RAY-TOLD combines ray-based latent dynamics from LiDAR with MPPI control and a learned policy prior via mixture sampling to lower collision rates in high-density dynamic obstacle environments compared to standard MPPI.
Dr-mpc: Deep residual model predictive control for real-world social navigation,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Integrates RL with a differentiable CVaR quadratic-program safety layer to jointly learn nominal controls, risk levels, and margins for adaptive safe navigation under motion uncertainty.
citing papers explorer
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RAY-TOLD: Ray-Based Latent Dynamics for Dense Dynamic Obstacle Avoidance with TDMPC
RAY-TOLD combines ray-based latent dynamics from LiDAR with MPPI control and a learned policy prior via mixture sampling to lower collision rates in high-density dynamic obstacle environments compared to standard MPPI.
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Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions
Integrates RL with a differentiable CVaR quadratic-program safety layer to jointly learn nominal controls, risk levels, and margins for adaptive safe navigation under motion uncertainty.