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This paper proposes Cascaded Skills Optimization, a two-stage post-training method for discrete-skill robot policies that combines rejection-sampling fine-tuning with a reinforcement learning step that uses per-skill importance ratios. The reported gains on LIBERO and RoboTwin2.0 are substantial, but they come from simulation benchmarks and a lightweight policy, and the real-robot result (61.0% across five single-arm tasks) is more modest. Readers should weigh the method's stability claims against how well it transfers beyond these settings.
CSO: Refining Robotic Policies via Skill Distribution Alignment and Skill-Grained Optimization
arXiv:2610.38357v1 Announce Type: cross Abstract: Discretizing continuous actions into discrete skills using methods like VQ-VAE has emerged as a powerful paradigm for robotic manipulation. However, quantization errors yield a suboptimal distribution for the skill prior, degrading its performance. While reinforcement learning offers a path for refinement, its direct application faces unstable encoder updates and a granularity dilemma in importance sampling. To address these challenges, we introduce Cascaded Skills Optimization (CSO), a two-stage post-training framework. First, Rejection-Sampling Supervised Fine-tuning aligns the model's observation-to-skill mapping with the distribution of successful online trajectories via supervised fine-tuning. Second, with the observation encoder frozen, Skills Policy Optimization computes an independent, clipped importance ratio for each complete skill, enabling stable and efficient updates. With a lightweight QueST-style policy, CSO raises overall LIBERO success from 80.1% to 97.5%, improves performance on RoboTwin2.0, and achieves 61.0% success across five physical single-arm tasks. Additional comparisons with FAST, discrete BEAST, and fixed-buffer IQL further validate the effectiveness of our two-stage post-training strategy.
Abstract:Discretizing continuous actions into discrete skills using methods like VQ-VAE has emerged as a powerful paradigm for robotic manipulation. However, quantization errors yield a suboptimal distribution for the skill prior, degrading its performance. While reinforcement learning offers a path for refinement, its direct application faces unstable encoder updates and a granularity dilemma in importance sampling. To address these challenges, we introduce Cascaded Skills Optimization (CSO), a two-stage post-training framework. First, Rejection-Sampling Supervised Fine-tuning aligns the model's observation-to-skill mapping with the distribution of successful online trajectories via supervised fine-tuning. Second, with the observation encoder frozen, Skills Policy Optimization computes an independent, clipped importance ratio for each complete skill, enabling stable and efficient updates. With a lightweight QueST-style policy, CSO raises overall LIBERO success from 80.1% to 97.5%, improves performance on RoboTwin2.0, and achieves 61.0% success across five physical single-arm tasks. Additional comparisons with FAST, discrete BEAST, and fixed-buffer IQL further validate the effectiveness of our two-stage post-training strategy.
Accepted at NeurIPS 2026. 31 pages, including appendices and checklist
Robotics (cs.RO); Machine Learning (cs.LG)
arXiv:2610.38357 [cs.RO]
(or arXiv:2610.38357v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.38357 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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