Bilal Hammoud

I am a PhD student in the Machines in Motion Lab working under the supervision of Prof. Ludovic Righetti at the Tandon School of engineering, NYU. I am also a member of the Movement Generation and Control Group at the Max Planck Institute for Intelligent Systems in Tübingen, Germany . Prior to joining NYU I received my Masters degree in Mechanical Engineering from the American University of Beirut where I worked on optimal control for autonomous agents under the supervision of Prof. Elie Shammas. I am mainly interested in utilizing advanced mathematical tools such as stochastic optimal control, game theory and reinforcement learning to solve decision making problems under uncertainty. One main application I am interested in is dynamic legged robots creating and breaking contacts with the enviroment.

selected publications

Impedance optimization for uncertain contact interactions through risk sensitive optimal control

Bilal Hammoud, Majid Khadiv, and Ludovic Righetti

IEEE Robotics and Automation Letters 2021

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This letter addresses the problem of computing optimal impedance schedules for legged locomotion tasks involving complex contact interactions. We formulate the problem of impedance regulation as a trade-off between disturbance rejection and measurement uncertainty. We extend a stochastic optimal control algorithm known as Risk Sensitive Control to take into account measurement uncertainty and propose a formal way to include such uncertainty for unknown contact locations. The approach can efficiently generate optimal state and control trajectories along with local feedback control gains, i.e. impedance schedules. Extensive simulations demonstrate the capabilities of the approach in generating meaningful stiffness and damping modulation patterns before and after contact interaction. For example, contact forces are reduced during early contacts, damping increases to anticipate a high impact event and tracking is automatically traded-off for increased stability. In particular, we show a significant improvement in performance during jumping and trotting tasks with a simulated quadruped robot.
Crocoddyl: An efficient and versatile framework for multi-contact optimal control

Carlos Mastalli, Rohan Budhiraja, Wolfgang Merkt, and 7 more authors

In 2020 IEEE International Conference on Robotics and Automation (ICRA) 2020

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We introduce Crocoddyl (Contact RObot COntrol by Differential DYnamic Library), an open-source framework tailored for efficient multi-contact optimal control. Crocoddyl efficiently computes the state trajectory and the control policy for a given predefined sequence of contacts. Its efficiency is due to the use of sparse analytical derivatives, exploitation of the problem structure, and data sharing. It employs differential geometry to properly describe the state of any geometrical system, e.g. floating-base systems. Additionally, we propose a novel optimal control algorithm called Feasibility-driven Differential Dynamic Programming (FDDP). Our method does not add extra decision variables which often increases the computation time per iteration due to factorization. FDDP shows a greater globalization strategy compared to classical Differential Dynamic Programming (DDP) algorithms. Concretely, we propose two modifications to the classical DDP algorithm. First, the backward pass accepts infeasible state-control trajectories. Second, the rollout keeps the gaps open during the early “exploratory” iterations (as expected in multiple-shooting methods with only equality constraints). We showcase the performance of our framework using different tasks. With our method, we can compute highly-dynamic maneuvers (e.g. jumping, front-flip) within few milliseconds.