CDP: Towards Robust Autoregressive Visuomotor Policy Learning via Causal Diffusion

2025-06-17

AI Summary

To address weak policy generalization and frequent failures in long-horizon tasks on real robots—caused by poor observation quality and strict real-time constraints—this paper proposes Causal Diffusion Policy (CDP). CDP innovatively conditions the diffusion model on historical action sequences, enabling temporally consistent and context-aware action prediction. It introduces a KV caching mechanism to reuse attention key-value pairs, balancing inference robustness with low latency. Furthermore, CDP fuses vision-action multimodal signals within a Transformer-based causal diffusion architecture. Evaluated on both simulation and real-robot platforms, CDP significantly outperforms existing methods: it maintains high-precision 2D/3D dexterous manipulation even under degraded image quality, and demonstrates superior stability and generalization in target localization, grasp planning, and long-duration task execution.

Technology Category

Computer Vision: Diffusion Models for VisionHumans and AI: Human-Aware Planning and Behavior PredictionMachine Learning: Causal Learning

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingResponsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Diffusion Policy (DP) enables robots to learn complex behaviors by imitating expert demonstrations through action diffusion. However, in practical applications, hardware limitations often degrade data quality, while real-time constraints restrict model inference to instantaneous state and scene observations. These limitations seriously reduce the efficacy of learning from expert demonstrations, resulting in failures in object localization, grasp planning, and long-horizon task execution. To address these challenges, we propose Causal Diffusion Policy (CDP), a novel transformer-based diffusion model that enhances action prediction by conditioning on historical action sequences, thereby enabling more coherent and context-aware visuomotor policy learning. To further mitigate the computational cost associated with autoregressive inference, a caching mechanism is also introduced to store attention key-value pairs from previous timesteps, substantially reducing redundant computations during execution. Extensive experiments in both simulated and real-world environments, spanning diverse 2D and 3D manipulation tasks, demonstrate that CDP uniquely leverages historical action sequences to achieve significantly higher accuracy than existing methods. Moreover, even when faced with degraded input observation quality, CDP maintains remarkable precision by reasoning through temporal continuity, which highlights its practical robustness for robotic control under realistic, imperfect conditions.

Problem

• Improving robustness in visuomotor policy learning with degraded data
• Reducing computational cost in autoregressive inference for real-time constraints
• Enhancing action prediction accuracy using historical action sequences

Innovation

• Transformer-based diffusion model for action prediction
• Caching mechanism reduces autoregressive computation cost
• Leverages historical actions for robust policy learning