Amir Hossain Raj

Robot Squid Game: Quadrupedal Locomotion for Traversing Narrow Tunnels

Amir Hossain Raj, Dibyendu Das, Xuesu Xiao

Quadruped robots demonstrate exceptional potential for navigating complex terrain in critical applications such as search and rescue missions and infrastructure inspection However autonomous traversal of confined 3D environments including tunnels caves and collapsed structures remains a significant challenge Existing methods often struggle …

Robot Squid Game: Quadrupedal Locomotion for Traversing Narrow Tunnels

Amir Hossain Raj, Dibyendu Das, Xuesu Xiao

Quadruped robots demonstrate exceptional potential for navigating complex terrain in critical applications such as search and rescue missions and infrastructure inspection However autonomous traversal of confined 3D environments including tunnels caves and collapsed structures remains a significant challenge Existing methods often struggle …

MorFiC: Fixing Value Miscalibration for Zero-Shot Quadruped Transfer

Prakhar Mishra, Amir Hossain Raj, Xuesu Xiao, Dinesh Manocha

Generalizing learned locomotion policies across quadrupedal robots with different morphologies remains a challenge. Policies trained on a single robot often fail when deployed on embodiments with different mass distributions, kinematics, joint limits, or actuation constraints, forcing per-robot retraining. Prior works have approached …

MorFiC: Fixing Value Miscalibration for Zero-Shot Quadruped Transfer

Prakhar Mishra, Amir Hossain Raj, Xuesu Xiao, Dinesh Manocha

Generalizing learned locomotion policies across quadrupedal robots with different morphologies remains a challenge. Policies trained on a single robot often fail when deployed on embodiments with different mass distributions, kinematics, joint limits, or actuation constraints, forcing per-robot retraining. Prior works have approached …

TransCurriculum: Multi-Dimensional Curriculum Learning for Fast & Stable Locomotion

Prakhar Mishra, Amir Hossain Raj, Xuesu Xiao, Dinesh Manocha

High-speed legged locomotion struggles with stability and transfer losses at higher command velocities during deployment. One reason is that most curricula vary difficulty along single axis, for example increase the range of command velocities, terrain difficulty, or domain parameters (e.g. friction or …

TransCurriculum: Multi-Dimensional Curriculum Learning for Fast & Stable Locomotion

Prakhar Mishra, Amir Hossain Raj, Xuesu Xiao, Dinesh Manocha

High-speed legged locomotion struggles with stability and transfer losses at higher command velocities during deployment. One reason is that most curricula vary difficulty along single axis, for example increase the range of command velocities, terrain difficulty, or domain parameters (e.g. friction or …

Social-LLaVA: Enhancing Social Robot Navigation through Human-Language Reasoning

Amirreza Payandeh, Daeun Song, Mohammad Nazeri, Jing Liang et autres

As mobile robots become increasingly common in human-centric environments, social navigation—adhering to unwritten social norms rather than merely avoiding pedestrians—has drawn growing attention. Existing methods, from hand-crafted techniques to learning-based approaches, often overlook the nuanced context and scene understanding that humans naturally …

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Human-Robot Co-Transportation using Disturbance-Aware MPC with Pose Optimization

Al Jaber Mahmud, Amir Hossain Raj, Duc Minh Nguyen, Weizi Li et autres

This paper proposes a new control algorithm for human-robot co-transportation using a robot manipulator equipped with a mobile base and a robotic arm. We integrate the regular Model Predictive Control (MPC) with a novel pose optimization mechanism to more efficiently mitigate disturbances …

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Enhancing Issue Labeling in Open-source Projects

Amir Hossain Raj, Sadia Afrin Mim, Fairuz Nawer Meem

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DARC: Disturbance-Aware Redundant Control for Human–Robot Co-Transportation

Al Jaber Mahmud, Amir Hossain Raj, Duc Minh Nguyen, Xuesu Xiao et autres

This paper introduces Disturbance-Aware Redundant Control (DARC), a control framework addressing the challenge of human–robot co-transportation under disturbances. Our method integrates a disturbance-aware Model Predictive Control (MPC) framework with a proactive pose optimization mechanism. The robotic system, comprising a mobile base and …

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HACL: History-Aware Curriculum Learning for Fast Locomotion

Prakhar Mishra, Amir Hossain Raj, Xuesu Xiao, Dinesh Manocha

We address the problem of agile and rapid locomotion, a key characteristic of quadrupedal and bipedal robots. We present a new algorithm that maintains stability and generates high-speed trajectories by considering the temporal aspect of locomotion. Our formulation takes into account past …

McARL:Morphology-Control-Aware Reinforcement Learning for Generalizable Quadrupedal Locomotion

Prakhar Mishra, Amir Hossain Raj, Xuesu Xiao, Dinesh Manocha

We present Morphology-Control-Aware Reinforcement Learning (McARL), a new approach to overcome challenges of hyperparameter tuning and transfer loss, enabling generalizable locomotion across robot morphologies. We use a morphology-conditioned policy by incorporating a randomized morphology vector, sampled from a defined morphology range, into …