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In this work, we focus on addressing the long-horizon manipulation tasks indensely cluttered scenes. Such tasks require policies to effectively managesevere occlusions among objects and continually produce actions based on visualobservations. We propose a vision-based Hierarchical policy for Cluttered-sceneLong-horizon Manipulation (HCLM). It employs a high-level policy and threeoptions to select and instantiate three parameterized action primitives: push,pick, and place. We first train the pick and place options by behavior cloning(BC). Subsequently, we use hierarchical reinforcement learning (HRL) to trainthe high-level policy and push option. During HRL, we propose a SpatiallyExtended Q-update (SEQ) to augment the updates for the push option and aTwo-Stage Update Scheme (TSUS) to alleviate the non-stationary transitionproblem in updating the high-level policy. We demonstrate that HCLMsignificantly outperforms baseline methods in terms of success rate andefficiency in diverse tasks. We also highlight our method's ability togeneralize to more cluttered environments with more additional blocks.

Hierarchical Visual Policy Learning for Long-Horizon Robot Manipulation in Densely Cluttered Scenes