Beyond Policy Alignment: Closing the Planning-Learning Loop for Robot Control with Learned World Models

arXiv:2609.39751 · cs.RO · Submitted 2026-09-30 · Read on arXiv

cs.RO

Submitted: 2026-09-30

Updated: 2026-09-30

Comments: 8 pages

Project page: https://pl-mpc-humanoid.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Actor — 36× in this paper · explained in 3 episodes
• World Model — 17× in this paper · explained in 7 episodes
• Distillation — 14× in this paper · explained in 2 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Ablation — 10× in this paper · explained in 1 episode
• Benchmark — 10× in this paper · explained in 2 episodes
• Total Average Return — 9× in this paper
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Model Predictive Path Integral — 2× in this paper
• Zero-Shot Transfer — 2× in this paper · explained in 4 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Adaptive Planning — 1× in this paper · explained in 1 episode
• Additive Decomposition — 1× in this paper · explained in 1 episode
• Model Predictive Path Integral (MPPI) — 1× in this paper · explained in 3 episodes
• Online Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Q-learning — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes

Sources

• DeepMind Control Suite
• Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
• AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
• TDMPBC: Self-Imitative Reinforcement Learning for Humanoid Robot Control
• Failure Forecasting Boosts Robustness of Sim2Real Rhythmic Insertion Policies

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