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LEAN: Reinforcement Learning-Based Energy-Aware Navigation Planner Released

The authors have released LEAN, a reinforcement learning-based local energy-aware navigation planner. The code is open-sourced on GitHub, and the paper was published in IEEE Robotics and Automation Letters. The authors state that the method does not require using proximity to the goal as a reward; instead, it lets the agent learn from failed trajectories and provides an explicit energy signal. An accompanying video compares the model's inference performance in real environments against an ablation version without energy awareness and a classical NMPC planner.

Part of the event: LEAN Energy-Aware Navigation Planner Paper and Code Released

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