# Long-Horizon Robotic Manipulation

StructRL is an online reinforcement learning framework that automatically decomposes each long-horizon instruction into subtasks verifiable by binary environment-state criteria and organizes them into ordered dependency groups, granting intermediate rewards only after all prerequisites of a subtask are complete and scaling each reward by completion pace; across RoboCasa365 and LIBERO-Long with the GR00T-N1.5 and π0.5 VLA backbones it consistently outperforms the evaluated online RL baselines (e.g., on GR00T-N1.5, RoboCasa365 rises from 41.5% to 49.1% and LIBERO-Long from 92.4% to 96.6%).

StructRL is an online reinforcement learning framework that automatically decomposes each long-horizon instruction into subtasks verifiable by binary environment-state criteria and organizes them into ordered dependency groups, granting intermediate rewards only after all prerequisites of a subtask are complete and scaling each reward by completion pace; across RoboCasa365 and LIBERO-Long with the GR00T-N1.5 and π0.5 VLA backbones it consistently outperforms the evaluated online RL baselines (e.g., on GR00T-N1.5, RoboCasa365 rises from 41.5% to 49.1% and LIBERO-Long from 92.4% to 96.6%).

The work proposes Physical Coding, representing physical world state (Code as World) and execution procedure (Code as Policy) as executable programs, and builds the physical coding agent HexaAnything, which treats VLA/WAM policies as callable action tools while a Harness owns state bookkeeping, verification, and recovery; on RoboCasa365 with XR-1 as the action model it raises Composite-Unseen success from 34.3% to 38.3% and overall success from 56.6% to 61.1%, a HexaModel v0.1 fine-tuned on Harness-collected traces beats its base model on every split in the same Harness, PhyBench simulated physics experiments are completed with mean relative errors below 5%, and on a dual-arm AgileX robot five of seven tabletop tasks succeed in all three trials.

The work proposes Physical Coding, representing physical world state (Code as World) and execution procedure (Code as Policy) as executable programs, and builds the physical coding agent HexaAnything, which treats VLA/WAM policies as callable action tools while a Harness owns state bookkeeping, verification, and recovery; on RoboCasa365 with XR-1 as the action model it raises Composite-Unseen success from 34.3% to 38.3% and overall success from 56.6% to 61.1%, a HexaModel v0.1 fine-tuned on Harness-collected traces beats its base model on every split in the same Harness, PhyBench simulated physics experiments are completed with mean relative errors below 5%, and on a dual-arm AgileX robot five of seven tabletop tasks succeed in all three trials.