50 citations · 105 across the 38 of their papers we have counts for
38 papers
Coordination of Bounded Rational Drones through Informed Prior Policy
Durgakant Pushp, Junhong Xu, Lantao Liu
Biological agents, such as humans and animals, are capable of making decisions out of a very large number of choices in a limited time. They can do so because they use their prior…
GP-guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments
Ihab S. Mohamed, Mahmoud Ali, Lantao Liu
Robotic navigation in unknown, cluttered environments with limited sensing capabilities poses significant challenges in robotics. Local trajectory optimization methods, such as Mod…
GP-Frontier for Local Mapless Navigation
Mahmoud Ali, Lantao Liu
We propose a new frontier concept called the Gaussian Process Frontier (GP-Frontier) that can be used to locally navigate a robot towards a goal without building a map. The GP-Fron…
Pseudo-Trilateral Adversarial Training for Domain Adaptive Traversability Prediction
Zheng Chen, Durgakant Pushp, Jason M. Gregory +1
Traversability prediction is a fundamental perception capability for autonomous navigation. Deep neural networks (DNNs) have been widely used to predict traversability during the l…
Multi-Objective and Model-Predictive Tree Search for Spatiotemporal Informative Planning
Weizhe Chen, Lantao Liu
Adaptive sampling and planning in robotic environmental monitoring are challenging when the target environmental process varies over space and time. The underlying environmental dy…
Long-Term Autonomous Ocean Monitoring with Streaming Samples
Weizhe Chen, Lantao Liu
In the autonomous ocean monitoring task, the sampling robot moves in the environment and accumulates data continuously. The widely adopted spatial modeling method - standard Gaussi…
Towards Efficient MPPI Trajectory Generation with Unscented Guidance: U-MPPI Control Strategy
Ihab S. Mohamed, Junhong Xu, Gaurav S Sukhatme +1
The classical Model Predictive Path Integral (MPPI) control framework, while effective in many applications, lacks reliable safety features due to its reliance on a risk-neutral tr…
Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes
Weizhe Chen, Roni Khardon, Lantao Liu
Robotic Information Gathering (RIG) is a foundational research topic that answers how a robot (team) collects informative data to efficiently build an accurate model of an unknown…
IDA: Informed Domain Adaptive Semantic Segmentation
Zheng Chen, Zhengming Ding, Jason M. Gregory +1
Mixup-based data augmentation has been validated to be a critical stage in the self-training framework for unsupervised domain adaptive semantic segmentation (UDA-SS), which aims t…
Light-Weight Pointcloud Representation with Sparse Gaussian Process
Mahmoud Ali, Lantao Liu
This paper presents a framework to represent high-fidelity pointcloud sensor observations for efficient communication and storage. The proposed approach exploits Sparse Gaussian Pr…
Causal Inference for De-biasing Motion Estimation from Robotic Observational Data
Junhong Xu, Kai Yin, Jason M. Gregory +1
Robot data collected in complex real-world scenarios are often biased due to safety concerns, human preferences, and mission or platform constraints. Consequently, robot learning f…
Decision-Making Among Bounded Rational Agents
Junhong Xu, Durgakant Pushp, Kai Yin +1
When robots share the same workspace with other intelligent agents (e.g., other robots or humans), they must be able to reason about the behaviors of their neighboring agents while…
UAV-miniUGV Hybrid System for Hidden Area Exploration and Manipulation
Durgakant Pushp, Swapnil Kalhapure, Kaushik Das +1
We propose a novel hybrid system (both hardware and software) of an Unmanned Aerial Vehicle (UAV) carrying a miniature Unmanned Ground Vehicle (miniUGV) to perform a complex search…
AK: Attentive Kernel for Information Gathering
Weizhe Chen, Roni Khardon, Lantao Liu
Robotic Information Gathering (RIG) relies on the uncertainty of a probabilistic model to identify critical areas for efficient data collection. Gaussian processes (GPs) with stati…
CALI: Coarse-to-Fine ALIgnments Based Unsupervised Domain Adaptation of Traversability Prediction for Deployable Autonomous Navigation
Zheng Chen, Durgakant Pushp, Lantao Liu
Traversability prediction is a fundamental perception capability for autonomous navigation. The diversity of data in different domains imposes significant gaps to the prediction pe…
Autonomous Navigation of AGVs in Unknown Cluttered Environments: log-MPPI Control Strategy
Ihab S. Mohamed, Kai Yin, Lantao Liu
Sampling-based model predictive control (MPC) optimization methods, such as Model Predictive Path Integral (MPPI), have recently shown promising results in various robotic tasks. H…
Model-Agnostic Multi-Agent Perception Framework
Runsheng Xu, Weizhe Chen, Hao Xiang +2
Existing multi-agent perception systems assume that every agent utilizes the same model with identical parameters and architecture. The performance can be degraded with different p…
Pareto Monte Carlo Tree Search for Multi-Objective Informative Planning
Weizhe Chen, Lantao Liu
In many environmental monitoring scenarios, the sampling robot needs to simultaneously explore the environment and exploit features of interest with limited time. We present an any…
Kernel-based diffusion approximated Markov decision processes for autonomous navigation and control on unstructured terrains
Junhong Xu, Kai Yin, Zheng Chen +3
We propose a diffusion approximation method to the continuous-state Markov Decision Processes (MDPs) that can be utilized to address autonomous navigation and control in unstructur…
Informative Planning in the Presence of Outliers
Weizhe Chen, Lantao Liu
Informative planning seeks a sequence of actions that guide the robot to collect the most informative data to build a large-scale environmental model or learn a dynamical system. E…
Polyline Generative Navigable Space Segmentation for Autonomous Visual Navigation
Zheng Chen, Zhengming Ding, David Crandall +1
Detecting navigable space is a fundamental capability for mobile robots navigating in unknown or unmapped environments. In this work, we treat visual navigable space segmentation a…
NSS-VAEs: Generative Scene Decomposition for Visual Navigable Space Construction
Zheng Chen, Lantao Liu
Detecting navigable space is the first and also a critical step for successful robot navigation. In this work, we treat the visual navigable space segmentation as a scene decomposi…
Efficient Map Prediction via Low-Rank Matrix Completion
Zheng Chen, Shi Bai, Lantao Liu
In many autonomous mapping tasks, the maps cannot be accurately constructed due to various reasons such as sparse, noisy, and partial sensor measurements. We propose a novel map pr…
Multi-Objective Autonomous Exploration on Real-Time Continuous Occupancy Maps
Zheng Chen, Weizhe Chen, Shi Bai +1
Autonomous exploration in unknown environments using mobile robots is the pillar of many robotic applications. Existing exploration frameworks either select the nearest geometric f…