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Behdad Chalaki

6 accepted papers

2025

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning

Harsh Goel, Mohammad Omama, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari, Sandeep P. Chinchali
ICML 2025poster

Multi-agent reinforcement learning (MARL) has achieved significant progress in large-scale traffic control, autonomous vehicles, and robotics. Drawing inspiration from biological systems where roles naturally emerge to enable coordination, role-based MARL methods have been proposed to enhance cooper…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

Active Learning with Dual Model Predictive Path-Integral Control for Interaction-Aware Autonomous Highway On-ramp Merging

Jacob Knaup, Jovin D’sa, Behdad Chalaki, Tyler Naes, Hossein Nourkhiz Mahjoub, Ehsan Moradi-Pari +1
ICRA 2024poster

Merging into dense highway traffic for an autonomous vehicle is a complex decision-making task, wherein the vehicle must identify a potential gap and coordinate with surrounding human drivers, each of whom may exhibit diverse driving behaviors. Many existing methods consider other drivers to be dyna…

Cited by 4Source ↗Scholar ↗
2024

Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication

Huao Li, Hossein Nourkhiz Mahjoub, Behdad Chalaki, Vaishnav Tadiparthi, Kwonjoon Lee, Ehsan Moradi Pari +2
NeurIPS 2024poster

Multi-Agent Reinforcement Learning (MARL) methods have shown promise in enabling agents to learn a shared communication protocol from scratch and accomplish challenging team tasks. However, the learned language is usually not interpretable to humans or other agents not co-trained together, limiting…

Cited by 6Source ↗PDF ↗Scholar ↗
2024

Multi-Robot Cooperative Navigation in Crowds: A Game-Theoretic Learning-Based Model Predictive Control Approach

Viet-Anh Le, Vaishnav Tadiparthi, Behdad Chalaki, Hossein Nourkhiz Mahjoub, Jovin D’Sa, Ehsan Moradi-Pari +1
ICRA 2024poster

In this paper, we develop a control framework for the coordination of multiple robots as they navigate through crowded environments. Our framework comprises of a local model predictive control (MPC) for each robot and a social long short-term memory model that forecasts pedestrians’ trajectories. We…

Cited by 8Source ↗Scholar ↗
2024

Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Models

Muhan Lin, Shuyang Shi, Yue Guo, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari +3
EMNLP 2024finding

The correct specification of reward models is a well-known challenge in reinforcement learning.Hand-crafted reward functions often lead to inefficient or suboptimal policies and may not be aligned with user values.Reinforcement learning from human feedback is a successful technique that can mitigate…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

Social Navigation in Crowded Environments with Model Predictive Control and Deep Learning-Based Human Trajectory Prediction

Viet-Anh Le, Behdad Chalaki, Vaishnav Tadiparthi, Hossein Nourkhiz Mahjoub, Jovin D’Sa, Ehsan Moradi-Pari
IROS 2024poster

Navigating a robot among a crowd has received increasing attention from researchers over the last few decades, resulting in the emergence of numerous approaches aimed at addressing the problem of social navigation to date. Our proposed approach couples agent motion prediction and planning to avoid t…

Cited by 3Source ↗Scholar ↗