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Max Bajracharya

Researcher at California Institute of Technology

Publications -  46
Citations -  1444

Max Bajracharya is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Stereo cameras & Robot. The author has an hindex of 19, co-authored 45 publications receiving 1315 citations. Previous affiliations of Max Bajracharya include Toyota & Jet Propulsion Laboratory.

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Journal ArticleDOI

Autonomy for Mars Rovers: Past, Present, and Future

TL;DR: The newest member of the Mars rover family will have the ability to autonomously approach and inspect a target and automatically detect interesting scientific events.
Proceedings ArticleDOI

CLARAty: an architecture for reusable robotic software

TL;DR: An overview of the Coupled Layered Architecture for Robotic Autonomy (CLARAty) is presented, which develops a framework for generic and reusable robotic components that can be adapted to a number of heterogeneous robot platforms.
Journal ArticleDOI

Mobile Manipulation and Mobility as Manipulation-Design and Algorithms of RoboSimian

TL;DR: The hardware design and software algorithms of RoboSimian are presented, a statically stable quadrupedal robot capable of both dexterous manipulation and versatile mobility in difficult terrain, demonstrating its ability to perform disaster recovery tasks in degraded human environments.
Proceedings ArticleDOI

CLARAty and challenges of developing interoperable robotic software

TL;DR: An overview of the Coupled Layered Architecture for Robotic Autonomy (CLARAty) is presented, which develops a framework for generic and reusable robotic components that can be adapted to a number of heterogeneous robot platforms.
Journal ArticleDOI

A Fast Stereo-based System for Detecting and Tracking Pedestrians from a Moving Vehicle

TL;DR: A fully integrated system for detecting, localizing, and tracking pedestrians from a moving vehicle that can reliably detect upright pedestrians to a range of 40 m in lightly cluttered urban environments and on a diverse set of datasets with groundtruth in outdoor environments with varying degrees of pedestrian density and clutter.