90 citations · 230 across the 45 of their papers we have counts for
31 papers · 1 filter
Adapting Generalist Vehicle Models for High-Speed MPC Across Terrains
Rwik Rana, Jesse Quattrociocchi, Christian Ellis +3
High-speed off-road autonomy requires precise closed-loop control for a target vehicle while remaining robust across changing terrains. Recent forward kinodynamic (FKD) prediction…
OVerSeeC: Open-Vocabulary Costmap Generation from Satellite Images and Natural Language
Rwik Rana, Jesse Quattrociocchi, Dongmyeong Lee +5
Aerial imagery provides essential global context for autonomous navigation, enabling route planning at scales inaccessible to onboard sensing. We address the problem of generating…
BEV-Patch-PF: Particle Filtering with BEV-Aerial Feature Matching for Off-Road Geo-Localization
Dongmyeong Lee, Jesse Quattrociocchi, Christian Ellis +5
We propose BEV-Patch-PF, a GPS-free sequential geo-localization system that integrates a particle filter with learned bird's-eye-view (BEV) and aerial feature maps. From onboard RG…
Terrain Costmap Generation via Scaled Preference Conditioning
Luisa Mao, Garrett Warnell, Peter Stone +1
Successful autonomous robot navigation in off-road domains requires the ability to generate high-quality terrain costmaps that are able to both generalize well over a wide variety…
ComposableNav: Instruction-Following Navigation in Dynamic Environments via Composable Diffusion
Zichao Hu, Chen Tang, Michael J. Munje +6
This paper considers the problem of enabling robots to navigate dynamic environments while following instructions. The challenge lies in the combinatorial nature of instruction spe…
SocialNav-SUB: Benchmarking VLMs for Scene Understanding in Social Robot Navigation
Michael J. Munje, Chen Tang, Shuijing Liu +6
Robot navigation in dynamic, human-centered environments requires socially-compliant decisions grounded in robust scene understanding. Recent Vision-Language Models (VLMs) exhibit…
VertiFormer: A Data-Efficient Multi-Task Transformer for Off-Road Robot Mobility
Mohammad Nazeri, Anuj Pokhrel, Alexandyr Card +3
Sophisticated learning architectures, e.g., Transformers, present a unique opportunity for robots to understand complex vehicle-terrain kinodynamic interactions for off-road mobili…
PACER: Preference-conditioned All-terrain Costmap Generation
Luisa Mao, Garrett Warnell, Peter Stone +1
In autonomous robot navigation, terrain cost assignment is typically performed using a semantics-based paradigm in which terrain is first labeled using a pre-trained semantic class…
VertiCoder: Self-Supervised Kinodynamic Representation Learning on Vertically Challenging Terrain
Mohammad Nazeri, Aniket Datar, Anuj Pokhrel +3
We present VertiCoder, a self-supervised representation learning approach for robot mobility on vertically challenging terrain. Using the same pre-training process, VertiCoder can…
Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The 3rd BARN Challenge at ICRA 2024
Xuesu Xiao, Zifan Xu, Aniket Datar +16
The 3rd BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024) in Yokohama, Japan and co…
STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience
Haresh Karnan, Elvin Yang, Daniel Farkash +3
Terrain awareness, i.e., the ability to identify and distinguish different types of terrain, is a critical ability that robots must have to succeed at autonomous off-road navigatio…
Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference Aligned Path Planning
Haresh Karnan, Elvin Yang, Garrett Warnell +2
Autonomous mobility tasks such as lastmile delivery require reasoning about operator indicated preferences over terrains on which the robot should navigate to ensure both robot saf…
Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The 2nd BARN Challenge at ICRA 2023
Xuesu Xiao, Zifan Xu, Garrett Warnell +8
The 2nd BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023) in London, UK and continu…
Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways
Jin-Soo Park, Xuesu Xiao, Garrett Warnell +2
While current systems for autonomous robot navigation can produce safe and efficient motion plans in static environments, they usually generate suboptimal behaviors when multiple r…
Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The BARN Challenge at ICRA 2022
Xuesu Xiao, Zifan Xu, Zizhao Wang +14
The BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2022 IEEE International Conference on Robotics and Automation (ICRA 2022) in Philadelphia, PA. The aim…
High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization
Pranav Atreya, Haresh Karnan, Kavan Singh Sikand +3
Accurate control of robots at high speeds requires a control system that can take into account the kinodynamic interactions of the robot with the environment. Prior works on learni…
VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics
Haresh Karnan, Kavan Singh Sikand, Pranav Atreya +5
One of the key challenges in high speed off road navigation on ground vehicles is that the kinodynamics of the vehicle terrain interaction can differ dramatically depending on the…
Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation
Haresh Karnan, Anirudh Nair, Xuesu Xiao +6
Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a 'socially compliant' manner in the presence of other intelligent agents such as humans…
Adversarial Imitation Learning from Video using a State Observer
Haresh Karnan, Garrett Warnell, Faraz Torabi +1
The imitation learning research community has recently made significant progress towards the goal of enabling artificial agents to imitate behaviors from video demonstrations alone…
APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback
Zizhao Wang, Xuesu Xiao, Garrett Warnell +1
Classical autonomous navigation systems can control robots in a collision-free manner, oftentimes with verifiable safety and explainability. When facing new environments, however,…
VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation
Haresh Karnan, Garrett Warnell, Xuesu Xiao +1
While imitation learning for vision based autonomous mobile robot navigation has recently received a great deal of attention in the research community, existing approaches typicall…
APPL: Adaptive Planner Parameter Learning
Xuesu Xiao, Zizhao Wang, Zifan Xu +5
While current autonomous navigation systems allow robots to successfully drive themselves from one point to another in specific environments, they typically require extensive manua…
Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey
Xuesu Xiao, Bo Liu, Garrett Warnell +1
Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated navigati…
APPLI: Adaptive Planner Parameter Learning From Interventions
Zizhao Wang, Xuesu Xiao, Bo Liu +2
While classical autonomous navigation systems can typically move robots from one point to another safely and in a collision-free manner, these systems may fail or produce suboptima…