PREM: Prediction-Regulated Memory for Robot Manipulation
Abstract
A robot's next action can depend on recent motion or earlier visual evidence absent from its current observation. A recurrent visual-memory architecture can retain this history in a visual state, but its usefulness depends on what the state represents and how it is updated with new observations. We introduce Prediction-Regulated Memory (PREM), a framework that uses action-conditioned future prediction to guide both memory learning and updating in vision-language-action policies. During training, predicting future visual features provides temporal supervision for memory learning. During execution, PREM compares current visual features with the prediction made at the preceding decision and uses the spatially normalized prediction discrepancy to regulate updates to the visual state at each spatial location. The updated visual state supplies temporal context for action generation alongside current observations. Built on $π_{0.5}$, PREM improves average success rate from 9.63% to 20.74% on DOMINO dynamic manipulation tasks and from 14.4% to 83.8% on the RMBench M(1) memory-dependent setting. Further evaluations on LIBERO and LIBERO-Pro show improvements in general manipulation and robustness to spatial perturbations. PREM incurs a mean inference overhead of 7.7 ms on a single NVIDIA H800 GPU.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper introduces a novel memory regulation mechanism for VLA policies that leverages action-conditioned future prediction to improve temporal context retention, demonstrating significant performance gains on memory-dependent and dynamic manipulation benchmarks with minimal computational overhead.
Methodology
The paper proposes PREM (Prediction-Regulated Memory), a framework that integrates action-conditioned future prediction into the memory mechanism of Vision-Language-Action (VLA) policies. The core innovation is twofold: during training, future visual feature prediction serves as a temporal supervision signal for the memory state; during inference, the discrepancy between the predicted future visual state and the actual current observation is used to spatially regulate the update of the recurrent memory. This approach addresses the common issue in robotic manipulation where the current observation lacks sufficient context about recent dynamics or hidden states. By leveraging prediction errors to modulate memory updates, the method aims to retain relevant historical information while discarding noise. The method is built upon the $\pi_{0.5}$ architecture, a strong baseline in the field.
Results & evaluation
The experimental evaluation is robust, covering multiple benchmarks: DOMINO (dynamic manipulation), RMBench (memory-dependent tasks), LIBERO, and LIBERO-Pro. The reported improvements are substantial, particularly on RMBench M(1), where success rate jumps from 14.4% to 83.8%, and on DOMINO, from 9.63% to 20.74%. These results suggest the method effectively handles tasks requiring temporal context. The inclusion of LIBERO-Pro evaluations for robustness to spatial perturbations adds depth to the generalization claims. The inference overhead of 7.7 ms on an H800 GPU is reported as negligible, which is a practical advantage for real-time control.
Reproducibility
The paper includes a detailed reproducibility statement, promising code and configuration files in the supplementary material. It specifies architectural dimensions, hyperparameters, and training schedules in the appendix. The use of standard benchmarks (LIBERO, DOMINO, RMBench) facilitates independent verification. The AI use statement is transparent, clarifying that AI was used only for editing and formatting, not for data generation or hypothesis formulation.
Limitations
The primary limitation is the reliance on the $\pi_{0.5}$ backbone; it is unclear how well the PREM module generalizes to other VLA architectures or non-recurrent memory structures. The evaluation is limited to simulation and specific real-world setups (implied by the benchmarks), and long-horizon real-world deployment challenges (e.g., lighting changes, object deformations) are not extensively explored. The "prediction" mechanism assumes a certain degree of predictability in the environment, which may not hold in highly stochastic or adversarial settings.
Broader impact
This work contributes to the broader goal of creating robots that can learn from experience and maintain context over time. The technique of using prediction errors to regulate memory updates is a concept that could be adapted to other sequential decision-making tasks beyond robotics, such as autonomous driving or interactive agents. It addresses a fundamental challenge in embodied AI: how to efficiently compress and update historical information for future action planning. The paper introduces a novel memory regulation mechanism for VLA policies that leverages action-conditioned future prediction to improve temporal context retention, demonstrating significant performance gains on memory-dependent and dynamic manipulation benchmarks with minimal computational overhead.
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