ForeDiffusion: Foresight-Conditioned Diffusion Policy via Future View Construction for Robot Manipulation

2026-01-19

AI Summary

Existing diffusion-based policies for robotic manipulation often suffer from trajectory drift and reduced success rates in complex tasks due to their reliance on short-horizon observations and a single denoising loss. To address this limitation, this work proposes a forward-looking diffusion policy that, for the first time, incorporates predicted future visual observations as conditioning inputs into the diffusion process. The approach introduces a dual-objective optimization framework that jointly minimizes both the standard denoising loss and a novel future observation consistency loss, thereby enabling more accurate and robust action generation. Evaluated on the Adroit and MetaWorld benchmarks, the method achieves an average task success rate of 80%, outperforming state-of-the-art diffusion approaches by 23% on complex tasks and demonstrating significantly improved policy stability and generalization.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Diffusion strategies have advanced visual motor control by progressively denoising high-dimensional action sequences, providing a promising method for robot manipulation. However, as task complexity increases, the success rate of existing baseline models decreases considerably. Analysis indicates that current diffusion strategies are confronted with two limitations. First, these strategies only rely on short-term observations as conditions. Second, the training objective remains limited to a single denoising loss, which leads to error accumulation and causes grasping deviations. To address these limitations, this paper proposes Foresight-Conditioned Diffusion (ForeDiffusion), by injecting the predicted future view representation into the diffusion process. As a result, the policy is guided to be forward-looking, enabling it to correct trajectory deviations. Following this design, ForeDiffusion employs a dual loss mechanism, combining the traditional denoising loss and the consistency loss of future observations, to achieve the unified optimization. Extensive evaluation on the Adroit suite and the MetaWorld benchmark demonstrates that ForeDiffusion achieves an average success rate of 80% for the overall task, significantly outperforming the existing mainstream diffusion methods by 23% in complex tasks, while maintaining more stable performance across the entire tasks.

Problem

• diffusion policy
• robot manipulation
• future view
• error accumulation
• visual motor control

Innovation

• Foresight-Conditioned Diffusion
• Future View Construction
• Dual Loss Mechanism
• Visual Motor Control
• Robot Manipulation