I received an MASc (=MSc) in 2021. In 2019, was the deputy lead of aUToronto, U of T’s self driving car team, for the second stage of the SAE AutoDrive Challenge. Before joining UTIAS, I completed my undergrad in Engineering Science(Robotics) at the University of Toronto.
By the way, my headshot has been cloaked by Fawkes.
Robots need to predict and react to human motions to navigate through a crowd without collisions. Many existing methods decouple prediction from planning, which does not account for the interaction between robot and human motions and can lead to the robot getting stuck. We propose SICNav, a Model Predictive Control (MPC) method that jointly solves for robot motion and predicted crowd motion in closed-loop. We model each human in the crowd to be following an Optimal Reciprocal Collision Avoidance (ORCA) scheme and embed that model as a constraint in the robot’s local planner, resulting in a bilevel nonlinear MPC optimization problem. We use a KKT-reformulation to cast the bilevel problem as a single level and use a nonlinear solver to optimize. Our MPC method can influence pedestrian motion while explicitly satisfying safety constraints in a single-robot multi-human environment. We analyze the performance of SICNav in two simulation environments and indoor experiments with a real robot to demonstrate safe robot motion that can influence the surrounding humans. We also validate the trajectory forecasting performance of ORCA on a human trajectory dataset.
@article{samavi2024sicnav,author={Samavi, Sepehr and Han, James R. and Shkurti, Florian and Schoellig, Angela P.},journal={IEEE Transactions on Robotics (T-RO)},title={SICNav: Safe and Interactive Crowd Navigation using Model Predictive Control and Bilevel Optimization},year={2024},volume={41},number={},pages={801-818},doi={10.1109/TRO.2024.3484634},url={https://arxiv.org/abs/2310.10982},}
SICNav-Diffusion: Safe and Interactive Crowd Navigation with Diffusion Trajectory Predictions
Sepehr Samavi, Anthony Lem , Fumiaki Sato , and 5 more authors
IEEE Robotics and Automation Letters (RA-L), in press, 2025
To navigate crowds without collisions, robots must interact with humans by forecasting their future motion and reacting accordingly. While learning-based prediction models have shown success in generating likely human trajectory predictions, integrating these stochastic models into a robot controller presents several challenges. The controller needs to account for interactive coupling between planned robot motion and human predictions while ensuring both predictions and robot actions are safe (i.e. collision-free). To address these challenges, we present a receding horizon crowd navigation method for single-robot multi-human environments. We first propose a diffusion model to generate joint trajectory predictions for all humans in the scene. We then incorporate these multi-modal predictions into a SICNav Bilevel MPC problem that simultaneously solves for a robot plan (upper-level) and acts as a safety filter to refine the predictions for non-collision (lower-level). Combining planning and prediction refinement into one bilevel problem ensures that the robot plan and human predictions are coupled. We validate the open-loop trajectory prediction performance of our diffusion model on the commonly used ETH/UCY benchmark and evaluate the closed-loop performance of our robot navigation method in simulation and extensive real-robot experiments demonstrating safe, efficient, and reactive robot motion.
@article{samavi2025sicnavdiffusion,author={Samavi, Sepehr and Lem, Anthony and Sato, Fumiaki and Chen, Sirui and Gu, Qiao and Yano, Keijiro and Schoellig, Angela P. and Shkurti, Florian},journal={IEEE Robotics and Automation Letters (RA-L), in press},title={SICNav-Diffusion: Safe and Interactive Crowd Navigation with Diffusion Trajectory Predictions},year={2025},volume={},number={},pages={},}