MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models
MemoryVLA++ integrates memory and imagination for full temporal modeling, significantly improving robotic task success rates.
Key Findings
Methodology
MemoryVLA++ employs a pretrained vision-language model (VLM) to encode current observations into perceptual and cognitive tokens, forming working memory. A perceptual-cognitive memory bank (PCMB) stores detailed low-level features and high-level semantics from past interactions. The model retrieves relevant historical context via attention-based querying, and fuses this information with current tokens through a gating mechanism. To predict future states, a world model based on diffusion processes performs partial denoising in the latent space, generating multi-scale future latent representations. These imagined latents are integrated with memory-augmented tokens under memory guidance, producing full temporal-aware representations. These representations condition a diffusion-based action expert to generate temporally consistent action sequences. This framework draws inspiration from cognitive science, mimicking human working memory, episodic memory, and internal models, to enhance long-horizon robotic manipulation.
Key Results
- On five simulation benchmarks (Libero, SimplerEnv, Mikasa-Robo, Calvin, Libero-Plus), MemoryVLA++ achieved success rates of up to 98.4%, 74.0%, 44.4%, and 4.29 scores, outperforming strong baselines with improvements up to 16.7 percentage points. In real robot experiments, it scored over 85% in general manipulation, with +9% on general tasks, +26% on memory-dependent tasks, and +28% on imagination-dependent tasks, demonstrating robustness and generalization.
- Ablation studies confirmed that memory retrieval and future latent generation are critical for performance gains, especially in long-horizon tasks. The model effectively mitigates error accumulation common in traditional approaches, leading to more stable and accurate control.
- Overall, the results validate that full temporal modeling with memory and imagination significantly enhances robotic autonomy, especially in tasks requiring long-term dependencies and future state anticipation.
Significance
This work introduces a paradigm shift in robotic manipulation by integrating cognitive-inspired memory and imagination mechanisms into deep learning frameworks. It addresses the core challenge of long-term dependency modeling, enabling robots to remember past experiences and pre-emptively predict future states. Such capabilities are crucial for autonomous systems operating in complex, dynamic environments, where decision-making relies on both historical context and future foresight. The approach bridges the gap between cognitive science and robotics, offering a scalable, end-to-end solution that can be extended to multi-task, multi-environment scenarios. Its success paves the way for more intelligent, adaptable robots capable of continuous learning and complex task execution, with broad implications for industrial automation, service robotics, and beyond.
Technical Contribution
The paper proposes a comprehensive framework combining several novel components: 1) a perceptual-cognitive memory bank (PCMB) for long-term storage and retrieval of detailed and semantic information; 2) a latent-space future state generator based on a pre-trained diffusion model (Stable Video Diffusion), which performs partial denoising to generate multi-scale future representations efficiently; 3) a memory-guided fusion mechanism that adaptively integrates imagined future latents with historical memory, maintaining decision relevance while suppressing irrelevant noise; 4) an end-to-end training pipeline that jointly optimizes perception, memory, imagination, and action prediction modules. These innovations collectively enable the model to perform full temporal modeling, surpassing prior methods that only relied on current observations or short-term memory, thus opening new avenues for robust long-horizon robotic control.
Novelty
This work is the first to systematically incorporate full temporal modeling—covering past, present, and future—within a unified VLA framework. Unlike previous models limited to reactive or short-term memory, MemoryVLA++ leverages a cognitive-inspired memory bank and a latent diffusion-based future generator, enabling explicit long-term memory and future prediction. Its integration of memory-guided imagination in the latent space, combined with end-to-end training, represents a significant step forward in robotic temporal reasoning. This approach addresses fundamental limitations in existing methods, offering a scalable, efficient, and more human-like decision-making process.
Limitations
- Despite its advances, MemoryVLA++ still faces challenges in extremely complex or highly dynamic environments, where memory capacity and future prediction accuracy may degrade. The reliance on large pre-trained models also entails significant computational costs, limiting real-time deployment in resource-constrained settings.
- The model's performance depends on the quality and diversity of training data, especially for world model adaptation. In scenarios with novel objects or unforeseen dynamics, the system may struggle to generate accurate future states.
- While the framework effectively handles long-horizon tasks, its scalability to multi-agent systems or multi-task learning remains to be explored. Further research is needed to optimize memory management and inference efficiency.
Future Work
Future directions include optimizing memory management strategies to handle larger and more diverse task sets, integrating reinforcement learning to adaptively improve retrieval and fusion processes, and extending the framework to multi-modal sensory inputs such as tactile and auditory data. Additionally, exploring online learning and continual adaptation will enable robots to refine their models in real-time, further enhancing autonomy. Bridging the gap between simulation and real-world deployment, especially in unstructured environments, remains a key goal. Finally, scaling the approach to multi-agent systems could unlock collaborative capabilities in complex scenarios like warehouse logistics or autonomous driving.
AI Executive Summary
In the rapidly evolving field of robotic manipulation, a persistent challenge has been enabling robots to handle tasks that require understanding and reasoning over extended periods. Traditional models excel at reactive behaviors based on immediate observations but falter when it comes to tasks demanding long-term memory and future prediction. This limitation hampers robots' ability to perform complex, multi-step operations reliably in real-world environments.
Addressing this gap, Hao Shi and colleagues introduce MemoryVLA++, a groundbreaking framework that integrates cognitive-inspired memory and imagination mechanisms into vision-language-action (VLA) models. Drawing inspiration from human cognition, the system combines a perceptual-cognitive memory bank with a latent-space world model to perform full temporal modeling—encompassing past experiences, current perceptions, and future state predictions.
The core of MemoryVLA++ is a pretrained vision-language model (VLM) that encodes current observations into perceptual and cognitive tokens. These tokens form the working memory, which queries a structured memory bank to retrieve relevant historical information. This retrieval process is attention-based, allowing the model to focus on decision-critical past experiences. The retrieved information is fused with current tokens through a gating mechanism, ensuring a balanced integration of past and present.
To anticipate future states, the framework employs a diffusion-based world model trained on large-scale manipulation videos. Instead of pixel-level predictions, it performs partial denoising in the latent space, generating multi-scale future representations efficiently. These imagined latents are then integrated with memory-augmented tokens, guided by the stored historical context, to produce comprehensive temporal-aware representations.
Experimental evaluations across five simulation benchmarks and three real-robot platforms demonstrate the effectiveness of MemoryVLA++. The results show substantial improvements over existing methods, with success rates reaching up to 98.4% in simulations and over 85% in real-world tasks. Notably, the system excels in long-horizon tasks that demand both memory and imagination, validating the importance of full temporal modeling.
This work marks a significant advancement in robotic autonomy, enabling systems to remember, reason, and anticipate more like humans. Its implications extend to industrial automation, service robots, and autonomous systems, paving the way for more adaptable, intelligent, and reliable robots capable of complex, continuous operations. Despite remaining challenges such as computational costs and scalability, MemoryVLA++ sets a new standard for future research in long-term robotic decision-making and cognitive-inspired AI.
Deep Dive
Abstract
Temporal modeling is essential for robotic manipulation, as effective control requires both memory of past interactions and imagination of future states. However, most VLA models rely primarily on the current observation and therefore struggle with long-horizon, temporally dependent tasks. Cognitive science suggests that humans rely on working memory to buffer short-lived context, the hippocampal system to preserve episodic memory of past experience, and internal models to imagine possible future state evolution. Inspired by these mechanisms, we propose MemoryVLA++, a full temporal modeling framework that equips VLA models with memory and imagination for robotic manipulation. A pretrained VLM encodes the current observation into perceptual and cognitive tokens, forming working memory. These tokens query a Perceptual-Cognitive Memory Bank to retrieve relevant historical context. This bank stores low-level details and high-level semantics from past interactions, and is updated through redundancy-aware consolidation. A world model imagines future states in a denoising latent space, and the imagined latents are integrated under memory guidance to form full temporal-aware tokens. The resulting tokens condition a diffusion action expert to predict temporally consistent action sequences. We conduct extensive experiments on 5 simulation benchmarks and 3 categories of real-robot tasks across 3 robots, covering general manipulation, long-horizon temporal tasks, robustness, and generalization. Our method achieves strong performance across Libero, SimplerEnv, Mikasa-Robo, Calvin, Libero-Plus, and diverse real-robot tasks, validating the effectiveness of full temporal modeling with memory and imagination. For example, on real robots, it achieves +9%, +26%, +28% gains on general, memory-dependent, and imagination-dependent tasks. Project Page: https://shihao1895.github.io/MemoryVLA-PP-Web