RISE: Self-Improving Robot Policy with Compositional World Model

• URL: http://arxiv.org/abs/2602.11075v1
• Date: Wed, 11 Feb 2026 17:43:36 GMT
• Title: RISE: Self-Improving Robot Policy with Compositional World Model
• Abstract summary: We present RISE, a scalable framework of robotic reinforcement learning via imagination.<n>At its core is a Compositional World Model that predicts multi-view future via a controllable dynamics model, and (ii) evaluates imagined outcomes with a progress value model.<n>These components are integrated into a closed-loop self-improving pipeline that continuously generates imaginary rollouts, estimates advantages, and updates the policy in imaginary space without costly physical interaction.
• Score: 52.227523057681786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the sustained scaling on model capacity and data acquisition, Vision-Language-Action (VLA) models remain brittle in contact-rich and dynamic manipulation tasks, where minor execution deviations can compound into failures. While reinforcement learning (RL) offers a principled path to robustness, on-policy RL in the physical world is constrained by safety risk, hardware cost, and environment reset. To bridge this gap, we present RISE, a scalable framework of robotic reinforcement learning via imagination. At its core is a Compositional World Model that (i) predicts multi-view future via a controllable dynamics model, and (ii) evaluates imagined outcomes with a progress value model, producing informative advantages for the policy improvement. Such compositional design allows state and value to be tailored by best-suited yet distinct architectures and objectives. These components are integrated into a closed-loop self-improving pipeline that continuously generates imaginary rollouts, estimates advantages, and updates the policy in imaginary space without costly physical interaction. Across three challenging real-world tasks, RISE yields significant improvement over prior art, with more than +35% absolute performance increase in dynamic brick sorting, +45% for backpack packing, and +35% for box closing, respectively.

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