1 citations · 2 across the 28 of their papers we have counts for
28 papers
What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models
Amirhosein Chahe, Tyler Naes, Jovin D'sa +4
Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view. Current approaches often treat all occlusions with uniform con…
HOLO-MPPI: Multi-Scenario Motion Planning via Hierarchical Policy Optimization
Youngjae Min, Jovin D'sa, Faizan M. Tariq +3
Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning. End-to-end reinforcement learning (RL) can generalize across scenarios b…
Exact, Efficient, and Safe Occlusion-Aware Planning Using AH-Polyhedrons
Long Kiu Chung, David Isele, Toktam Mohammadnejad +4
Safely handling occlusions is a fundamental challenge for autonomous mobile robots operating in dynamic environments. This issue is especially prominent in autonomous valet parking…
VLM-Based Advanced Rider Assistance System for Motorcycle Safety
Mohamed Elnoor, Francesca Baldini, Ananya Trivedi +6
Motorcycles face disproportionately high crash risks compared to cars due to limited protection and heightened sensitivity to surface hazards, yet Advanced Rider Assistance Systems…
N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme
Yifan Xue, Toktam Mohammadnejad, Faizan M Tariq +5
Autonomous parking requires efficient path planning that ensures kinematic feasibility and collision avoidance in constrained environments. Hybrid A* is widely used but computation…
Selecting Spots by Explicitly Predicting Intention from Motion History Improves Performance in Autonomous Parking
Long Kiu Chung, David Isele, Faizan M. Tariq +3
In many applications of social navigation, existing works have shown that predicting and reasoning about human intentions can help robotic agents make safer and more socially accep…
Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams
Piyush Gupta, Sangjae Bae, Jiachen Li +1
Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; yet, it remains challenging due to the l…
ONRAP: Occupancy-driven Noise-Resilient Autonomous Path Planning
Faizan M. Tariq, Avinash Singh, Vipul Ramtekkar +4
Dynamic path planning must remain reliable in the presence of sensing noise, uncertain localization, and incomplete semantic perception. We propose a practical, implementation-frie…
Adaptive Time Step Flow Matching for Autonomous Driving Motion Planning
Ananya Trivedi, Anjian Li, Mohamed Elnoor +7
Autonomous driving requires reasoning about interactions with surrounding traffic. A prevailing approach is large-scale imitation learning on expert driving datasets, aimed at gene…
KGLAMP: Knowledge Graph-guided Language model for Adaptive Multi-robot Planning and Replanning
Chak Lam Shek, Faizan M. Tariq, Sangjae Bae +2
Heterogeneous multi-robot systems are increasingly used in long-horizon missions requiring coordinated planning across diverse capabilities. However, existing planning approaches s…
Occupancy-aware Trajectory Planning for Autonomous Valet Parking in Uncertain Dynamic Environments
Farhad Nawaz, Faizan M. Tariq, Sangjae Bae +5
Autonomous Valet Parking (AVP) requires planning under partial observability, where parking spot availability evolves as dynamic agents enter and exit spots. Existing approaches ei…
Active Probing with Multimodal Predictions for Motion Planning
Darshan Gadginmath, Farhad Nawaz, Minjun Sung +5
Navigation in dynamic environments requires autonomous systems to reason about uncertainties in the behavior of other agents. In this paper, we introduce a unified framework that c…
IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Kanghyun Ryu, Minjun Sung, Piyush Gupta +4
Motion planning for autonomous vehicles (AVs) in dense traffic is challenging, often leading to overly conservative behavior and unmet planning objectives. This challenge stems fro…
Graph-based Path Planning with Dynamic Obstacle Avoidance for Autonomous Parking
Farhad Nawaz, Minjun Sung, Darshan Gadginmath +6
Safe and efficient path planning in parking scenarios presents a significant challenge due to the presence of cluttered environments filled with static and dynamic obstacles. To ad…
Frenet Corridor Planner: An Optimal Local Path Planning Framework for Autonomous Driving
Faizan M. Tariq, Zheng-Hang Yeh, Avinash Singh +2
Motivated by the requirements for effectiveness and efficiency, path-speed decomposition-based trajectory planning methods have widely been adopted for autonomous driving applicati…
GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks
Haoqiang Kang, Enna Sachdeva, Piyush Gupta +2
Vision-Language Models (VLMs) have recently shown promising advancements in sequential decision-making tasks through task-specific fine-tuning. However, common fine-tuning methods,…
Graph-Grounded LLMs: Leveraging Graphical Function Calling to Minimize LLM Hallucinations
Piyush Gupta, Sangjae Bae, David Isele
The adoption of Large Language Models (LLMs) is rapidly expanding across various tasks that involve inherent graphical structures. Graphs are integral to a wide range of applicatio…
Delayed-Decision Motion Planning in the Presence of Multiple Predictions
David Isele, Alexandre Miranda Anon, Faizan M. Tariq +3
Reliable automated driving technology is challenged by various sources of uncertainties, in particular, behavioral uncertainties of traffic agents. It is common for traffic agents…
Predictive Planner for Autonomous Driving with Consistency Models
Anjian Li, Sangjae Bae, David Isele +2
Trajectory prediction and planning are essential for autonomous vehicles to navigate safely and efficiently in dynamic environments. Traditional approaches often treat them separat…
Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees
Piyush Gupta, David Isele, Enna Sachdeva +4
We present a novel mission-planning strategy for heterogeneous multi-robot teams, taking into account the specific constraints and capabilities of each robot. Our approach employs…
Deep Reinforcement Learning for Reach-Avoid-Stay Problems
Gabriel Chenevert, Jingqi Li, Achyuta kannan +2
Reach-Avoid-Stay (RAS) tasks are essential in applications where systems must safely reach a target set and remain within it under all bounded disturbances. Existing approaches eit…
Don't Get Stuck: A Deadlock Recovery Approach
Francesca Baldini, Faizan M. Tariq, Sangjae Bae +1
When multiple agents share space, interactions can lead to deadlocks, where no agent can advance towards its goal. This paper addresses this challenge with a deadlock recovery stra…
Gaussian Lane Keeping: A Robust Prediction Baseline
David Isele, Piyush Gupta, Xinyi Liu +1
Predicting agents' behavior for vehicles and pedestrians is challenging due to a myriad of factors including the uncertainty attached to different intentions, inter-agent interacti…
Adaptive Prediction Ensemble: Improving Out-of-Distribution Generalization of Motion Forecasting
Jinning Li, Jiachen Li, Sangjae Bae +1
Deep learning-based trajectory prediction models for autonomous driving often struggle with generalization to out-of-distribution (OOD) scenarios, sometimes performing worse than s…