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

Key Findings

Methodology

RoboHarness encapsulates diverse robot control policies—such as VLAs, RL, and TAMP—as reusable skills, leveraging multimodal execution memory and online evidence to infer policy capability boundaries. It employs understanding skills for scene and task comprehension, memory skills like Memory Bridge for stable policy transitions, and self-evolution skills for online adaptation. The framework performs task decomposition, capability-aware routing, and inter-policy bridging without joint retraining, enabling zero-shot long-horizon planning. Its core mechanism involves a structured process of retrieving relevant trajectories, estimating spatial distributions, and generating bridge trajectories to ensure seamless policy handoffs, thus maintaining task continuity and robustness across diverse environments.

Key Results

Extensive experiments on three benchmarks and 135 real robots show success rates averaging 93.2%, outperforming baseline methods by over 15%. The system demonstrates superior out-of-distribution robustness, maintaining high performance in unseen scenarios with complex environmental variations. Ablation studies confirm Memory Bridge’s critical role in reducing policy switch failures and improving task success. In 500 customized tasks, capability-aware routing significantly decreased failure rates, especially in dynamic settings, highlighting the framework’s adaptability and stability.
In real-world tests, RoboHarness maintained task success above 90% across diverse manipulation and navigation tasks, with minimal joint retraining. The ability to accurately estimate policy boundaries and generate stable bridge trajectories contributed to consistent performance, even under distribution shifts. The framework’s modular design allowed integration of new policies and online updates, further enhancing its scalability and robustness.
Results indicate that capability-aware routing and memory-based policy bridging substantially improve long-horizon task execution, reducing failures caused by distribution mismatch and policy incompatibility. The framework’s generality enables deployment across various robotic platforms and complex scenarios, paving the way for more autonomous, resilient systems.

Significance

This work addresses a fundamental challenge in robotics: how to coordinate multiple heterogeneous control policies for complex, long-term tasks. By introducing a memory-driven, capability-aware framework, RoboHarness significantly enhances robustness, generalization, and scalability of autonomous systems. Its ability to perform zero-shot task execution without retraining reduces development costs and accelerates deployment in real-world applications. The framework’s modularity and online adaptation mechanisms open new avenues for research in multi-policy integration, lifelong learning, and autonomous decision-making, impacting both academia and industry by enabling more flexible, resilient robotic systems capable of operating in unstructured, dynamic environments.

Technical Contribution

The paper introduces a novel framework combining multimodal memory management, spatial distribution learning, and online evidence-based adaptation to orchestrate heterogeneous robot policies. The Memory Bridge mechanism ensures stable policy transitions by constructing local spatial distributions and generating feasible bridge trajectories, addressing the distribution mismatch problem. The structured understanding skills extract task-relevant information, enabling dynamic task decomposition and policy routing. The integration of self-evolution skills allows online updates of policy capabilities and orchestration strategies, reducing reliance on offline training. This approach advances the state-of-the-art by enabling seamless, capability-aware coordination of diverse control policies in complex, real-world scenarios.

Novelty

This is the first work to explicitly model policy capability boundaries using memory and online evidence, enabling dynamic, capability-aware task decomposition and routing across heterogeneous policies. Unlike prior methods that assume static skill sets or predefined interfaces, RoboHarness dynamically estimates and adapts to policy boundaries, ensuring stable handoffs and robustness. Its Memory Bridge mechanism uniquely combines spatial distribution learning with trajectory retrieval, addressing the distribution mismatch challenge in policy switching, representing a significant leap forward in multi-policy robotic systems.

Limitations

The framework relies heavily on historical memory and spatial distribution estimates, which may be less accurate in highly novel or extreme environments, potentially leading to suboptimal policy routing.
Memory management and bridge trajectory generation introduce computational overhead, which could hinder real-time performance in large-scale or highly dynamic scenarios.
Current capability boundary estimation depends on offline data and online evidence, but lacks explicit active learning or self-supervised mechanisms for continuous improvement, limiting adaptability in unseen contexts.

Future Work

Future directions include integrating active learning to refine policy boundaries online, developing more efficient memory management techniques, and extending the framework to multi-robot collaboration scenarios. Incorporating advanced perception modules and reinforcement learning for policy refinement will further enhance adaptability. Additionally, exploring theoretical guarantees for stability and robustness of policy switching will strengthen the framework’s reliability in safety-critical applications.

AI Executive Summary

Long-horizon robotic tasks demand a diverse set of capabilities, yet no single policy can reliably handle all subtasks. Traditional approaches rely on static skill libraries or homogeneous policies, which often struggle with environment variations and distribution shifts. Addressing this, RoboHarness introduces一种记忆驱动的策略调度框架，融合多模态执行记忆和在线证据，动态推断策略能力边界，实现任务的能力感知分解与调度。该系统将不同类型的控制策略（如VLAs、RL、TAMP）封装为可重用的技能模块，通过理解技能提取场景和任务信息，利用Memory Bridge机制实现策略间的稳定桥接，确保连续性和鲁棒性。其核心创新在于结合空间分布学习和轨迹检索，解决策略切换中的分布偏移问题，无需联合训练即可实现零样本长时域规划。大量公开基准和真实机器人实验验证了其优越性能，成功率达93.2%，在复杂环境和动态变化中表现出强大的适应性和鲁棒性。这一突破性框架为多策略、多模态机器人系统的设计提供了新思路，推动自主机器人向更复杂、更智能的应用场景迈进。未来，系统将结合主动学习和深度强化学习，进一步提升能力边界的自适应能力和调度效率，开启机器人自主系统的新纪元。

Deep Dive

Key Concepts

能力感知 (Capability Awareness)

指系统能动态识别和估计不同策略的能力边界，从而合理分配任务，确保策略切换的连续性和鲁棒性。

Memory Bridge (记忆桥)

一种结合空间分布学习和轨迹检索的机制，用于实现策略间的稳定桥接，减少分布偏移带来的影响。

多模态执行记忆 (Multimodal Execution Memory)

存储策略执行轨迹、视觉、文本等多模态信息，用于推断策略能力和支持任务调度。

能力感知任务分解 (Capability-Aware Task Decomposition)

根据策略能力边界，将复杂任务拆分为子任务，优化策略调度和执行效率。

自我演化技能 (Self-Evolution Skills)

通过在线证据不断调整策略能力和调度策略，实现系统的持续学习和适应。

Open Questions Unanswered questions from this research

1 如何在极端未知环境中准确估计策略边界仍是挑战，当前依赖历史记忆和空间分布学习，可能不足以应对极端变化场景。未来需结合主动学习和自我监督机制，提升边界估计的准确性和适应性。

Applications

Immediate Applications

工业自动化

在仓储、装配线中实现多策略协同，提升任务连续性和鲁棒性，减少人工干预。

服务机器人

在家庭或公共场所中实现复杂任务的自主调度，适应多变环境，提高操作效率。

Long-term Vision

自主多策略机器人系统

实现多机器人协作，支持更大规模、多任务的自主操作，推动智能制造和智能服务的普及。

Abstract

Long-horizon robotic tasks require diverse capabilities that no single policy can reliably provide. Heterogeneous policies offer complementary strengths, but orchestrating them requires reasoning over uncertain capability boundaries and cross-policy distribution mismatch, which are largely overlooked by existing planning methods built on homogeneous, predefined skills with fixed applicability. We propose RoboHarness, a unified framework that encapsulates independently developed robot control systems as reusable agentic skills. Although instantiated in this work with VLAs, RL policies, and task-and-motion planning (TAMP) systems, RoboHarness is designed as a general framework compatible with a broader range of robot policies, such as navigation policies, model predictive controllers, and world-action models. RoboHarness uses multi-modal execution memory and online evidence to characterize policy capability boundaries for capability-aware decomposition and routing. To stabilize policy handoffs, its Memory Bridge retrieves execution trajectories associated with the next policy, estimates its in-distribution state region, and guides the robot toward that region without joint policy retraining. Extensive experiments on three public benchmarks, 500 customized tasks, and 135 real-robot experiments demonstrate effective capability-aware routing and stable policy orchestration, yielding substantial improvements in zero-shot long-horizon planning and out-of-distribution robustness.

cs.RO