1 citations · 1 across the 12 of their papers we have counts for
13 papers
LiTe-GS: Oracle-Efficient Next Best View Selection for 3D Gaussian Splatting
Vivek Pandey, Amirhossein Mollaei Khass, Nader Motee
Selecting informative camera views is critical for efficient training and adaptive refinement in 3D Gaussian Splatting, where each observation significantly influences model parame…
Multi-Agent Next-Best-View Optimization for Risk-Averse Planning
Amirhossein Mollaei Khass, Vivek Pandey, Guangyi Liu +3
Multi-agent Next-Best-View (NBV) selection for safe path planning in uncertain and unknown environments requires informative, safety-aware, and efficient coordination. Centralized…
Conflict-Aware Active Perception and Control in 3D Gaussian Splatting Fields via Control Barrier Functions
Amirhossein Mollaei Khass, Athanasios Cosse, Vivek Pandey +1
Active perception in uncertain environments requires robots to navigate safely while acquiring informative observations to reduce map uncertainty. These objectives inherently confl…
Entropic Value-at-Risk for Inter-Vehicle Collision in Platoons: Network- and Delay-Induced Bounds on Risk Due to Extreme Events
Vivek Pandey, Nader Motee
Safe operation of connected vehicle platoons under stochastic disturbances and time-delayed dynamics requires accurate quantification of rare but dangerous events, such as inter-ve…
Incremental Risk Assessment for Cascading Failures in Large-Scale Multi-Agent Systems
Guangyi Liu, Vivek Pandey, Christoforos Somarakis +1
We develop a framework for studying and quantifying the risk of cascading failures in time-delay consensus networks, motivated by a team of agents attempting temporal rendezvous un…
Distributionally Robust Cascading Risk in Multi-Agent Rendezvous: Extended Analysis of Parameter-Induced Ambiguity
Vivek Pandey, Nader Motee
Ensuring safety in autonomous multi-agent systems during time-critical tasks such as rendezvous is a fundamental challenge, particularly under communication delays and uncertainty…
Efficient Greedy Algorithms for Feature Selection in Robot Visual Localization
Vivek Pandey, Amirhossein Mollaei, Nader Motee
Robot localization is a fundamental component of autonomous navigation in unknown environments. Among various sensing modalities, visual input from cameras plays a central role, en…
Active Next-Best-View Optimization for Risk-Averse Path Planning
Amirhossein Mollaei Khass, Guangyi Liu, Vivek Pandey +4
Safe navigation in uncertain environments requires planning methods that integrate risk aversion with active perception. In this work, we present a unified framework that refines a…
Distributionally Robust Cascading Risk Quantification in Multi-Agent Rendezvous: Effects of Time Delay and Network Connectivity
Vivek Pandey, Nader Motee
Achieving safety in autonomous multi-agent systems, particularly in time-critical tasks like rendezvous, is a critical challenge. In this paper, we propose a distributionally robus…
Scalable Networked Feature Selection with Randomized Algorithm for Robot Navigation
Vivek Pandey, Arash Amini, Guangyi Liu +4
We address the problem of sparse selection of visual features for localizing a team of robots navigating an unknown environment, where robots can exchange relative position measure…
Beyond Uncertainty: Risk-Aware Active View Acquisition for Safe Robot Navigation and 3D Scene Understanding with FisherRF
Guangyi Liu, Wen Jiang, Boshu Lei +3
The active view acquisition problem has been extensively studied in the context of robot navigation using NeRF and 3D Gaussian Splatting. To enhance scene reconstruction efficiency…
Data-Driven Distributionally Robust Mitigation of Risk of Cascading Failures
Guangyi Liu, Arash Amini, Vivek Pandey +1
We introduce a novel data-driven method to mitigate the risk of cascading failures in delayed discrete-time Linear Time-Invariant (LTI) systems. Our approach involves formulating a…
Quantification of Distributionally Robust Risk of Cascade of Failures in Platoon of Vehicles
Vivek Pandey, Guangyi Liu, Arash Amini +1
Achieving safety is a critical aspect of attaining autonomy in a platoon of autonomous vehicles. In this paper, we propose a distributionally robust risk framework to investigate c…