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Amine Elhafsi

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ICRA Conference 2020 Conference Paper

Map-Predictive Motion Planning in Unknown Environments

  • Amine Elhafsi
  • Boris Ivanovic
  • Lucas Janson
  • Marco Pavone 0001

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 au-tonomous 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 applied this map-predictive motion planning strategy to randomly generated winding hallway environments, yielding substantial improvement in trajectory duration over a naïve frontier pursuit method. We also experimentally demonstrate similar performance to methods using more sophisticated fron-tier selection heuristics while significantly reducing computation time.

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RLDM Conference 2019 Conference Abstract

Reinforcement Learning for Network Offloading in Cloud Robotics

  • Sandeep Chinchali
  • Apoorva Sharma
  • Amine Elhafsi
  • Daniel Kang
  • Evgenya Perga-
  • Eyal Cidon
  • Sachin Katti

We apply deep reinforcement learning to a central decision-making problem in robotics - when should a robot use its on-board compute model or, in cases of local uncertainty, query a compute-intensive model in “the cloud”? Today’s robotic systems are increasingly turning to computationally expensive mod- els such as deep neural networks (DNNs) for tasks like object detection, perception and planning. 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 to offload 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 un- derstated cost: communicating with the cloud over congested wireless networks may result in latency and increase network congestion. In fact, sending high data-rate video or LIDAR from multiple robots over congested networks can lead to prohibitive delay for real-time applications. 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 practical 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. We conclude by showing how cloud offloading has an inherent exploration vs. exploitation trade-off since a robot must balance use of a known local model (exploitation) with learning context-dependent utility of the cloud (exploration). Accordingly, we discuss how our model is widely applicable beyond cloud robotics.

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