7 citations · 7 across the 1 of their papers we have counts for
9 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…
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…
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…
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…
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…
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…