Publications (65)
Convergence and Emergence of In-Context Reinforcement Learning with Chain of Thought
Zixuan Xie, Xinyu Liu, Rohan Chandra +1
In-context reinforcement learning (ICRL) refers to the ability of RL agents to adapt to new tasks at inference time without parameter updates by conditioning on additional context.…
EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle
Trisha Mittal, Pooja Guhan, Uttaran Bhattacharya +3
We present EmotiCon, a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. Motivated by Frege's Context Principle from psychology…
Multi-Robot Navigation in Social Mini-Games: Definitions, Taxonomy, and Algorithms
Rohan Chandra, Shubham Singh, Wenhao Luo +1
The "Last Mile Challenge" has long been considered an important, yet unsolved, challenge for autonomous vehicles, public service robots, and delivery robots. A central issue in thi…
DR. Nav: Semantic-Geometric Representations for Proactive Dead-End Recovery and Navigation
Vignesh Rajagopal, Kasun Weerakoon Kulathun Mudiyanselage, Gershom Devake Seneviratne +5
We present DR. Nav (Dead-End Recovery-aware Navigation), a novel approach to autonomous navigation in scenarios where dead-end detection and recovery are critical, particularly in…
Decentralized Safe and Scalable Multi-Agent Control under Limited Actuation
Vrushabh Zinage, Abhishek Jha, Rohan Chandra +1
To deploy safe and agile robots in cluttered environments, there is a need to develop fully decentralized controllers that guarantee safety, respect actuation limits, prevent deadl…
Rethinking Social Robot Navigation: Leveraging the Best of Two Worlds
Amir Hossain Raj, Zichao Hu, Haresh Karnan +6
Empowering robots to navigate in a socially compliant manner is essential for the acceptance of robots moving in human-inhabited environments. Previously, roboticists have develope…
DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviors
Adarsh Jagan Sathyamoorthy, Jing Liang, Utsav Patel +3
We present DenseCAvoid, a novel navigation algorithm for navigating a robot through dense crowds and avoiding collisions by anticipating pedestrian behaviors. Our formulation uses…
Estimating Emotion Contagion on Social Media via Localized Diffusion in Dynamic Graphs
Trisha Mittal, Puneet Mathur, Rohan Chandra +5
We present a computational approach for estimating emotion contagion on social media networks. Built on a foundation of psychology literature, our approach estimates the degree to…
Deadlock-free, Safe, and Decentralized Multi-Robot Navigation in Social Mini-Games via Discrete-Time Control Barrier Functions
Rohan Chandra, Vrushabh Zinage, Efstathios Bakolas +2
We present an approach to ensure safe and deadlock-free navigation for decentralized multi-robot systems operating in constrained environments, including doorways and intersections…
METEOR:A Dense, Heterogeneous, and Unstructured Traffic Dataset With Rare Behaviors
Rohan Chandra, Xijun Wang, Mridul Mahajan +5
We present a new traffic dataset, METEOR, which captures traffic patterns and multi-agent driving behaviors in unstructured scenarios. METEOR consists of more than 1000 one-minute…
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…
Using Graph-Theoretic Machine Learning to Predict Human Driver Behavior
Rohan Chandra, Aniket Bera, Dinesh Manocha
Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic environment composed of human drivers and do not adapt to local conditions and socio-cultural n…
Prompt-Driven Domain Adaptation for End-to-End Autonomous Driving via In-Context RL
Aleesha Khurram, Amir Moeini, Shangtong Zhang +1
Despite significant progress and advances in autonomous driving, many end-to-end systems still struggle with domain adaptation (DA), such as transferring a policy trained under cle…
Beyond Linear Attention: Softmax Transformers Implement In-Context Reinforcement Learning
Zixuan Xie, Xinyu Liu, Claire Chen +3
In-context reinforcement learning (ICRL) studies agents that, after pretraining, adapt to new tasks by conditioning on additional context without parameter updates. Existing theore…
Empowering Dynamic Urban Navigation with Stereo and Mid-Level Vision
Wentao Zhou, Xuweiyi Chen, Vignesh Rajagopal +3
The success of foundation models in language and vision motivated research in fully end-to-end robot navigation foundation models (NFMs). NFMs directly map monocular visual input t…
Towards Provable Emergence of In-Context Reinforcement Learning
Jiuqi Wang, Rohan Chandra, Shangtong Zhang
Typically, a modern reinforcement learning (RL) agent solves a task by updating its neural network parameters to adapt its policy to the task. Recently, it has been observed that s…
CMetric: A Driving Behavior Measure Using Centrality Functions
Rohan Chandra, Uttaran Bhattacharya, Trisha Mittal +2
We present a new measure, CMetric, to classify driver behaviors using centrality functions. Our formulation combines concepts from computational graph theory and social traffic psy…
GAMEOPT+: Improving Fuel Efficiency in Unregulated Heterogeneous Traffic Intersections via Optimal Multi-agent Cooperative Control
Nilesh Suriyarachchi, Rohan Chandra, Arya Anantula +2
Better fuel efficiency leads to better financial security as well as a cleaner environment. We propose a novel approach for improving fuel efficiency in unstructured and unregulate…
Physics-Informed CNN-LSTM for Street-Scale Urban Flood Prediction: Reconciling Aggregate Accuracy and Street-Level Plausibility
Luc DCosta, Yidi Wang, Jonathan L. Goodall +1
Deep learning surrogate models trained with mean-squared-error loss produce statistically accurate but physically unconstrained flood predictions: water may flow uphill, appear spo…
Are LLMs The Way Forward? A Case Study on LLM-Guided Reinforcement Learning for Decentralized Autonomous Driving
Timur Anvar, Jeffrey Chen, Yuyan Wang +1
Autonomous vehicle navigation in complex environments such as dense and fast-moving highways and merging scenarios remains an active area of research. A key limitation of RL is its…
Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers
Rohan Chandra, Mingyu Wang, Mac Schwager +1
We present a novel approach for risk-aware planning with human agents in multi-agent traffic scenarios. Our approach takes into account the wide range of human driver behaviors on…
Neural Differentiable Integral Control Barrier Functions for Unknown Nonlinear Systems with Input Constraints
Vrushabh Zinage, Rohan Chandra, Efstathios Bakolas
In this paper, we propose a deep learning based control synthesis framework for fast and online computation of controllers that guarantees the safety of general nonlinear control s…
RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments
Rohan Chandra, Uttaran Bhattacharya, Tanmay Randhavane +2
We present a realtime tracking algorithm, RoadTrack, to track heterogeneous road-agents in dense traffic videos. Our approach is designed for traffic scenarios that consist of diff…
Group Fairness in Multi-Task Reinforcement Learning
Kefan Song, Runnan Jiang, Rohan Chandra +1
This paper addresses a critical societal consideration in the application of Reinforcement Learning (RL): ensuring equitable outcomes across different demographic groups in multi-t…
GameChat: Multi-LLM Dialogue for Safe, Agile, and Socially Optimal Multi-Agent Navigation in Constrained Environments
Vagul Mahadevan, Shangtong Zhang, Rohan Chandra
Safe, agile, and socially compliant multi-robot navigation in cluttered and constrained environments remains a critical challenge. This is especially difficult with self-interested…
Safe In-Context Reinforcement Learning
Amir Moeini, Minjae Kwon, Alper Kamil Bozkurt +4
In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks without any parameter updates, in…
Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs
Rohan Chandra, Tianrui Guan, Srujan Panuganti +4
We present a novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning. We predict both the low-level infor…
Dynamic Control Barrier Function Regulation with Vision-Language Models for Safe, Adaptive, and Realtime Visual Navigation
Jeffrey Chen, Rohan Chandra
Robots operating in dynamic, unstructured environments must balance safety and efficiency under potentially limited sensing. While control barrier functions (CBFs) provide principl…
Disturbance Observer-based Robust Integral Control Barrier Functions for Nonlinear Systems with High Relative Degree
Vrushabh Zinage, Rohan Chandra, Efstathios Bakolas
In this paper, we consider the problem of safe control synthesis of general controlled nonlinear systems in the presence of bounded additive disturbances. Towards this aim, we firs…
SOCIALMAPF: Optimal and Efficient Multi-Agent Path Finding with Strategic Agents for Social Navigation
Rohan Chandra, Rahul Maligi, Arya Anantula +1
We propose an extension to the MAPF formulation, called SocialMAPF, to account for private incentives of agents in constrained environments such as doorways, narrow hallways, and c…
B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Driving
Angelos Mavrogiannis, Rohan Chandra, Dinesh Manocha
We address the problem of ego-vehicle navigation in dense simulated traffic environments populated by road agents with varying driver behaviors. Navigation in such environments is…
LiveNet: Robust, Minimally Invasive Multi-Robot Control for Safe and Live Navigation in Constrained Environments
Srikar Gouru, Siddharth Lakkoju, Rohan Chandra
Robots in densely populated real-world environments frequently encounter constrained and cluttered situations such as passing through narrow doorways, hallways, and corridor inters…
Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios
Chirag Parikh, Ravi Shankar Mishra, Rohan Chandra +1
Recognizing driving behaviors is important for downstream tasks such as reasoning, planning, and navigation. Existing video recognition approaches work well for common behaviors (e…
Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds
Rohan Chandra, Haresh Karnan, Negar Mehr +2
Social robot navigation in crowded public spaces such as university campuses, restaurants, grocery stores, and hospitals, is an increasingly important area of research. One of the…
Experience Replay Addresses Loss of Plasticity in Continual Learning
Jiuqi Wang, Rohan Chandra, Shangtong Zhang
Loss of plasticity is one of the main challenges in continual learning with deep neural networks, where neural networks trained via backpropagation gradually lose their ability to…
STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits
Uttaran Bhattacharya, Trisha Mittal, Rohan Chandra +3
We present a novel classifier network called STEP, to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture. Gi…
FACA: Fair and Agile Multi-Robot Collision Avoidance in Constrained Environments with Dynamic Priorities
Jaskirat Singh, Rohan Chandra
Multi-robot systems are increasingly being used for critical applications such as rescuing injured people, delivering food and medicines, and monitoring key areas. These applicatio…
SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments
Divya Kothandaraman, Rohan Chandra, Dinesh Manocha
We present a novel approach for unsupervised road segmentation in adverse weather conditions such as rain or fog. This includes a new algorithm for source-free domain adaptation (S…
DAVE: Diverse Atomic Visual Elements Dataset with High Representation of Vulnerable Road Users in Complex and Unpredictable Environments
Xijun Wang, Pedro Sandoval-Segura, Chengyuan Zhang +7
Most existing traffic video datasets including Waymo are structured, focusing predominantly on Western traffic, which hinders global applicability. Specifically, most Asian scenari…
GAMEOPT: Optimal Real-time Multi-Agent Planning and Control for Dynamic Intersections
Nilesh Suriyarachchi, Rohan Chandra, John S. Baras +1
We propose GameOpt: a novel hybrid approach to cooperative intersection control for dynamic, multi-lane, unsignalized intersections. Safely navigating these complex and accident pr…
Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
Zixuan Xie, Xinyu Liu, Rohan Chandra +1
Linear TD() is one of the most fundamental reinforcement learning algorithms for policy evaluation. Previously, convergence rates are typically established under the assumption…
Texture Synthesis with Recurrent Variational Auto-Encoder
Rohan Chandra, Sachin Grover, Kyungjun Lee +2
We propose a recurrent variational auto-encoder for texture synthesis. A novel loss function, FLTBNK, is used for training the texture synthesizer. It is rotational and partially c…
TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions
Rohan Chandra, Uttaran Bhattacharya, Aniket Bera +1
We present a new algorithm for predicting the near-term trajectories of road-agents in dense traffic videos. Our approach is designed for heterogeneous traffic, where the road-agen…
Reward Is Enough: LLMs Are In-Context Reinforcement Learners
Kefan Song, Amir Moeini, Peng Wang +4
Reinforcement learning (RL) is a framework for solving sequential decision-making problems. In this work, we demonstrate that, surprisingly, RL emerges during the inference time of…
HumAIN: Human-Aware Implicit Social Robot Navigation
Daeun Song, Nhat Le, Jeffrey Chen +7
Effective social robot navigation requires sensitivity to human behavior, often revealed through subtle skeletal cues like gait and orientation. We present Human-Aware Implicit Soc…
Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models
Kefan Song, Jin Yao, Runnan Jiang +2
As Large Language Models (LLMs) become increasingly powerful and accessible to human users, ensuring fairness across diverse demographic groups, i.e., group fairness, is a critical…
LIVEPOINT: Fully Decentralized, Safe, Deadlock-Free Multi-Robot Control in Cluttered Environments with High-Dimensional Inputs
Jeffrey Chen, Rohan Chandra
Fully decentralized, safe, and deadlock-free multi-robot navigation in dynamic, cluttered environments is a critical challenge in robotics. Current methods require exact state meas…
Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization
Rohan Chandra, Rahul Menon, Zayne Sprague +2
This paper presents a fully decentralized approach for realtime non-cooperative multi-robot navigation in social mini-games, such as navigating through a narrow doorway or negotiat…
GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging
Rohan Chandra, Dinesh Manocha
We present a new method for multi-agent planning involving human drivers and autonomous vehicles (AVs) in unsignaled intersections, roundabouts, and during merging. In multi-agent…
M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues
Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra +2
We present M3ER, a learning-based method for emotion recognition from multiple input modalities. Our approach combines cues from multiple co-occurring modalities (such as face, tex…
A Survey of In-Context Reinforcement Learning
Amir Moeini, Jiuqi Wang, Jacob Beck +4
Reinforcement learning (RL) agents typically optimize their policies by performing expensive backward passes to update their network parameters. However, some agents can solve new…
PhasePack User Guide
Rohan Chandra, Ziyuan Zhong, Justin Hontz +3
"Phase retrieval" refers to the recovery of signals from the magnitudes (and not the phases) of linear measurements. While there has been a recent explosion in development of phase…
Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping
Uttaran Bhattacharya, Christian Roncal, Trisha Mittal +5
We present an autoencoder-based semi-supervised approach to classify perceived human emotions from walking styles obtained from videos or motion-captured data and represented as se…
iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning
Xiyang Wu, Rohan Chandra, Tianrui Guan +2
Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging for autonomous vehicles (AVs) due to their inability to infer the behaviors or intenti…
SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces
Zayne Sprague, Rohan Chandra, Jarrett Holtz +1
We present SocialGym 2, a multi-agent navigation simulator for social robot research. Our simulator models multiple autonomous agents, replicating real-world dynamics in complex en…
StylePredict: Machine Theory of Mind for Human Driver Behavior From Trajectories
Rohan Chandra, Aniket Bera, Dinesh Manocha
Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic environment composed of human drivers and do not adapt to local conditions and socio-cultural n…
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
Tianrui Guan, Jun Wang, Shiyi Lan +4
We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales bas…
BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments
Divya Kothandaraman, Rohan Chandra, Dinesh Manocha
We present an unsupervised adaptation approach for visual scene understanding in unstructured traffic environments. Our method is designed for unstructured real-world scenarios wit…
DensePeds: Pedestrian Tracking in Dense Crowds Using Front-RVO and Sparse Features
Rohan Chandra, Uttaran Bhattacharya, Aniket Bera +1
We present a pedestrian tracking algorithm, DensePeds, that tracks individuals in highly dense crowds (greater than 2 pedestrians per square meter). Our approach is designed for vi…
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues
Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra +2
We present a learning-based method for detecting real and fake deepfake multimedia content. To maximize information for learning, we extract and analyze the similarity between the…
RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs
Rohan Chandra, Uttaran Bhattacharya, Christian Roncal +2
We present RobustTP, an end-to-end algorithm for predicting future trajectories of road-agents in dense traffic with noisy sensor input trajectories obtained from RGB cameras (eith…
PhasePack: A Phase Retrieval Library
Rohan Chandra, Ziyuan Zhong, Justin Hontz +3
Phase retrieval deals with the estimation of complex-valued signals solely from the magnitudes of linear measurements. While there has been a recent explosion in the development of…
GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments
Tianrui Guan, Divya Kothandaraman, Rohan Chandra +3
We propose GANav, a novel group-wise attention mechanism to identify safe and navigable regions in off-road terrains and unstructured environments from RGB images. Our approach cla…
GraphRQI: Classifying Driver Behaviors Using Graph Spectrums
Rohan Chandra, Uttaran Bhattacharya, Trisha Mittal +3
We present a novel algorithm (GraphRQI) to identify driver behaviors from road-agent trajectories. Our approach assumes that the road-agents exhibit a range of driving traits, such…
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
Chen Tang, Ben Abbatematteo, Jiaheng Hu +3
Reinforcement learning (RL), particularly its combination with deep neural networks referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications,…