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Mohsen Sombolestan

PhD Candidate in Robotics

University of Southern California

I am a PhD candidate in Robotics at the University of Southern California (USC), where I am advised by Prof. Quan Nguyen. My research focuses on advancing legged locomotion and manipulation in uncertain environments, leveraging model predictive control (MPC) and adaptive control techniques.

Previously, I was a research intern at Field AI, where I worked on addressing vision-based locomotion challenges for quadruped robots navigating complex terrains, such as those found on construction sites.

Interests

  • Legged Locomotion and Manipulation
  • Motion Planning
  • Model Predictive Control
  • Machine Learning

Education

  • University of Southern California

    PhD in Mechanical Engineering, Robotics and Control

  • Isfahan University of Technology

    MSc in Mechanical Engineering, Robotics and Control

  • Sharif University of Technology

    BSc in Mechanical Engineering

News

Publications

Hierarchical Adaptive Motion Planning with Nonlinear Model Predictive Control for Safety-Critical Collaborative Loco-Manipulation

Preprint, 2024, submitted to The International Journal of Robotics Research (IJRR)

Adaptive Force-Based Control of Dynamic Legged Locomotion over Uneven Terrain

IEEE Transactions on Robotics (T-RO), 2024

Hierarchical Adaptive Control for Collaborative Manipulation of a Rigid Object by Quadrupedal Robots

IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 2023

Hierarchical Adaptive Loco-manipulation Control for Quadruped Robots

IEEE/RSJ International Conference on Robotics and Automation, ICRA, 2023

Adaptive Force-based Control for Legged Robots

IEEE/RSJ International Conference on Robotics and Automation, IROS, 2021

Optimal path-planning for mobile robots to find a hidden target in an unknown environment based on machine learning

Journal of Ambient Intelligence and Humanized Computing, 2018

Ongoing Projects

Adaptive Loco-Manipulation Control for Humanoid Robots

Expanding our methodology to empower humanoid robots (such as HECTOR) with capabilities for performing loco-manipulation tasks and perceptive locomotion across diverse and challenging terrains.

Adaptive Sampling-based Model Predictive Control for Bipedal Locomotion

Exploring the potential of adaptive sampling-based MPC for bipedal robots to address the challenges of real-time planning and control in short-horizon agile movements and disturbance rejection under model uncertainty.