37 citations · 64 across the 14 of their papers we have counts for
15 papers
Characterizing the Robustness of Black-Box LLM Planners Under Perturbed Observations with Adaptive Stress Testing
Neeloy Chakraborty, John Pohovey, Melkior Ornik +1
Large language models (LLMs) have recently demonstrated success in decision-making tasks including planning, control, and prediction, but their tendency to hallucinate unsafe and u…
An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving
Tianchen Ji, Neeloy Chakraborty, Andre Schreiber +1
As automated vehicles enter public roads, safety in a near-infinite number of driving scenarios becomes one of the major concerns for the widespread adoption of fully autonomous dr…
HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments
Shuijing Liu, Haochen Xia, Fatemeh Cheraghi Pouria +5
We study the problem of robot navigation in dense and interactive crowds with static constraints such as corridors and furniture. Previous methods fail to consider all types of spa…
Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation
Neeloy Chakraborty, Yixiao Fang, Andre Schreiber +6
Teleoperation is an important technology to enable supervisors to control agricultural robots remotely. However, environmental factors in dense crop rows and limitations in network…
Cooperative Advisory Residual Policies for Congestion Mitigation
Aamir Hasan, Neeloy Chakraborty, Haonan Chen +3
Fleets of autonomous vehicles can mitigate traffic congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these a…
Lessons in Cooperation: A Qualitative Analysis of Driver Sentiments towards Real-Time Advisory Systems from a Driving Simulator User Study
Aamir Hasan, Neeloy Chakraborty, Haonan Chen +2
Real-time Advisory (RTA) systems, such as navigational and eco-driving assistants, are becoming increasingly ubiquitous in vehicles due to their benefits for users and society. Unt…
Structured Graph Network for Constrained Robot Crowd Navigation with Low Fidelity Simulation
Shuijing Liu, Kaiwen Hong, Neeloy Chakraborty +1
We investigate the feasibility of deploying reinforcement learning (RL) policies for constrained crowd navigation using a low-fidelity simulator. We introduce a representation of t…
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art
Neeloy Chakraborty, Melkior Ornik, Katherine Driggs-Campbell
Autonomous systems are soon to be ubiquitous, spanning manufacturing, agriculture, healthcare, entertainment, and other industries. Most of these systems are developed with modular…
PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
Aamir Hasan, Neeloy Chakraborty, Haonan Chen +3
Intelligent driving systems can be used to mitigate congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these…
Structural Attention-Based Recurrent Variational Autoencoder for Highway Vehicle Anomaly Detection
Neeloy Chakraborty, Aamir Hasan, Shuijing Liu +4
In autonomous driving, detection of abnormal driving behaviors is essential to ensure the safety of vehicle controllers. Prior works in vehicle anomaly detection have shown that mo…
Towards Co-operative Congestion Mitigation
Aamir Hasan, Neeloy Chakraborty, Cathy Wu +1
The effects of traffic congestion are widespread and are an impedance to everyday life. Piecewise constant driving policies have shown promise in helping mitigate traffic congestio…
A Data-Efficient Visual-Audio Representation with Intuitive Fine-tuning for Voice-Controlled Robots
Peixin Chang, Shuijing Liu, Tianchen Ji +3
A command-following robot that serves people in everyday life must continually improve itself in deployment domains with minimal help from its end users, instead of engineers. Prev…
Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph
Shuijing Liu, Peixin Chang, Zhe Huang +6
We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. Most previous reinforcement learning (RL) based methods fail to consider differen…
Learning to Navigate Intersections with Unsupervised Driver Trait Inference
Shuijing Liu, Peixin Chang, Haonan Chen +2
Navigation through uncontrolled intersections is one of the key challenges for autonomous vehicles. Identifying the subtle differences in hidden traits of other drivers can bring s…
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning
Shuijing Liu, Peixin Chang, Weihang Liang +2
Safe and efficient navigation through human crowds is an essential capability for mobile robots. Previous work on robot crowd navigation assumes that the dynamics of all agents are…