,024 cores for the synthetic network.

cs.DC↗

arXiv2019

Open Traffic Models -- A framework for hybrid simulation of transportation networks

This paper introduces a new approach to hybrid traffic modeling, along with its implementation in software. The software allows modelers to assign traffic models to individual links in a network. Each model implements a series of methods, refered to as the modeling interface. These methods are used by the program to exchange information between adjacent models. Traffic controllers are implemented in a similar manner. The paper outlines the important components of the method: the network description, the description of demands, and the modeling and control interfaces. We include tests demonstrating the propagation of congestion between pairs of macroscpoic, mesoscopic, and microscopic models. Open Traffic Models is an open source implementation of these concepts, and is available at https://github.com/ggomes/otm-sim.

cs.MS↗

arXiv2019

Integrated Offline and Online Optimization-Based Control in a Base-Parallel Architecture

We propose an integrated control architecture to address the gap that currently exists for efficient real-time implementation of MPC-based control approaches for highly nonlinear systems with fast dynamics and a large number of control constraints. The proposed architecture contains two types of controllers: base controllers that are tuned or optimized offline, and parallel controllers that solve an optimization-based control problem online. The control inputs computed by the base controllers provide starting points for the optimization problem of the parallel controllers, which operate in parallel within a limited time budget that does not exceed the control sampling time. The resulting control system is very flexible and its architecture can easily be modified or changed online, e.g., by adding or eliminating controllers, for online improvement of the performance of the controlled system. In a case study, the proposed control architecture is implemented for highway traffic, which is characterized by nonlinear, fast dynamics with multiple control constraints, to minimize the overall travel time of the vehicles, while increasing their total traveled distance within the fixed simulation time window. The results of the simulation show the excellent real-time (i.e., within the given time budget) performance of the proposed control architecture, with the least realized value of the overall cost function. Moreover, among the online control approaches considered for the case study, the average cost per vehicle for the base-parallel control approach is the closest to the online MPC-based controllers, which have excellent performance but may involve computation times that exceed the given time budget.

eess.SY↗

arXiv2019

The Magnetic Grüneisen Parameter for Model Systems

The magneto-caloric effect (MCE), which is the refrigeration based on the variation of the magnetic entropy, is of great interest in both technological applications and fundamental research. The MCE is quantified by the magnetic Grüneisen parameter $Γ_{\textmd{mag}}$. We report on an analysis of $Γ_{\textmd{mag}}$ for the classical Brillouin-like paramagnet, for a modified Brillouin function taking into account a zero-field splitting originated from the spin-orbit (SO) interaction and for the one-dimensional Ising (1DI) model under longitudinal field. For both Brillouin-like model with SO interaction and the longitudinal 1DI model, for $ T \rightarrow$ 0 and vanishing field a sign change of the MCE is observed, suggestive of a quantum phase transition. SO interaction leads to a narrowing of the critical fluctuations upon approaching the critical point. Our findings emphasize the relevance of $Γ_{\textmd{mag}}$ for exploring critical points. Also, we show that the Brillouin model with and without SO interaction can be recovered from the 1DI model in the regime of high-temperatures and vanishing coupling constant $J$.

cond-mat.str-el↗

arXiv2018

Enhanced Grüneisen Parameter in Supercooled Water

We use the recently-proposed \emph{compressible cell} Ising-like model [Phys. Rev. Lett. \textbf{120}, 120603 (2018)] to estimate the ratio between thermal expansivity and specific heat (the Grüneisen parameter $Γ$) in supercooled water. Near the critical pressure and temperature, $Γ$ increases. The $Γ$ value diverges near the pressure-induced finite-$T$ critical end-point [Phys. Rev. Lett. \textbf{104}, 245701 (2010)] and quantum critical points [Phys. Rev. Lett. \textbf{91}, 066404 (2003)], which indicates that two energy scales are governing the system. This enhanced behavior of $Γ$ is caused by the coexistence of high- and low-density liquids [Science \textbf{358}, 1543 (2017)]. Our findings support the proposed liquid-liquid critical point in supercooled water in the No-Man's Land regime, and indicates possible applications of this model to other systems.

cond-mat.stat-mech↗

arXiv2018

Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction

Computational efficiency is an important consideration for deploying machine learning models for time series prediction in an online setting. Machine learning algorithms adjust model parameters automatically based on the data, but often require users to set additional parameters, known as hyperparameters. Hyperparameters can significantly impact prediction accuracy. Traffic measurements, typically collected online by sensors, are serially correlated. Moreover, the data distribution may change gradually. A typical adaptation strategy is periodically re-tuning the model hyperparameters, at the cost of computational burden. In this work, we present an efficient and principled online hyperparameter optimization algorithm for Kernel Ridge regression applied to traffic prediction problems. In tests with real traffic measurement data, our approach requires as little as one-seventh of the computation time of other tuning methods, while achieving better or similar prediction accuracy.

cs.LG↗

arXiv2018

A unified software framework for solving traffic assignment problems

We describe a software framework for solving user equilibrium traffic assignment problems. The design is based on the formulation of the problem as a variational inequality. The software implements these as well as several numerical methods for find equilirbria. We compare the solutions obtained under several models: static, Merchant-Nemhauser, `CTM with instantaneous travel time', and `CTM with actual travel time'. Some important differences are demonstrated.

cs.DC↗

arXiv2017

Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning

This article shows how the recent breakthroughs in Reinforcement Learning (RL) that have enabled robots to learn to play arcade video games, walk or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems. We present the first use of RL for the control of systems modeled by discretized non-linear Partial Differential Equations (PDEs) and devise a novel algorithm to use non-parametric control techniques for large multi-agent systems. We show how neural network based RL enables the control of discretized PDEs whose parameters are unknown, random, and time-varying. We introduce an algorithm of Mutual Weight Regularization (MWR) which alleviates the curse of dimensionality of multi-agent control schemes by sharing experience between agents while giving each agent the opportunity to specialize its action policy so as to tailor it to the local parameters of the part of the system it is located in.

cs.AI↗

arXiv2016

On node models for high-dimensional road networks

Macroscopic traffic models are necessary for simulation and study of traffic's complex macro-scale dynamics, and are often used by practitioners for road network planning, integrated corridor management, and other applications. These models have two parts: a link model, which describes traffic flow behavior on individual roads, and a node model, which describes behavior at road junctions. As the road networks under study become larger and more complex --- nowadays often including arterial networks --- the node model becomes more important. This paper focuses on the first order node model and has two main contributions. First, we formalize the multi-commodity flow distribution at a junction as an optimization problem with all the necessary constraints. Most interesting here is the formalization of input flow priorities. Then, we discuss a very common "conservation of turning fractions" or "first-in-first-out" (FIFO) constraint, and how it often produces unrealistic spillback. This spillback occurs when, at a diverge, a queue develops for a movement that only a few lanes service, but FIFO requires that all lanes experience spillback from this queue. As we show, avoiding this unrealistic spillback while retaining FIFO in the node model requires complicated network topologies. Our second contribution is a "partial FIFO" mechanism that avoids this unrealistic spillback, and a node model and solution algorithm that incorporates this mechanism. The partial FIFO mechanism is parameterized through intervals that describe how individual movements influence each other, can be intuitively described from physical lane geometry and turning movement rules, and allows tuning to describe a link as having anything between full FIFO and no FIFO. Excepting the FIFO constraint, the present node model also fits within the well-established "general class of first-order node models" for multi-commodity flows.

eess.SY↗

arXiv2015

A new model for multi-commodity macroscopic modeling of complex traffic networks

We propose a macroscopic modeling framework for a network of roads and multi-commodity traffic. The proposed framework is based on the Lighthill-Whitham-Richards kinematic wave theory; more precisely, on its discretization, the Cell Transmission Model (CTM), adapted for networks and multi-commodity traffic. The resulting model is called the Link-Node CTM (LNCTM). In the LNCTM, we use the fundamental diagram of an "inverse lambda" shape that allows modeling of the capacity drop and the hysteresis behavior of the traffic state in a link that goes from free flow to congestion and back. A model of the node with multiple input and multiple output links accepting multi-commodity traffic is a cornerstone of the LNCTM. We present the multi-input-multi-output (MIMO) node model for multi-commodity traffic that supersedes previously developed node models. The analysis and comparison with previous node models are provided. Sometimes, certain traffic commodities may choose between multiple output links in a node based on the current traffic state of the node's input and output links. For such situations, we propose a local traffic assignment algorithm that computes how incoming traffic of a certain commodity should be distributed between output links, if this information is not known a priori.

eess.SY↗