8 citations · 29 across the 27 of their papers we have counts for
32 papers
SymAnt: Grounding Long-Horizon Action Anticipation with Adaptable Symbolic Priors
Fnu Aryan, Yaqi Xie, Joseph Campbell +6
As robots and intelligent systems increasingly interact with humans, they must anticipate long sequences of future actions from only brief observations. While purely neural models…
Balancing Reference Guidance and Free Generation in Trajectory Rollouts for Reasoning RL
Hanyu Wang, Nakul Agarwal, Hossein Nourkhiz Mahjoub +4
A verified reference solution provides a correct trajectory for training a reasoning model. Alternatively, a prefix of the reference can guide the model in generating a trajectory…
Watch, Infer, Coordinate: Inferring Robot Partner Constraints for Zero-Shot Coordination
Suyu Ye, Zheyuan Zhang, Vaishnav Tadiparthi +5
Robots operating in the physical world will increasingly need to coordinate with other robots, particularly in manipulation tasks where an object may be too large or heavy for a si…
JEPA-TTT: Persistent Test-Time Training of Latent World Models for Planning under Dynamics Shifts
Zheyuan Zhang, Suyu Ye, Nakul Agarwal +5
World models enable agents to plan by predicting future states of the environment, but their predictions can become unreliable when test-time dynamics differ from those seen during…
Test-Time Unlearning via Sparse Autoencoder
Pingzhi Li, Jinhao Duan, Vaishnav Tadiparthi +6
Machine unlearning aims to remove specific knowledge from a trained large language model (LLM) without retraining from scratch. Existing methods modify model weights via gradient a…
Bayesian Partner Modelling enables Adaptive Replanning for LLM Coordination
Harsh Goel, Aditya Sai Ellendula, Vaishnav Tadiparthi +3
Multi-agent Large Language Model (LLM) systems often struggle to collaborate with new teammates whose strategies shift mid-task. Because agents execute multi-step or temporally ext…
Generative Skill Composition for LLM Agents
Xinyu Zhao, Zhen Tan, Vaishnav Tadiparthi +5
Recent LLM agents benefit from skills for solving complex tasks. Skills encapsulate modular packages of procedural knowledge and instructions for performing specialized tasks, such…
Understanding the Role of Hallucination in Reinforcement Post-Training of Multimodal Reasoning Models
Gengwei Zhang, Jie Peng, Zhen Tan +6
The recent success of reinforcement learning (RL) in large reasoning models has inspired the growing adoption of RL for post-training Multimodal Large Language Models (MLLMs) to en…
SSR: A Generic Framework for Text-Aided Map Compression for Localization
Mohammad Omama, Po-han Li, Harsh Goel +6
Mapping is crucial in robotics for localization and downstream decision-making. As robots are deployed in ever-broader settings, the maps they rely on continue to increase in size.…
Learning Robust Reasoning through Guided Adversarial Self-Play
Shuozhe Li, Vaishnav Tadiparthi, Kwonjoon Lee +6
Reinforcement learning from verifiable rewards (RLVR) produces strong reasoning models, yet they can fail catastrophically when the conditioning context is fallible (e.g., corrupte…
Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction
Xu Shen, Qi Zhang, Song Wang +8
Large Language Model based multi-agent systems (MAS) excel at collaborative problem solving but remain brittle to cascading errors: a single faulty step can propagate across agents…
MR-LDM -- The Merge-Reactive Longitudinal Decision Model: Game Theoretic Human Decision Modeling for Interactive Sim Agents
Dustin Holley, Jovin D'sa, Hossein Nourkhiz Mahjoub +1
Enhancing simulation environments to replicate real-world driver behavior, i.e., more humanlike sim agents, is essential for developing autonomous vehicle technology. In the contex…
SMART-Merge Planner: A Safe Merging and Real-Time Motion Planner for Autonomous Highway On-Ramp Merging
Toktam Mohammadnejad, Jovin D'sa, Behdad Chalaki +2
Merging onto a highway is a complex driving task that requires identifying a safe gap, adjusting speed, often interactions to create a merging gap, and completing the merge maneuve…
Dual Control for Interactive Autonomous Merging with Model Predictive Diffusion
Jacob Knaup, Jovin D'sa, Behdad Chalaki +3
Interactive decision-making is essential in applications such as autonomous driving, where the agent must infer the behavior of nearby human drivers while planning in real-time. Tr…
In Search of a Lost Metric: Human Empowerment as a Pillar of Socially Conscious Navigation
Vasanth Reddy Baddam, Behdad Chalaki, Vaishnav Tadiparthi +4
In social robot navigation, traditional metrics like proxemics and behavior naturalness emphasize human comfort and adherence to social norms but often fail to capture an agent's a…
Symbolic Graph Inference for Compound Scene Understanding
FNU Aryan, Simon Stepputtis, Sarthak Bhagat +4
Scene understanding is a fundamental capability needed in many domains, ranging from question-answering to robotics. Unlike recent end-to-end approaches that must explicitly learn…
Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication
Huao Li, Hossein Nourkhiz Mahjoub, Behdad Chalaki +5
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.…
Modeling the Lane-Change Reactions to Merging Vehicles for Highway On-Ramp Simulations
Dustin Holley, Jovin Dsa, Hossein Nourkhiz Mahjoub +4
Enhancing simulation environments to replicate real-world driver behavior is essential for developing Autonomous Vehicle technology. While some previous works have studied the yiel…
Active Learning with Dual Model Predictive Path-Integral Control for Interaction-Aware Autonomous Highway On-ramp Merging
Jacob Knaup, Jovin D'sa, Behdad Chalaki +4
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 hu…
Multi-Robot Cooperative Navigation in Crowds: A Game-Theoretic Learning-Based Model Predictive Control Approach
Viet-Anh Le, Vaishnav Tadiparthi, Behdad Chalaki +4
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 predic…
Social Navigation in Crowded Environments with Model Predictive Control and Deep Learning-Based Human Trajectory Prediction
Viet-Anh Le, Behdad Chalaki, Vaishnav Tadiparthi +3
Crowd navigation has received increasing attention from researchers over the last few decades, resulting in the emergence of numerous approaches aimed at addressing this problem to…
MR-IDM -- Merge Reactive Intelligent Driver Model: Towards Enhancing Laterally Aware Car-following Models
Dustin Holley, Jovin D'sa, Hossein Nourkhiz Mahjoub +3
This paper discusses the limitations of existing microscopic traffic models in accounting for the potential impacts of on-ramp vehicles on the car-following behavior of main-lane v…
Task-aware Distributed Source Coding under Dynamic Bandwidth
Po-han Li, Sravan Kumar Ankireddy, Ruihan Zhao +5
Efficient compression of correlated data is essential to minimize communication overload in multi-sensor networks. In such networks, each sensor independently compresses the data a…
Minimally Disruptive Cooperative Lane-change Maneuvers
Behdad Chalaki, Vaishnav Tadiparthi, Hossein Nourkhiz Mahjoub +5
A lane-change maneuver on a congested highway could be severely disruptive or even infeasible without the cooperation of neighboring cars. However, cooperation with other vehicles…