RISE
RISE: Self-Improving Robot Policy with Compositional World Model
RSS 2026
The first study on leveraging world models as an effective learning environment for challenging real-world manipulation, bootstrapping performance on tasks requiring high dynamics, dexterity, and precision.
Jiazhi Yang1,2*† Kunyang Lin2* Jinwei Li2,6* Wencong Zhang2* Tianwei Lin5 Longyan Wu4 Zhizhong Su5 Hao Zhao6 Ya-Qin Zhang6 Li Chen3 Ping Luo3 Xiangyu Yue1♮ Hongyang Li3♮
1 The Chinese University of Hong Kong 2 Kinetix AI 3 The University of Hong Kong
4 Shanghai Innovation Institute 5 Horizon Robotics 6 Tsinghua University
* Equal Contribution † Project Lead ♮ Equal Advising
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At a Glance

System Overview

RISE shifts the RL environment to a Compositional World Model, which first emulates future observations for proposed actions, then evaluates imagined states to derive advantage for policy improvement.

Contributions

①
RISE, a principled framework for robotic reinforcement learning, that enables autonomous self-improvement in a scalable and online manner. RISE overcomes the physical restrictions in prior arts by shifting robotic interactions from physical environment to imaginative space.
②
Compositional World Model, an online learning environment that builds reliable dynamics and value estimates for real-world tasks. We unveil critical design choices to derive informative learning signals for policy improvement.
③
Through extensive experiments on dexterous manipulation tasks, we demonstrate that RISE exhibits significantly higher performance compared to existing RL methods.

Real-World Autonomous Execution

Continuous Success achieved by RISE

Autonomous execution on three complex tasks.

Comparison: RISE vs. Baselines

Left: RISE (Ours). Right: Baselines (Composite video including RECAP, π0.5, π0.5+DSRL).

RISE (Backpack Packing)
Baselines (Backpack Packing)
RISE (Dynamic Brick Sorting)
Baselines (Dynamic Brick Sorting)
RISE (Box Closing)
Baselines (Box Closing)

Robustness & Generalization

Self-Correction & Robustness
Spatial Generalization

Methodology

Key Component#1: Compositional World Model

Inference pipeline that yields rewarded samples for policy optimization. Both modules are compatible with multi-view images.

Key Component#2: Self-Improving Loop

Top: Rollout stage. Prompted with an optimal advantage, the rollout policy interacts with the world model to produce rollout data.
Bottom: Training stage. The behavior policy is then trained to generate proper action under an advantage-conditioning scheme.

Results

Main Results

RISE outperforms all baselines with +35% on brick sorting, +45% on backpack packing, and +35% on box closing through world model imagination.

Metric:

Dynamic Brick Sorting: Success (%)

Backpack Packing: Success (%)

Box Closing: Success (%)

Ablations

Ablation studies conducted on the Dynamic Brick Sorting task.

Left: Optimal offline ratio (0.6) balances demonstrations with online rollouts.
Right: Both online actions and states are essential; full RISE with all components achieves best performance.

Ablation: Offline data ratio

Ablation: Online action/state integration

Sort Accuracy
Pick&Place Success Rate
Complete Success Rate

Dynamics Model Comparisons

Qualitative comparison: controllable future prediction under candidate actions.

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.

BibTeX

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@article{rise2026,
  title={RISE: Self-Improving Robot Policy with Compositional World Model},
  author={Yang, Jiazhi and Lin, Kunyang and Li, Jinwei and Zhang, Wencong and Lin, Tianwei and Wu, Longyan and Su, Zhizhong and Zhao, Hao and Zhang, Ya-Qin and Chen, Li and Luo, Ping and Yue, Xiangyu and Li, Hongyang},
  journal={arXiv preprint arXiv:2602.11075},
  year={2026}
}