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

TL;DR

ForeDiffusion introduces a foresight-conditioned diffusion policy for robot manipulation, leveraging future view prediction to improve success rates and stability in complex tasks, surpassing existing methods.

Contribution

The paper proposes ForeDiffusion, a novel diffusion strategy that incorporates future view prediction and dual loss optimization to enhance robot manipulation performance.

Findings

Achieves 80% success rate on Adroit and MetaWorld benchmarks.

Outperforms existing diffusion methods by 23% in complex tasks.

Demonstrates improved stability across various tasks.

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.