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Edwin Babaians

5 accepted papers

2023

SRI-Graph: A Novel Scene-Robot Interaction Graph for Robust Scene Understanding

ICRA 2023poster

We propose a novel scene-robot interaction graph (SRI-Graph) that exploits the known position of a mobile manipulator for robust and accurate scene understanding. Compared to the state-of-the-art scene graph approaches, the proposed SRI-Graph captures not only the relationships between the objects,…

Cited by 5SourceScholar
2022

PourNet: Robust Robotic Pouring Through Curriculum and Curiosity-based Reinforcement Learning

IROS 2022poster

Pouring liquids accurately into containers is one of the most challenging tasks for robots as they are unaware of the complex fluid dynamics and the behavior of liquids when pouring. Therefore, it is not possible to formulate a generic pouring policy for real-time applications. In this paper, we pro…

Cited by 10SourcecodeScholar
2022

Skill-CPD: Real-time Skill Refinement for Shared Autonomy in Manipulator Teleoperation

IROS 2022

Advanced wireless communication networks provide lower latency and a higher transmission rate. Although this is an enabler for many new teleoperation applications, the risk of network instability or packet drop is still unavoidable. Real-time manipulator teleoperation requires data transmission with

Cited by 8SourcecodeScholar
2021

LoLa-SLAM: Low-Latency LiDAR SLAM Using Continuous Scan Slicing

RA-L 2021

Real-time 6D pose estimation is a key component for autonomous indoor navigation of Unmanned Aerial Vehicles (UAVs). This letter presents a low-latency LiDAR SLAM framework based on LiDAR scan slicing and concurrent matching, called LoLa-SLAM. Our framework uses sliced point cloud data from a rotati

Cited by 50SourceScholar
2021

NMPC-MP: Real-time Nonlinear Model Predictive Control for Safe Motion Planning in Manipulator Teleoperation

IROS 2021poster

Motion control and planning for the manipulator are critical components in manipulator teleoperation. Online (real-time) motion control is challenging for active obstacle avoidance and often results in fluctuating and unsafe motion. Offline motion planning, on the other hand, generates precise and s…

Cited by 20SourceScholar