BiPOP: Partial-Order Planning for Long-Horizon Bimanual Robotic Manipulation

Bimanual robotic manipulation is versatile, yet its central challenge lies not only in executing dexterous motion with each hand, but also in coordinating the two arms for long-horizon manipulation under coupled spatial and temporal constraints. Prior work has largely focused on endowing robots with human-level manipulation skills, paying less attention to the structure of bimanual behaviour itself: many practical tasks are a coordinated composition of reusable unimanual skills and a small set of synchronous bimanual skills. This makes high-level spatio-temporal coordination, rather than skill acquisition alone, a key bottleneck for efficient bimanual execution. We propose BiPOP, a framework that couples Large Language Models (LLMs) with Temporal Partial-Order Planning for long-horizon bimanual manipulation. At its core is an explicit spatio-temporal model that partitions the workspace by arm reachability and captures inter-arm dependencies, arm assignment, synchronization requirements, and opportunities for safe concurrent execution. Building on this model, an LLM translates natural-language instructions into a formal PDDL specification, while a temporal planner enforces the constraints and schedules actions, yielding plans that are logically consistent at the symbolic level and efficient through parallel execution. On simulated long-horizon tasks of varying complexity, BiPOP outperforms open-loop planning with strong reasoning models and closed-loop ReAct-style planning in success rate, planning time, and plan efficiency, and the full pipeline runs end to end on the real bimanual robot NICOL. A failure analysis further shows that LLM-only planners mainly violate spatial and temporal coordination constraints, which motivates delegating these constraints to a symbolic planner. Code: https://github.com/Kchu/BiPOP