RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning

Key points

• Instead of solving long tasks with a single policy, we reformulate the problem as coordinating heterogeneous policies with different capability boundaries.
• Existing planning methods assume homogeneous skills with fixed, non-overlapping capabilities, but we point out that the capability boundaries of real policies change with context.
• We use three groups of skills, understanding, memory, and self-evolution, to handle context interpretation, capability-boundary estimation, and experience-based correction during execution.
• The memory bridge retrieves trajectories associated with the next policy, estimates that policy's normal input region, and guides the robot into that region to make handoff stable without retraining.
• In ablation tests on 500 long-horizon tasks, the full system reaches 86.0% success, removing the memory bridge drops it to 60.4%, removing understanding skills drops it to 37.6%, and using only a single policy yields 0%.
• In 135 real-robot trials, success drops from 86.7% to 66.7% under block re-obscuration perturbations, but remains 80.0% even after partial dismantling of the structure.

Paper links

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