90 citations · 230 across the 12 of their papers we have counts for
15 papers
Safe Payload Transfer with Ship-Mounted Cranes: A Robust Model Predictive Control Approach
Ersin Das, William A. Welch, Patrick Spieler +14
Ensuring safe real-time control of ship-mounted cranes in unstructured transportation environments requires handling multiple safety constraints while maintaining effective payload…
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…
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…
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…
Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data
John So, Amber Xie, Sunggoo Jung +5
Autonomous driving is complex, requiring sophisticated 3D scene understanding, localization, mapping, and control. Rather than explicitly modelling and fusing each of these compone…
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…
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…
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…
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…
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…
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…
Bayesian Learning-Based Adaptive Control for Safety Critical Systems
David D. Fan, Jennifer Nguyen, Rohan Thakker +3
Deep learning has enjoyed much recent success, and applying state-of-the-art model learning methods to controls is an exciting prospect. However, there is a strong reluctance to us…
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…
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…
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,…