activity
20122024
most citedParallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization

69 citations · 462 across the 66 of their papers we have counts for

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Showing 2020 · cs.ROShow all

8 papers · 2 filters

cs.RO2020★ 1 cited

Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion

Amanda Bouman, Muhammad Fadhil Ginting, Nikhilesh Alatur +8

This paper serves as one of the first efforts to enable large-scale and long-duration autonomy using the Boston Dynamics Spot robot. Motivated by exploring extreme environments, pa…

cs.RO2020

ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes

Kejun Li, Maegan Tucker, Erdem Bıyık +6

Characterizing what types of exoskeleton gaits are comfortable for users, and understanding the science of walking more generally, require recovering a user's utility landscape. Le…

cs.RO2020★ 1 cited

Motivations and Preliminary Design for Mid-Air Deployment of a Science Rotorcraft on Mars

Jeff Delaune, Jacob Izraelevitz, Larry A. Young +18

Mid-Air Deployment (MAD) of a rotorcraft during Entry, Descent and Landing (EDL) on Mars eliminates the need to carry a propulsion or airbag landing system. This reduces the total…

cs.RO2020★ 19 cited

A Unified NMPC Scheme for MAVs Navigation with 3D Collision Avoidance under Position Uncertainty

Sina Sharif Mansouri, Christoforos Kanellakis, Bjorn Lindqvist +4

This article proposes a novel Nonlinear Model Predictive Control (NMPC) framework for Micro Aerial Vehicle (MAV) autonomous navigation in constrained environments. The introduced f…

cs.RO2020

Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud

Sina Sharif Mansouri, Farhad Pourkamali-Anaraki, Miguel Castano Arranz +3

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The imple…

cs.RO2020★ 18 cited

Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties

Richard Cheng, Mohammad Javad Khojasteh, Aaron D. Ames +1

Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions (CBF…