RAPAC-DP: Response-Aligned Pending-Action Compensation for Diffusion Policies under Delayed Execution
RAPAC-DP introduces response-aligned pending-action compensation, retaining 81.4% performance at maximum delay, improving robustness of diffusion policies under communication latency.
Key Findings
Methodology
RAPAC-DP constructs delay-conditioned training samples by shifting supervision windows from delay-free demonstrations, using executed actions during delay as pending actions. It incorporates a pending-action encoder and LoRA adapters into a frozen base policy, training only these modules for parameter efficiency. During deployment, it leverages local pending-action queues and known delay to predict response-aligned action chunks, effectively mitigating observation-action mismatch caused by communication delays. Compatible with diffusion and flow-based action generators, this approach significantly enhances delay robustness in cloud-based imitation policies.
Key Results
- On Kinetix, RAPAC-DP maintains 81.4% of its delay-free performance at maximum tested delay, with success rate dropping from 0.883 to 0.731, outperforming Naive (21.2%) and RTC. On RoboMimic, it achieves an average success rate of 0.633 across three tasks, surpassing A2C2 by 20.6 percentage points. The method demonstrates strong robustness across various delay levels, with minimal performance degradation even at severe delays.
- Experimental results show that response-aligned training and pending-action conditioning are crucial for robustness. Ablation studies confirm that only training the compensation pathway with response alignment yields significant improvements, while LoRA adaptation alone offers negligible gains. The approach generalizes well across diverse environments and tasks, validating its practical effectiveness.
- The method requires no additional delayed demonstrations, relying solely on delay-free data, which reduces training complexity and cost. Its parameter-efficient design facilitates deployment on large models, making it suitable for real-world robotic systems with communication constraints.
Significance
This work addresses a critical challenge in cloud robotics: mitigating the adverse effects of communication delays on control performance. By introducing a response-aligned pending-action framework, it offers a scalable, efficient solution that preserves high task success rates under severe delays. The approach bridges the gap between high-capacity cloud inference and real-time robotic control, enabling more reliable remote operation. Its parameter-efficient design also opens avenues for deploying large-scale diffusion-based policies in resource-constrained environments, fostering advancements in autonomous robotics, teleoperation, and distributed AI systems.
Technical Contribution
The paper proposes a novel response-aligned training paradigm that constructs delay-conditioned samples from delay-free demonstrations, avoiding the need for system dynamics models or additional data. It integrates a pending-action encoder and LoRA adapters into a frozen base policy, training only these modules for delay compensation. The response-aligned prediction mechanism ensures that the generated action chunks are synchronized with the response time, significantly improving robustness. This framework is compatible with both diffusion and flow-based models, providing a flexible, parameter-efficient solution for asynchronous inference challenges.
Novelty
This research is the first to explicitly leverage response-aligned pending-action conditioning for delay compensation in diffusion policies. Unlike prior methods relying on state prediction or history augmentation, it directly models the delay effects through training sample construction from delay-free data, combined with parameter-efficient adaptation. The integration of a pending-action encoder and LoRA modules into a frozen policy represents a novel engineering approach, enabling real-time delay robustness without retraining the entire model.
Limitations
- The approach assumes known, fixed delay durations; in real-world scenarios with variable delays, performance may degrade unless extended with adaptive delay estimation.
- Reliance on high-quality demonstrations means that in complex or noisy environments, the effectiveness could diminish. Additional robustness mechanisms might be necessary.
- While parameter-efficient, the method still introduces extra modules that add complexity to deployment, especially in highly dynamic or resource-limited systems. Further research is needed to handle variable delays and reduce computational overhead.
Future Work
Future directions include developing adaptive delay estimation techniques, enabling the system to handle variable and unpredictable network latencies. Extending the framework to multi-robot coordination and more complex tasks, such as long-horizon planning, is also promising. Integrating reinforcement learning to optimize compensation strategies dynamically and exploring online adaptation mechanisms could further enhance robustness and scalability in real-world applications.
AI Executive Summary
In modern robotics, leveraging cloud-based inference offers unprecedented computational power, enabling complex policies like diffusion models to perform sophisticated tasks. However, communication delays pose a significant challenge, causing a mismatch between the robot’s current state and the actions generated remotely. Traditional solutions, such as state prediction or history augmentation, often struggle with high latency or computational costs. To address this, the authors propose RAPAC-DP, a novel framework that introduces response-aligned pending-action compensation.
RAPAC-DP constructs training samples directly from delay-free demonstrations by shifting supervision windows according to sampled delays, effectively modeling the delay effects without additional data. It incorporates a pending-action encoder and LoRA adapters into a frozen base policy, training only these modules for parameter efficiency. During deployment, the system uses local pending-action queues and known delay durations to predict response-aligned action chunks, ensuring synchronization with the actual response time. This mechanism maintains high control fidelity even under severe delays.
Extensive experiments on Kinetix and RoboMimic demonstrate that RAPAC-DP retains over 81% of its delay-free performance at maximum tested delays, outperforming naive and existing asynchronous methods. The approach’s ability to adapt without extra demonstrations and its parameter-efficient design make it highly practical for real-world robotic applications. Overall, this work significantly advances cloud robotics by providing a robust, scalable solution to communication latency, paving the way for more reliable remote robotic control and autonomous systems.
Deep Dive
Glossary
Diffusion Model (扩散模型)
A generative model that iteratively denoises a sample to produce complex data distributions; used for action generation in robotics.
RAPAC-DP supports diffusion-based policies.
LoRA (Low-Rank Adaptation)
A technique that adds low-rank matrices to frozen weights, enabling parameter-efficient fine-tuning; used for delay compensation modules.
Implemented in RAPAC-DP for efficient adaptation.
Response-Aligned Prediction (响应对齐预测)
A method that aligns predicted actions with the system’s response time, reducing delays' impact.
Core to RAPAC-DP’s delay compensation.
Pending-Action Sequence (待执行动作序列)
Actions queued during communication delay, used as conditioning input for response-aligned prediction.
Key input in the proposed framework.
Open Questions Unanswered questions from this research
- 1 如何在动态变化的网络延迟环境中自适应调整补偿机制仍未解决，需研究实时延迟估计与动态调节策略。
- 2 目前方法依赖固定延迟假设，实际应用中网络延迟可能变化，影响补偿效果。
- 3 在极端复杂环境或非理想示范下，补偿机制的鲁棒性和泛化能力仍需验证。
Applications
Immediate Applications
云端机器人控制
在工业自动化、远程操控等场景中，利用RAPAC-DP缓解通信延迟，提升控制稳定性和任务成功率。
远程无人驾驶
通过响应对齐补偿确保远程车辆在网络不稳定时仍能安全、精准地执行控制指令。
Long-term Vision
多机器人协作
实现多机器人系统中延迟鲁棒的协调控制，推动智能制造和探索任务的自动化发展。
Abstract
Cloud-side inference gives imitation-learning policies access to greater computational resources, but communication and computation delays can degrade control performance. To compensate for these delays, we propose RAPAC-DP, a response-aligned pending-action compensation framework designed for both diffusion- and flow-based action generators. RAPAC-DP encodes the actions already scheduled for execution before the cloud response arrives into a pending-action sequence that serves as the conditioning input to a parameter-efficient compensation pathway. When delay effects are negligible, bypassing this pathway exactly recovers the frozen base policy. For training, RAPAC-DP constructs delay-conditioned samples from delay-free demonstrations, requiring neither explicit system dynamics nor additional delayed demonstrations. At the largest fixed delay tested on Kinetix, RAPAC-DP retained 81.4% of its overall delay-free performance. At the largest fixed delay tested on each RoboMimic task, it achieved a mean success rate of 0.633 across the three tasks. These results demonstrate the effectiveness of pending-action compensation for cloud-deployed imitation-learning policies.