Dream-MPC refines policy-generated trajectories by gradient ascent in a latent world model with uncertainty regularization and temporal amortization, improving base policy performance and beating gradient-free MPC on 24 continuous control tasks.
Td-m(pc) 2: Improving temporal difference mpc through policy constraint
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
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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.
citing papers explorer
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Dream-MPC: Gradient-Based Model Predictive Control with Latent Imagination
Dream-MPC refines policy-generated trajectories by gradient ascent in a latent world model with uncertainty regularization and temporal amortization, improving base policy performance and beating gradient-free MPC on 24 continuous control tasks.
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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.