16 citations · 63 across the 30 of their papers we have counts for
30 papers
HUMEMBR: Learning Human Routines for Predictive Embodied Navigation
Samira Huber, Klaas Pelzer, Duc M. Nguyen +2
Understanding and navigating human-centered environments over extended periods of time while considering human behavior and routines remains a fundamental challenge in robotics. In…
GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring
Linji Wang, Zifan Xu, Peter Stone +1
Curriculum learning has emerged as a promising approach for training complex robotics tasks, yet current applications predominantly rely on manually designed curricula, which deman…
Grounded Curriculum Learning
Linji Wang, Zifan Xu, Peter Stone +1
The high cost of real-world data for robotics Reinforcement Learning (RL) leads to the wide usage of simulators. Despite extensive work on building better dynamics models for simul…
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…
Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning
Zifan Xu, Amir Hossain Raj, Xuesu Xiao +1
Recent advances of locomotion controllers utilizing deep reinforcement learning (RL) have yielded impressive results in terms of achieving rapid and robust locomotion across challe…
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination
Saad Abdul Ghani, Zizhao Wang, Peter Stone +1
This paper introduces Dynamic Learning from Learned Hallucination (Dyna-LfLH), a self-supervised method for training motion planners to navigate environments with dense and dynamic…
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning
Zizhao Wang, Caroline Wang, Xuesu Xiao +2
Two desiderata of reinforcement learning (RL) algorithms are the ability to learn from relatively little experience and the ability to learn policies that generalize to a range of…
A Study on Learning Social Robot Navigation with Multimodal Perception
Bhabaranjan Panigrahi, Amir Hossain Raj, Mohammad Nazeri +1
Autonomous mobile robots need to perceive the environments with their onboard sensors (e.g., LiDARs and RGB cameras) and then make appropriate navigation decisions. In order to nav…
How susceptible are LLMs to Logical Fallacies?
Amirreza Payandeh, Dan Pluth, Jordan Hosier +2
This paper investigates the rational thinking capability of Large Language Models (LLMs) in multi-round argumentative debates by exploring the impact of fallacious arguments on the…
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…
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
Anthony Francis, Claudia Pérez-D'Arpino, Chengshu Li +28
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation ha…
Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terrain
Aniket Datar, Chenhui Pan, Xuesu Xiao
Most autonomous navigation systems assume wheeled robots are rigid bodies and their 2D planar workspaces can be divided into free spaces and obstacles. However, recent wheeled mobi…
Team Coordination on Graphs with State-Dependent Edge Cost
Sara Oughourli, Manshi Limbu, Zechen Hu +3
This paper studies a team coordination problem in a graph environment. Specifically, we incorporate "support" action which an agent can take to reduce the cost for its teammate to…
Toward Human-Like Social Robot Navigation: A Large-Scale, Multi-Modal, Social Human Navigation Dataset
Duc M. Nguyen, Mohammad Nazeri, Amirreza Payandeh +2
Humans are well-adept at navigating public spaces shared with others, where current autonomous mobile robots still struggle: while safely and efficiently reaching their goals, huma…
Toward Wheeled Mobility on Vertically Challenging Terrain: Platforms, Datasets, and Algorithms
Aniket Datar, Chenhui Pan, Mohammad Nazeri +1
Most conventional wheeled robots can only move in flat environments and simply divide their planar workspaces into free spaces and obstacles. Deeming obstacles as non-traversable s…
Learning Real-world Autonomous Navigation by Self-Supervised Environment Synthesis
Zifan Xu, Anirudh Nair, Xuesu Xiao +1
Machine learning approaches have recently enabled autonomous navigation for mobile robots in a data-driven manner. Since most existing learning-based navigation systems are trained…
Benchmarking Reinforcement Learning Techniques for Autonomous Navigation
Zifan Xu, Bo Liu, Xuesu Xiao +2
Deep reinforcement learning (RL) has brought many successes for autonomous robot navigation. However, there still exists important limitations that prevent real-world use of RL-bas…
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…
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
Xuesu Xiao, Tingnan Zhang, Krzysztof Choromanski +14
Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e.g., in cluttered home environments or in human-occupied publi…
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…
Causal Dynamics Learning for Task-Independent State Abstraction
Zizhao Wang, Xuesu Xiao, Zifan Xu +2
Learning dynamics models accurately is an important goal for Model-Based Reinforcement Learning (MBRL), but most MBRL methods learn a dense dynamics model which is vulnerable to sp…
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…
A Protocol for Validating Social Navigation Policies
Sören Pirk, Edward Lee, Xuesu Xiao +3
Enabling socially acceptable behavior for situated agents is a major goal of recent robotics research. Robots should not only operate safely around humans, but also abide by comple…
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…