activity
20182022
most citedPareto Monte Carlo Tree Search for Multi-Objective Informative Planning

50 citations · 54 across the 15 of their papers we have counts for

collaborators

19 papers

cs.RO2022

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…

cs.RO20221 cited

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…

cs.RO2022

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…

cs.RO20221 cited

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…

cs.RO2022

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…

cs.RO202150 cited

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…