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

arXiv: https://arxiv.org/abs/2607.18060 · HTML: https://mlsanity.com/paper/2607.18060/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2026.

## Built on by (most cited first, 11 of 11)

- [Faster-WAM: Do World Action Models Need Deep Action Modules?](https://mlsanity.com/paper/2608.02365/) (2026)
- [RoboRSI: Stable, efficient, and reusable robot self-evolution in complex real-world environments](https://mlsanity.com/paper/2610.12424/) (2026)
- [StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation](https://mlsanity.com/paper/2609.20791/) (2026)
- [CHOREO: Every Humanoid Skill as a Trajectory](https://mlsanity.com/paper/2609.22274/) (2026)
- [RoboFoundry: System-as-Policy Evolution for Self-Learning Embodied Agents](https://mlsanity.com/paper/2609.32862/) (2026)
- [In-Context Learning for Robots: Methods and Applications](https://mlsanity.com/paper/2609.36012/) (2026)
- [Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems](https://mlsanity.com/paper/2609.17320/) (2026)
- [MaskHarness-WAM: Instance-Grounded Harnessing for Long-Horizon Robot Manipulation](https://mlsanity.com/paper/2609.19974/) (2026)
- [CogWAM: Aligning Semantic Cognition with World Action Modeling via Event-Driven Interfaces](https://mlsanity.com/paper/2609.37721/) (2026)
- [PhysEvo: Astra Can Act, Let It](https://mlsanity.com/paper/2610.08995/) (2026)
- [RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes](https://mlsanity.com/paper/2610.11480/) (2026)

## Abstract (arXiv)

Long-horizon robotic tasks require a breadth of capabilities beyond what any single existing robot control policy can reliably provide. Combining heterogeneous policies with complementary strengths offers a promising solution, but introduces two key challenges: uncertain capability boundaries and distribution mismatches during policy handoffs. These challenges remain largely unaddressed by existing planning methods, which typically assume homogeneous, predefined skills with fixed applicability. We propose RoboHarness, a unified framework that encapsulates independently developed heterogeneous policies, including vision-language-action models (VLAs), world-action models (WAMs), reinforcement learning (RL) policies, and task and motion planners (TAMP), as reusable agentic skills. RoboHarness integrates understanding, memory, and evolution skills to reason about policy capabilities and support capability-aware task decomposition and policy routing. To mitigate distribution mismatches during policy handoffs, we introduce Memory Bridge, a plug-in policy-chaining mechanism that enables reliable transitions between heterogeneous policies without joint retraining. Extensive experiments across five public benchmarks, 500 customized tasks across 10 classes, and 135 real-robot trials demonstrate substantial gains in long-horizon and memory-dependent tasks, as well as robustness to out-of-distribution conditions.