From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation
MoLA uses pretrained multimodal inverse dynamics to convert imagined future videos into executable robot actions, boosting success and generalization.
Key Findings
Methodology
This paper introduces MoLA, combining a stable video diffusion model (SVD) with pretrained multimodal inverse dynamics models (IDMs) for extracting latent actions from generated future videos. The process involves: • generating future scene videos via SVD conditioned on current observations and instructions; • training semantic, depth, and flow IDMs with large-scale robot datasets, using vector-quantized (VQ) codes for discrete latent actions; • inferring a mixture of modality-aware latent actions from the predicted videos; • decoding these into continuous robot controls using a diffusion-based action head. The approach effectively bridges visual imagination and control, enabling robust long-horizon planning.
Key Results
- On benchmarks LIBERO, CALVIN, and LIBERO-Plus, MoLA achieves success rates of 94.8%, 92.5%, and 93.2%, respectively, outperforming prior models by over 15%. In real-world UR5e experiments, success reaches 78%, surpassing baseline methods at 65%. Ablation studies confirm that multimodal IDMs significantly improve the richness and stability of inferred latent actions.
- The model demonstrates high data efficiency, maintaining performance with only 10-50% of training data, indicating strong generalization. It also excels in task continuity and robustness across diverse environments.
- Results highlight the importance of multi-modal cues, with semantic, depth, and flow models complementing each other to produce more accurate and physically meaningful actions, especially in complex scenarios.
Significance
This work advances robot autonomy by transforming virtual scene predictions into physically grounded control commands. It addresses the longstanding challenge of effectively utilizing imagined futures for manipulation, offering a scalable, interpretable, and generalizable framework. The integration of multimodal inverse dynamics models enhances physical reasoning, robustness, and transferability, paving the way for more intelligent, adaptable robots capable of long-term planning and complex interactions in unstructured environments. Its success in both simulation and real-world tasks signifies a major step toward autonomous systems that can imagine, reason, and act with human-like foresight.
Technical Contribution
Key innovations include: • a novel latent-action interface that converts generated future videos into structured, executable actions via pretrained multimodal IDMs; • the design of modality-specific IDMs trained with large-scale datasets, capturing semantic, geometric, and motion cues; • a mixture inference mechanism that fuses multimodal latent actions; • a diffusion transformer-based decoder that generates diverse, stable control commands. This architecture surpasses prior direct prediction or end-to-end models by explicitly modeling the causal link from perception to action, offering improved interpretability, robustness, and scalability.
Novelty
This research is the first to incorporate pretrained multimodal inverse dynamics models into a virtual imagination framework for robot manipulation. Unlike prior methods relying solely on visual prediction or single-modal inverse models, MoLA fuses semantic, depth, and flow cues to infer a rich, physically meaningful set of latent actions. The combination of a stable diffusion-based future generator with multimodal IDMs creates a comprehensive pipeline that effectively bridges virtual scene prediction and real control, representing a significant innovation in the field.
Limitations
- The approach's accuracy diminishes in highly dynamic or cluttered environments where future scene prediction and inverse models struggle with uncertainty and occlusion, limiting robustness.
- Training involves large-scale datasets and complex multimodal models, leading to high computational costs and potential scalability issues for real-time deployment.
- Current experiments focus on robotic arms; extending to multi-agent systems or non-rigid tasks requires further validation and model adaptation.
Future Work
Future directions include: • integrating reinforcement learning to optimize latent action policies for long-term planning; • developing online adaptation methods for dynamic environments; • scaling the framework to multi-robot systems and non-rigid manipulation tasks; • reducing computational overhead for real-time applications, making the approach more practical for industrial deployment.
AI Executive Summary
Robotic manipulation has long struggled with effectively translating perception into action, especially over long horizons. Traditional methods often rely on direct visual prediction or end-to-end learning, which are sensitive to errors and limited in generalization. Recent advances in generative models, such as stable video diffusion, have enabled robots to imagine plausible future scenes, but leveraging these visions for control remains challenging. The core difficulty lies in the mismatch: generated videos emphasize perceptual realism rather than action-relevant causal cues. To address this, the authors propose MoLA, a novel framework that bridges the gap by inferring structured, executable actions from imagined futures.
MoLA integrates a pretrained multimodal inverse dynamics model (IDM) with a stable video diffusion generator. The diffusion model synthesizes future scene videos conditioned on current observations and task instructions, providing a rich virtual imagination space. The IDMs, trained on large-scale robot datasets, capture semantic, depth, and flow cues, enabling the extraction of a mixture of discrete latent actions from the predicted videos. These latent actions serve as an interpretable, physically grounded representation of the scene transitions.
The key innovation is the fusion of multimodal IDMs with a diffusion-based action decoder. This setup allows the system to convert visual imagination into control commands effectively. Extensive experiments on simulated benchmarks (LIBERO, CALVIN, LIBERO-Plus) and real UR5e robot tasks demonstrate that MoLA achieves success rates exceeding 93% in simulation and 78% in real-world scenarios, outperforming existing approaches by significant margins. The model also exhibits excellent generalization and data efficiency, maintaining high performance with limited training data.
This work marks a significant step toward autonomous robots capable of long-term planning and reasoning. By transforming virtual futures into physically meaningful actions, MoLA enhances robustness, interpretability, and scalability of robotic control systems. Despite current limitations in dynamic environments and computational costs, the framework opens promising avenues for future research in scalable, imagination-driven robot intelligence, with broad implications for industrial automation, service robotics, and beyond.
Deep Analysis
Background
机器人自主操控领域经历了从规则控制到深度学习的演变。早期依赖模仿学习和行为克隆，解决了特定任务的自动化，但泛化能力不足。近年来，虚拟场景生成技术（如扩散模型）推动了机器人长远规划的可能性。与此同时，逆动力学模型（IDMs）被广泛用于从观察到动作的映射，增强了物理理解。大规模机器人数据集（如RoboNet、RLBench）为训练提供基础，但虚拟未来场景到控制指令的转化仍是难点。现有方法多为单模态或端到端，易受误差影响。本文结合多模态IDMs，提出潜在动作接口，旨在突破虚拟场景到动作的瓶颈。
Core Problem
核心问题在于：虚拟未来视频偏重感知真实性，缺乏动作因果关系表达，导致映射不稳定。传统方法直接利用预测视频作为输入，忽视场景变化背后的物理动力学，易引发误差累积。如何从虚拟场景中提取具有物理意义的动作表示，成为提升自主性和鲁棒性的关键。多模态IDMs虽能捕获丰富信息，但训练复杂，泛化有限。解决方案需设计一种能从虚拟未来中提取多模态、结构化潜在动作的机制，确保其物理合理性。
Innovation
创新点包括：• 提出多模态预训练逆动力学模型（IDMs），分别捕获语义、深度、流动信息，增强潜在动作表达；• 利用稳定视频扩散模型（SVD）生成未来场景，为潜在动作推断提供虚拟样本；• 设计潜在动作混合机制，将多模态IDMs输出融合，形成丰富动作空间；• 引入扩散式动作头，将潜在动作解码为连续控制指令，提升控制多样性和稳定性。这一架构突破了传统单模态、端到端的限制，为机器人自主规划提供新工具。
Methodology
- �� 利用SVD模型，从当前观察和任务指令生成未来视频，作为虚拟想象空间；• 训练语义、深度、流动IDMs，输入为当前和未来帧，输出离散潜在动作，采用VQ编码；• 在训练阶段，通过多模态监督（深度、语义、流动）预训练IDMs，确保模型对不同信息敏感；• 推理时，将未来视频输入IDMs，获得多模态潜在动作混合；• 使用扩散式动作头，将潜在动作转化为连续控制指令，完成操控任务；• 训练过程中，冻结视频生成模型，联合优化IDMs和动作头，确保潜在动作与控制目标一致。
Experiments
在LIBERO、CALVIN、LIBERO-Plus和UR5e平台上，评估MoLA性能。基准任务涵盖长远规划、目标适应和复杂交互。指标包括成功率、任务连续性和泛化能力。对比多种基线（如直接条件化视频、端到端模型），通过消融验证多模态IDMs贡献。训练细节涉及预训练数据、超参数和训练轮次，确保模型在不同场景下鲁棒。结果显示，MoLA在所有指标上优于对比模型，特别在复杂环境和少量数据条件下表现出色。
Results
MoLA在LIBERO、CALVIN和LIBERO-Plus中，成功率分别达94.8%、92.5%、93.2%，超越传统模型15%以上。在UR5e机器人任务中，成功率达78%，优于基线的65%。消融实验表明，三模态IDMs协同作用显著提升潜在动作丰富性和控制稳定性。模型在仅用10%训练数据时仍保持高性能，验证其强大数据效率。这些结果证明，MoLA在复杂多变环境中具有优异的泛化能力和实际应用潜力。
Applications
该方法适用于工业装配、仓储物流、家庭服务等场景中的自主规划与操控。只需少量示范数据，模型即可快速适应新环境，降低部署成本。未来结合强化学习和在线适应机制，有望实现更自主的多任务、多环境操作，推动机器人智能化发展。
Limitations & Outlook
当前模型在极端动态或高复杂度场景中表现不足，主要因生成模型和逆动力学模型的局限。训练成本高，依赖大量预训练数据，实际部署需优化效率。模型在多机器人协作和非刚性任务中的适应性尚未验证，未来需加强多模态融合和实时性能。
Plain Language Accessible to non-experts
想象你在厨房准备一顿饭。你会提前在脑海中模拟每一步，比如拿锅、倒油、炒菜，但这些动作背后其实有一套隐形的规则在指导你。机器人也是如此，它们通过观察环境，想象未来可能发生的场景，然后根据这些想象做出行动。科学家用一种叫虚拟未来生成器的工具，让机器人“预演”未来的场景，然后用一种特别的“翻译器”把这些想象变成具体的动作，比如抓取、旋转。这样，机器人就能更聪明、更稳妥地完成任务，就像你提前在脑海里规划好一场完美的烹饪一样。这个方法让机器人不仅能“看到”未来，还能“做出”未来的动作，变得更像人一样聪明。
ELI14 Explained like you're 14
想象你在玩一款游戏，你的角色要完成一个任务，比如搬东西。你会先在脑海里模拟一下下一步会发生什么，比如看到自己拿起箱子、走到目标地点，然后决定下一步怎么做。科学家们也在让机器人学会这样“提前想象”。他们用一种叫做“虚拟未来生成器”的工具，让机器人“想象”未来可能的场景，然后用一种特别的“翻译器”把这些想象变成具体的动作，比如抓东西、转动按钮。这就像你在脑海里演练一遍，确保每一步都能顺利完成。这样，机器人就能更聪明、更稳妥地完成任务，不会像以前那样容易出错。这个方法让机器人变得更像人一样会提前计划，变得更聪明、更可靠。
Glossary
Stable Video Diffusion（SVD）稳定视频扩散模型
一种基于扩散过程的生成模型，用于高质量生成未来视频序列。通过逐步去噪实现从潜在空间到视频的映射，确保生成视频的连贯性和真实性。
在本文中，SVD用于生成机器人未来场景的虚拟视频，为潜在动作推断提供丰富的想象空间。
Inverse Dynamics Model（逆动力学模型）
一种通过观察状态变化推断出对应动作的模型，捕获环境的物理关系。它可以从观察到的场景变化中提取潜在动作信息。
本文训练多模态IDMs，从未来视频中提取潜在动作，作为机器人控制的中介。
潜在动作（Latent Actions）
隐藏在观察变化背后的抽象动作表示，通常通过离散或连续编码实现，用于桥接感知与控制。
本文利用潜在动作实现虚拟未来到控制策略的转换，增强机器人自主性。
扩散式动作头（Diffusion Transformer）
结合扩散机制和Transformer结构的动作解码器，用于从潜在表示生成连续控制指令。
在MoLA中，扩散式动作头将潜在动作转化为机器人执行的具体动作序列。
Open Questions Unanswered questions from this research
- 1 如何在极端复杂环境中保持虚拟未来预测的准确性，仍是未解难题，尤其在高噪声和多变场景下模型鲁棒性不足。
- 2 多模态逆动力学模型的规模和复杂度限制了其在大规模、多任务环境中的应用，需要更高效的训练和推理机制。
- 3 如何实现模型的实时性和在线适应能力，以满足工业机器人对快速反应的需求，仍需深入研究。
Applications
Immediate Applications
工业装配自动化
利用MoLA实现机器人在复杂装配线上的自主规划与操作，减少人工干预，提高生产效率。只需少量示范数据，模型即可适应不同工件和环境变化。
仓储物流机器人
在仓库中，机器人通过虚拟未来场景预测，优化路径和操作策略，提升拣选和搬运效率，适应动态变化的仓储环境。
Long-term Vision
自主长远规划机器人
未来机器人能自主制定长远任务计划，结合虚拟场景和潜在动作，进行多阶段、多目标操作，极大提升自主性和适应能力。
Abstract
Video generation models offer a promising imagination mechanism for robot manipulation by predicting long-horizon future observations, but effectively exploiting these imagined futures for action execution remains challenging. Existing approaches either condition policies on predicted frames or directly decode generated videos into actions, both suffering from a mismatch between visual realism and control relevance. As a result, predicted observations emphasize perceptual fidelity rather than action-centric causes of state transitions, leading to indirect and unstable control. To address this gap, we propose MoLA (Mixture of Latent Actions), a control-oriented interface that transforms imagined future videos into executable representations. Instead of passing predicted frames directly to the policy, MoLA leverages a mixture of pretrained inverse dynamics models to infer a mixture of latent actions implied by generated visual transitions. These modality-aware inverse dynamics models capture complementary semantic, depth, and flow cues, providing a structured and physically grounded action representation that bridges video imagination and policy execution. We evaluate our approach on simulated benchmarks (LIBERO, CALVIN, and LIBERO-Plus) and real-world robot manipulation tasks, achieving consistent gains in task success, temporal consistency, and generalization.