Constrained Differentiable Policy Learning for Safe Quadrotor Navigation
Overview
Research introduces constrained differentiable policy learning for safe quadrotor navigation, improving safety-performance trade-offs and generalization to out-of-distribution environments. Key components include bounded clearance-based constraints and log-mean-exp aggregation. Experiments show zero-shot transfer to simulation and physical quadrotors. arXiv:2610.16119v1 Latest: arXiv:2610.16119v1 reports improved safety-performance trade-offs and generalization to denser out-of-distribution environments, with zero-shot transfer to both simulation and physical quadrotors. The method uses bounded clearance-based constraints and log-mean-exp aggregation for constrained training. Theoretical guarantees and experiments demonstrate enhanced safety and performance. arXiv:2610.16119v1
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- arXiv Robotics — research abstractsConstrained Differentiable Policy Learning for Safe Quadrotor Navigation
This research presents a method for safe quadrotor navigation using constrained differentiable policy learning. The approach formulates obstacle avoidance as an explicit constraint and employs primal-dual gradient descent-ascent through a differentiable simulator. Two key components—bounded clearance-based constraints and log-mean-exp aggregation—enable effective constrained training. Theoretical guarantees and experiments demonstrate improved safety-performance trade-offs and generalization to out-of-distribution environments, with zero-shot transfer to both simulation and physical quadrotors.
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