90 citations · 261 across the 18 of their papers we have counts for
21 papers
Semantic Belief Behavior Graph: Enabling Autonomous Robot Inspection in Unknown Environments
Muhammad Fadhil Ginting, David D. Fan, Sung-Kyun Kim +2
This paper addresses the problem of autonomous robotic inspection in complex and unknown environments. This capability is crucial for efficient and precise inspections in various r…
A Multi-step Dynamics Modeling Framework For Autonomous Driving In Multiple Environments
Jason Gibson, Bogdan Vlahov, David Fan +4
Modeling dynamics is often the first step to making a vehicle autonomous. While on-road autonomous vehicles have been extensively studied, off-road vehicles pose many challenging m…
STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation; Results from the DARPA Subterranean Challenge
Anushri Dixit, David D. Fan, Kyohei Otsu +3
Although autonomy has gained widespread usage in structured and controlled environments, robotic autonomy in unknown and off-road terrain remains a difficult problem. Extreme, off-…
PrePARE: Predictive Proprioception for Agile Failure Event Detection in Robotic Exploration of Extreme Terrains
Sharmita Dey, David Fan, Robin Schmid +5
Legged robots can traverse a wide variety of terrains, some of which may be challenging for wheeled robots, such as stairs or highly uneven surfaces. However, quadruped robots face…
Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments
Nitish Dashora, Daniel Shin, Dhruv Shah +5
Geometric methods for solving open-world off-road navigation tasks, by learning occupancy and metric maps, provide good generalization but can be brittle in outdoor environments th…
Learning Risk-aware Costmaps for Traversability in Challenging Environments
David D. Fan, Sharmita Dey, Ali-akbar Agha-mohammadi +1
One of the main challenges in autonomous robotic exploration and navigation in unknown and unstructured environments is determining where the robot can or cannot safely move. A sig…
Shot Contrastive Self-Supervised Learning for Scene Boundary Detection
Shixing Chen, Xiaohan Nie, David Fan +3
Scenes play a crucial role in breaking the storyline of movies and TV episodes into semantically cohesive parts. However, given their complex temporal structure, finding scene boun…
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…
STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation
David D. Fan, Kyohei Otsu, Yuki Kubo +3
Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem. Extrem…
PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments
Sung-Kyun Kim, Amanda Bouman, Gautam Salhotra +4
In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion…
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 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…
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…
Deep Learning Tubes for Tube MPC
David D. Fan, Ali-akbar Agha-mohammadi, Evangelos A. Theodorou
Learning-based control aims to construct models of a system to use for planning or trajectory optimization, e.g. in model-based reinforcement learning. In order to obtain guarantee…
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…
Schrödinger Approach to Optimal Control of Large-Size Populations
Kaivalya Bakshi, David D. Fan, Evangelos A. Theodorou
Large-size populations consisting of a continuum of identical and non-cooperative agents with stochastic dynamics are useful in modeling various biological and engineered systems.…
MPC-Inspired Neural Network Policies for Sequential Decision Making
Marcus Pereira, David D. Fan, Gabriel Nakajima An +1
In this paper we investigate the use of MPC-inspired neural network policies for sequential decision making. We introduce an extension to the DAgger algorithm for training such pol…
Model-Based Stochastic Search for Large Scale Optimization of Multi-Agent UAV Swarms
David D. Fan, Evangelos Theodorou, John Reeder
Recent work from the reinforcement learning community has shown that Evolution Strategies are a fast and scalable alternative to other reinforcement learning methods. In this paper…
Differential Dynamic Programming for time-delayed systems
David D. Fan, Evangelos A. Theodorou
Trajectory optimization considers the problem of deciding how to control a dynamical system to move along a trajectory which minimizes some cost function. Differential Dynamic Prog…