collaborators

7 papers

cs.CV2026

Streaming Interventions: Can Video Large Language Models Correct Mistakes as They Occur?

Apratim Bhattacharyya, Shweta Mahajan, Sanjay Haresh +5

Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos. This opens the door to the use of video (and multimodal) large lang…

cs.RO2026

MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving

Rajeev Yasarla, Deepti Hegde, Hsin-Pai Cheng +9

Vision-language-action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings due to being trained under traditional im…

cs.CV2026

Generative Scenario Rollouts for End-to-End Autonomous Driving

Rajeev Yasarla, Deepti Hegde, Shizhong Han +10

Vision-Language-Action (VLA) models are emerging as highly effective planning models for end-to-end autonomous driving systems. However, current works mostly rely on imitation lear…

cs.CV2025

Can Multi-Modal LLMs Provide Live Step-by-Step Task Guidance?

Apratim Bhattacharyya, Bicheng Xu, Sanjay Haresh +6

Multi-modal Large Language Models (LLM) have advanced conversational abilities but struggle with providing live, interactive step-by-step guidance, a key capability for future AI a…

cs.CV2025

RoCA: Robust Cross-Domain End-to-End Autonomous Driving

Rajeev Yasarla, Shizhong Han, Hsin-Pai Cheng +7

End-to-end (E2E) autonomous driving has recently emerged as a new paradigm, offering significant potential. However, few studies have looked into the practical challenge of deploym…

cs.CL2025

Enhancing Hallucination Detection through Noise Injection

Litian Liu, Reza Pourreza, Sunny Panchal +4

Large Language Models (LLMs) are prone to generating plausible yet incorrect responses, known as hallucinations. Effectively detecting hallucinations is therefore crucial for the s…

cs.CV2025

Distilling Multi-modal Large Language Models for Autonomous Driving

Deepti Hegde, Rajeev Yasarla, Hong Cai +7

Autonomous driving demands safe motion planning, especially in critical "long-tail" scenarios. Recent end-to-end autonomous driving systems leverage large language models (LLMs) as…