Shafeef Omar

2026

1. 

   DynaRetarget: Dynamically-Feasible Retargeting using Sampling-Based Trajectory Optimization

   Victor Dhedin, Ilyass Taouil, Shafeef Omar, Dian Yu, Kun Tao, Angela Dai, and Majid Khadiv

   arXiv preprint arXiv:2602.06827, 2026

   Abs arXiv Bib Website Video

   DynaRetarget: Dynamically-Feasible Retargeting using Sampling-Based Trajectory Optimization.

   @article{dhedin26dynaretarget,
     author = {Dhedin, Victor and Taouil, Ilyass and Omar, Shafeef and Yu, Dian and Tao, Kun and Dai, Angela and Khadiv, Majid},
     title = {DynaRetarget: Dynamically-Feasible Retargeting using Sampling-Based Trajectory Optimization},
     journal = {arXiv preprint arXiv:2602.06827},
     year = {2026},
   }

2. 

   Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning

   Shafeef Omar, and Majid Khadiv

   In Proceedings of The 8th Annual Learning for Dynamics & Control Conference (L4DC), 2026

   Abs arXiv Bib Video

   Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning.

   @inproceedings{omar25contact,
     author = {Omar, Shafeef and Khadiv, Majid},
     title = {Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning},
     booktitle = {Proceedings of The 8th Annual Learning for Dynamics \& Control Conference (L4DC)},
     series = {Proceedings of Machine Learning Research},
     publisher = {PMLR},
     year = {2026},
   }

2023

1. 

   SafeSteps: Learning Safer Footstep Planning Policies for Legged Robots via Model-Based Priors

   Shafeef Omar, Lorenzo Amatucci, Giulio Turrisi, Victor Barasuol, and Claudio Semini

   In IEEE-RAS International Conference on Humanoid Robots, 2023

   Abs arXiv Bib PDF Website

   We present a footstep planning policy for quadrupedal locomotion that is able to directly take into consideration a-priori safety information in its decisions. At its core, a learning process analyzes terrain patches, classifying each landing location by its kinematic feasibility, shin collision, and terrain roughness. This information is then encoded into a small vector representation and passed as an additional state to the footstep planning policy, which furthermore proposes only safe footstep location by applying a masked variant of the Proximal Policy Optimization (PPO) algorithm [1]. The performance of the proposed approach is shown by comparative simulations on an electric quadruped robot walking in different rough terrain scenarios. We show that violations of the above safety conditions are greatly reduced both during training and the successive deployment of the policy, resulting in an inherently safer footstep planner. Furthermore, we show how, as a byproduct, fewer reward terms are needed to shape the behavior of the policy, which in return is able to achieve both better final performances and sample efficiency.

   @inproceedings{omar23humanoids,
     author = {Omar, Shafeef and Amatucci, Lorenzo and Turrisi, Giulio and Barasuol, Victor and Semini, Claudio},
     title = {SafeSteps: Learning Safer Footstep Planning Policies for Legged Robots via Model-Based Priors},
     booktitle = {IEEE-RAS International Conference on Humanoid Robots},
     year = {2023},
   }