activity
20172023
most citedTraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions

233 citations · 450 across the 31 of their papers we have counts for

collaborators

36 papers

eess.SY2023

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…

cs.RO2023★ 1 cited

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…

cs.RO2023★ 4 cited

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…

cs.RO2023★ 4 cited

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…

cs.RO2023★ 16 cited

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…

cs.MA2023★ 1 cited

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…