Amine Elhafsi

Amine Elhafsi


Amine is a Ph.D. student in the Department of Aeronautics and Astronautics. He recently obtained his M.S. degree from Stanford in 2019. Prior to joining Stanford, Amine graduated summa cum laude with a B.S. in aerospace engineering from UCLA. Before turning to robotics, he has also held multiple internships with the electric propulsion group at JPL.

Amine’s research interests include motion planning, optimal control, machine learning, and robotics. His current work is focused on developing algorithms for safe robotic navigation of unknown or partially occluded environments.

Outside of the lab, Amine enjoys playing piano, soccer, and struggling to lift weights at the gym.

Awards:

  • UCLA Outstanding Bachelor of Science in Aerospace Engineering, 2016
  • NASA Space Technology Research Fellowship, 2019

ASL Publications

  1. A. Elhafsi, B. Ivanovic, L. Janson, and M. Pavone, “Map-Predictive Motion Planning in Unknown Environments,” in Proc. IEEE Conf. on Robotics and Automation, Paris, France, 2020. (In Press)

    Abstract: Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods generally navigate unknown environments by relying on heuristic methods to choose intermediate objectives along frontiers. We present a unified method that combines map prediction and motion planning for safe, time-efficient autonomous navigation of unknown environments by dynamically-constrained robots. We propose a data-driven method for predicting the map of the unobserved environment, using the robot’s observations of its surroundings as context. These map predictions are then used to plan trajectories from the robot’s position to the goal without requiring frontier selection. We demonstrate that our map-predictive motion planning strategy yields a substantial improvement in trajectory time over a naive frontier pursuit method and demonstrates similar performance to methods using more sophisticated frontier selection heuristics with significantly shorter computation time.

    @inproceedings{ElhafsiIvanovicEtAl2020,
      author = {Elhafsi, A. and Ivanovic, B. and Janson, L. and Pavone, M.},
      title = {Map-Predictive Motion Planning in Unknown Environments},
      booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
      year = {2020},
      note = {In Press},
      address = {Paris, France},
      month = jun,
      url = {https://arxiv.org/abs/1910.08184},
      keywords = {press},
      owner = {borisi},
      timestamp = {2019-10-21}
    }
    
  2. S. Chinchali, A. Sharma, J. Harrison, A. Elhafsi, D. Kang, E. Pergament, E. Cidon, S. Katti, and M. Pavone, “Network Offloading Policies for Cloud Robotics: a Learning-based Approach,” in Robotics: Science and Systems, Freiburg im Breisgau, Germany, 2019.

    Abstract: Today’s robotic systems are increasingly turning to computationally expensive models such as deep neural networks (DNNs) for tasks like localization, perception, planning, and object detection. However, resource-constrained robots, like low-power drones, often have insufficient on-board compute resources or power reserves to scalably run the most accurate, state-of-the art neural network compute models. Cloud robotics allows mobile robots the benefit of offloading compute to centralized servers if they are uncertain locally or want to run more accurate, compute-intensive models. However, cloud robotics comes with a key, often understated cost: communicating with the cloud over congested wireless networks may result in latency or loss of data. In fact, sending high data-rate video or LIDAR from multiple robots over congested networks can lead to prohibitive delay for real-time applications, which we measure experimentally. In this paper, we formulate a novel Robot Offloading Problem - how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication? We formulate offloading as a sequential decision making problem for robots, and propose a solution using deep reinforcement learning. In both simulations and hardware experiments using state-of-the art vision DNNs, our offloading strategy improves vision task performance by between 1.3-2.6x of benchmark offloading strategies, allowing robots the potential to significantly transcend their on-board sensing accuracy but with limited cost of cloud communication.

    @inproceedings{ChinchaliSharmaEtAl2019,
      author = {Chinchali, S. and Sharma, A. and Harrison, J. and Elhafsi, A. and Kang, D. and Pergament, E. and Cidon, E. and Katti, S. and Pavone, M.},
      title = {Network Offloading Policies for Cloud Robotics: a Learning-based Approach},
      booktitle = {{Robotics: Science and Systems}},
      year = {2019},
      address = {Freiburg im Breisgau, Germany},
      month = jun,
      url = {https://arxiv.org/pdf/1902.05703.pdf},
      owner = {apoorva},
      timestamp = {2019-02-07}
    }