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20172025
most citedBayesian Learning-Based Adaptive Control for Safety Critical Systems

90 citations · 230 across the 12 of their papers we have counts for

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12 papers · 1 filter

cs.RO2025

Risk-Guided Diffusion: Toward Deploying Robot Foundation Models in Space, Where Failure Is Not An Option

Rohan Thakker, Adarsh Patnaik, Vince Kurtz +10

Safe, reliable navigation in extreme, unfamiliar terrain is required for future robotic space exploration missions. Recent generative-AI methods learn semantically aware navigation…

cs.RO2025

Risk-aware Integrated Task and Motion Planning for Versatile Snake Robots under Localization Failures

Ashkan Jasour, Guglielmo Daddi, Masafumi Endo +7

Snake robots enable mobility through extreme terrains and confined environments in terrestrial and space applications. However, robust perception and localization for snake robots…

cs.RO2024

Enabling Novel Mission Operations and Interactions with ROSA: The Robot Operating System Agent

Rob Royce, Marcel Kaufmann, Jonathan Becktor +6

The advancement of robotic systems has revolutionized numerous industries, yet their operation often demands specialized technical knowledge, limiting accessibility for non-expert…

cs.RO2021

ReQuBiS -- Reconfigurable Quadrupedal-Bipedal Snake Robots

Harshad Zade, Aadesh Varude, Karan Pandya +3

The selection of mobility modes for robot navigation consists of various trade-offs. Snake robots are ideal for traversing through constrained environments such as pipes, cluttered…

cs.RO2021★ 20 cited

NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

Ali Agha, Kyohei Otsu, Benjamin Morrell +69

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARP…

cs.RO2021

Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions

Rohan Thakker, Nikhilesh Alatur, David D. Fan +5

We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and…

cs.RO2020★ 63 cited

Autonomous Hybrid Ground/Aerial Mobility in Unknown Environments

David D. Fan, Rohan Thakker, Tara Bartlett +4

Hybrid ground and aerial vehicles can possess distinct advantages over ground-only or flight-only designs in terms of energy savings and increased mobility. In this work we outline…

cs.RO2020

Towards Resilient Autonomous Navigation of Drones

Angel Santamaria-Navarro, Rohan Thakker, David D. Fan +2

Robots and particularly drones are especially useful in exploring extreme environments that pose hazards to humans. To ensure safe operations in these situations, usually perceptua…

cs.RO2020★ 44 cited

Where to Map? Iterative Rover-Copter Path Planning for Mars Exploration

Takahiro Sasaki, Kyohei Otsu, Rohan Thakker +2

In addition to conventional ground rovers, the Mars 2020 mission will send a helicopter to Mars. The copter's high-resolution data helps the rover to identify small hazards such as…

cs.RO2019

Contact Inertial Odometry: Collisions are your Friends

Thomas Lew, Tomoki Emmei, David D. Fan +4

Autonomous exploration of unknown environments with aerial vehicles remains a challenge, especially in perceptually degraded conditions. Dust, fog, or a lack of visual or LiDAR-bas…

cs.RO2019★ 11 cited

Bi-directional Value Learning for Risk-aware Planning Under Uncertainty: Extended Version

Sung-Kyun Kim, Rohan Thakker, Ali-akbar Agha-mohammadi

Decision-making under uncertainty is a crucial ability for autonomous systems. In its most general form, this problem can be formulated as a Partially Observable Markov Decision Pr…

cs.RO2017★ 2 cited

Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo

Daqing Yi, Rohan Thakker, Cole Gulino +2

Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found,…