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arXiv · 2607.17651

HCPG-Flow:Hierarchical Contact-Progress Guidance for Flow-Policy Robot Manipulation

Guanghu Xie·Mingxu Li·Shuo Zhang·Yonglong Zhang·Yifan Yang·Yang Liu·Zongwu Xie·Baoshi Cao
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Abstract

Flow policies can represent multimodal action distributions for robot manipulation, yet a robot must execute one action at each control step. When several proposals are sampled, critic-based ranking makes data collection depend on value estimates over candidate actions that may be weakly represented in replay. We introduce HCPG-Flow, an analytic rollout-time selector that augments SAC-Flow with hierarchical, object-centric contact-progress guidance while preserving its actor and critic objectives. HCPG switches from end-effector approach to task progress after contact, scores each proposal by the first-order reduction of a task-relevant distance, standardizes scores within the candidate set, and executes a temperature-controlled action embedding. Across ten simulated tasks, HCPG improves mean success over SAC-Flow on both benchmarks, including a 9.5 percentage-point gain on Maniskill. Four physical tasks further show high success with a 17.4% reduction in successful completion steps.Project page: https://hitxraz.github.io/HCPG-Flow/

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Guanghu Xie, Mingxu Li, Shuo Zhang, Yonglong Zhang, Yifan Yang, Yang Liu, Zongwu Xie, Baoshi Cao. 2026-07-20. HCPG-Flow:Hierarchical Contact-Progress Guidance for Flow-Policy Robot Manipulation. https://arxiv.org/abs/2607.17651

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