Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection
Goal2Skill uses a dual-system with VLM-based planning and diffusion-based control, achieving 32.4% success in long-horizon tasks.
Key Findings
Methodology
The paper introduces a dual-system architecture: a high-level planner based on vision-language models (VLM) for semantic goal decomposition, structured memory, and reflection; and a low-level visuomotor executor utilizing diffusion models for robust action generation. These modules operate in a closed-loop, with the high-level system maintaining task memory, verifying outcomes, and adjusting plans dynamically, while the low-level controller executes sub-tasks conditioned on geometry-preserving filtered observations. Experiments on RMBench tasks show a success rate of 32.4%, significantly outperforming baselines at 9.8%. Ablation studies confirm the importance of structured memory and reflection for robustness.
Key Results
- Across five RMBench tasks, the proposed framework achieved an average success rate of 32.4%, with memory-dependent tasks reaching 38.7%, vastly surpassing previous methods. The integration of structured memory and explicit verification notably improved resilience against partial observability, occlusion, and multi-stage dependencies. Ablation experiments demonstrated that removing memory or reflection reduced success rates by approximately 15-20%, highlighting their critical roles in long-horizon manipulation.
Significance
This work advances embodied AI by effectively integrating semantic reasoning, persistent memory, and adaptive control within a modular framework. It addresses longstanding challenges in long-horizon, memory-dependent tasks, enabling robots to perform multi-stage operations with higher success and robustness. The approach bridges the gap between high-level planning and low-level control, offering a scalable solution for complex real-world applications such as autonomous service robots and industrial automation. Its modular design facilitates future extensions with online learning and real-time optimization, promising broader impact in intelligent robotics.
Technical Contribution
The paper's key innovation is the explicit decoupling of semantic planning and geometric control via a dual-system architecture. The high-level planner employs a pre-trained VLM to generate structured sub-tasks, manage memory, and perform reflection-based diagnosis. The low-level executor, based on diffusion models, translates sub-tasks into continuous motor commands conditioned on filtered observations, enhancing robustness. The system's closed-loop interaction allows for dynamic re-planning and fault recovery, setting a new standard for long-horizon embodied manipulation. This design introduces a novel integration of large-scale multimodal models with diffusion-based control in robotics.
Novelty
This is the first work to combine VLM-driven semantic goal decomposition with diffusion-based visuomotor control within a structured, closed-loop framework. Unlike prior end-to-end models, it explicitly separates high-level reasoning from low-level execution, enabling persistent memory, adaptive replanning, and explicit failure recovery. The integration of reflection mechanisms for diagnosis and correction further distinguishes this approach, offering a new paradigm for scalable, robust long-horizon manipulation.
Limitations
- The system relies heavily on large pre-trained models, which demand significant computational resources, potentially limiting real-time deployment.
- In highly dynamic or unpredictable environments, the reflection and re-planning mechanisms may face efficiency bottlenecks.
- Handling extremely long or complex tasks may require further optimization of memory management and decision cycles.
Future Work
Future research will focus on reducing computational overhead through model compression, enhancing online learning capabilities, and improving real-time performance. Incorporating reinforcement learning for policy adaptation, extending to more diverse environments, and enabling lifelong learning are promising directions to further empower autonomous long-horizon manipulation.
AI Executive Summary
This study introduces Goal2Skill, a novel dual-system framework designed to tackle the challenges of long-horizon embodied manipulation. Traditional vision-language-action (VLA) systems excel at short-term decision-making but struggle with tasks requiring persistent memory, multi-stage dependencies, and explicit error correction. To address these issues, the authors propose a hierarchical architecture that explicitly separates semantic planning from low-level motor control.
The high-level planner, built upon a pre-trained vision-language model (VLM), maintains structured task memory, performs goal decomposition, and conducts outcome verification and reflection. It dynamically revises plans based on feedback, enabling adaptive replanning and failure diagnosis. The low-level executor, based on diffusion models, translates sub-tasks into continuous motor commands, conditioned on geometry-preserving filtered observations that suppress distractors. This design ensures robust execution even in cluttered, partially observable environments.
The core innovation lies in the closed-loop interaction between the two modules, allowing the system to perform memory-aware reasoning, online recovery, and adaptive control. Extensive experiments on RMBench tasks demonstrate a success rate of 32.4%, a substantial improvement over the baseline of 9.8%. Ablation studies confirm the crucial roles of structured memory and reflection in enhancing robustness.
Overall, Goal2Skill represents a significant step forward in embodied AI, enabling robots to perform complex, multi-stage tasks with higher reliability. Future work aims to optimize computational efficiency, incorporate online learning, and extend applicability to more dynamic real-world scenarios, paving the way for more autonomous and adaptable robotic systems.
Deep Analysis
Background
机器人在长时序、多阶段任务中的自主执行能力一直是研究难点。早期方法多依赖端到端深度学习模型，虽能处理部分任务，但在面对遮挡、部分观察和复杂环境时表现不足。近年来，结构化记忆、多模态融合和高层规划成为研究热点，例如Multi-Scale Embodied Memory和MemoryVLA，提升了任务上下文的持续性。然而，现有系统仍缺乏有效的故障检测与自我修正机制，难以应对长时间、多阶段任务中的突发故障和环境变化，亟需更具鲁棒性和适应性的架构。
Core Problem
长时序操控中的核心难题在于：如何持续维护任务记忆、实现目标的动态分解、以及在执行过程中及时检测和修正错误。端到端模型受限于观察窗口和泛化能力，难以应对部分可观测、遮挡等复杂场景。缺乏明确的语义推理与几何感知的分离，导致系统在长时间、多阶段任务中鲁棒性不足。解决这些问题，需要引入结构化记忆、反思机制和模块化设计，以提升系统的长时任务表现和故障修复能力。
Innovation
本研究的创新点包括：1）利用预训练VLM实现高层目标分解、结构化记忆和动态反思，支持长时序任务的自适应规划；2）设计基于扩散模型的低层运动控制器，增强几何过滤与鲁棒性；3）实现记忆驱动的闭环决策流程，结合反思机制进行故障诊断和修正。这一架构突破了传统端到端模型的局限，使语义推理与运动控制有效分离，显著提升了长时任务的成功率和系统的适应性。
Methodology
- �� 高层规划：输入目标G、观察和记忆，利用VLM生成子任务序列，定义任务指令、条件和约束。• 记忆管理：维护任务历史、工作记忆和错误寄存器，支持动态更新和反思。• 反思机制：在子任务失败时，分析原因，提出修正策略（重试、参数调整或重规划）。• 低层执行：基于几何过滤观察，利用扩散模型生成连续动作，确保鲁棒运动。• 闭环交互：每个子任务后，观察反馈给高层验证，支持自主修正。• 训练评估：在五个RMBench任务上，采用专家示范训练，成功率显著提升。
Experiments
采用五个代表性任务（如观察、拾取、堆叠等），比较不同方法的成功率、鲁棒性和修正能力。基线包括端到端模型和记忆增强模型。指标涵盖成功率、任务完成时间和故障修正次数。通过消融实验验证结构化记忆和反思机制的贡献。系统参数调优包括扩散模型的采样步数和记忆窗口大小，确保公平比较。
Results
在五个任务中，平均成功率达32.4%，远超基线9.8%。在记忆依赖任务中，成功率达38.7%，验证了结构化记忆的有效性。引入反思机制后，故障修正能力增强，系统在遮挡和部分观察条件下表现出更强的鲁棒性。消融实验显示，去除记忆或反思模块，成功率分别下降约15%和20%，强调其关键作用。
Applications
该框架适用于工业自动化、服务机器人和复杂任务场景。只需提供明确目标和环境感知，即可实现自主长时任务。系统能应对遮挡、环境变化和任务中断，提升机器人自主性与可靠性。未来可结合强化学习，优化策略适应性，推动智能机器人普及。
Limitations & Outlook
模型依赖大规模预训练，计算资源消耗大，实时性受限。反思机制在极端复杂场景中的效率仍需提升，且对动态环境的适应性有限。未来需优化模型压缩和在线学习能力，增强系统的自主学习和适应能力。
Plain Language Accessible to non-experts
想象你在厨房做饭。你有一个菜单（目标），但厨房里有很多东西（环境变化、障碍）。你需要记住哪些步骤（记忆），比如先切菜，再煮饭。遇到问题时，比如菜炒糊了，你会停下来检查（验证），发现问题后，再重新调整做法（反思修正）。这个过程就像机器人一样，先计划好每个步骤，然后根据实际情况不断调整，确保最后能做出美味的饭。这个系统把“计划”和“执行”分开，像厨师和助手合作，厨师制定菜单，助手负责具体操作，二者通过不断沟通，确保厨房顺利进行。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的拼图游戏。你先有个大计划，知道要拼出一幅画，但拼图很多，不能一下子全部完成。你会记住哪些拼图片已经拼好，哪些还没拼，遇到卡住的地方，就停下来想办法，比如换个角度或重新找拼图。这个过程就像机器人用的系统：它先制定一个长远的计划，把任务分成小部分，然后一步步去做。每完成一部分，它会检查是不是正确，如果发现问题，就会停下来，想办法修正，或者重新规划下一步。这样，机器人就能像你一样，耐心又聪明地完成复杂任务，不会因为一点小问题就放弃。
Glossary
Vision-Language Model (VLM) (视觉-语言模型)
一种结合视觉理解和自然语言处理的深度学习模型，用于理解场景和指令，支持多模态推理。
在高层规划中，VLM用于目标分解和任务验证。
扩散模型 (Diffusion Model)
一种生成模型，通过逐步去噪实现高质量连续动作生成，增强运动控制鲁棒性。
低层运动控制器采用扩散模型生成连续动作。
结构化记忆 (Structured Memory)
一种多层次、多模态的记忆系统，用于存储任务历史、状态信息和故障记录，支持动态推理。
高层利用结构化记忆进行目标追踪和反思。
闭环决策 (Closed-Loop Decision Making)
系统在执行过程中不断反馈观察，动态调整策略，确保任务稳妥完成。
高低层交互通过闭环实现长时序操控。
Open Questions Unanswered questions from this research
- 1 如何在极端复杂环境中提升反思机制的效率，尤其是在多障碍、多动态变化场景下的故障检测与修正能力。
Applications
Immediate Applications
工业自动化
机器人自主完成装配、检验等复杂任务，提升生产效率和灵活性。
服务机器人
家庭或公共场所中，机器人能自主完成多阶段任务，如送餐、整理等。
Long-term Vision
智能自主系统
实现具有长时记忆和自我修正能力的机器人，能在未知环境中自主学习与适应，推动智能制造和服务行业变革。
Abstract
Recent vision-language-action (VLA) systems have demonstrated strong capabilities in embodied manipulation. However, most existing VLA policies rely on limited observation windows and end-to-end action prediction, which makes them brittle in long-horizon, memory-dependent tasks with partial observability, occlusions, and multi-stage dependencies. Such tasks require not only precise visuomotor control, but also persistent memory, adaptive task decomposition, and explicit recovery from execution failures. To address these limitations, we propose a dual-system framework for long-horizon embodied manipulation. Our framework explicitly separates high-level semantic reasoning from low-level motor execution. A high-level planner, implemented as a VLM-based agentic module, maintains structured task memory and performs goal decomposition, outcome verification, and error-driven correction. A low-level executor, instantiated as a VLA-based visuomotor controller, carries out each sub-task through diffusion-based action generation conditioned on geometry-preserving filtered observations. Together, the two systems form a closed loop between planning and execution, enabling memory-aware reasoning, adaptive replanning, and robust online recovery. Experiments on representative RMBench tasks show that the proposed framework substantially outperforms representative baselines, achieving a 32.4% average success rate compared with 9.8% for the strongest baseline. Ablation studies further confirm the importance of structured memory and closed-loop recovery for long-horizon manipulation.