Diffusion Policy (DP) has attracted significant attention as an effective method for policy representation due to its capacity to model multi-distribution dynamics. However, current DPs are often based on a single visual modality (e.g., RGB or point cloud), limiting their accuracy and generalization potential. Although training a generalized DP capable of handling heterogeneous multimodal data would enhance performance, it entails substantial computational and data-related costs. To address these challenges, we propose a novel policy composition method: by leveraging multiple pre-trained DPs based on individual visual modalities, we can combine their distributional scores to form a more expressive Modality-Composable Diffusion Policy (MCDP), without the need for additional training. Through extensive empirical experiments on the RoboTwin dataset, we demonstrate the potential of MCDP to improve both adaptability and performance. This exploration aims to provide valuable insights into the flexible composition of existing DPs, facilitating the development of generalizable cross-modality, cross-domain, and even cross-embodiment policies. Our code is open-sourced at https://github.com/AndyCao1125/MCDP.

Existing group fairness notions require algorithms to treat Fairness has become a crucial aspect in the development different groups equally, and the degree of fairness violation of trustworthy machine learning algorithms. is usually measured via the dissimilarity of model Current fairness metrics to measure predictions. For example, Demographic Parity (DP) requires the violation of demographic parity have the following model predictions to be independent of sensitive attributes drawbacks: (i) the average difference of (Dwork et al., 2012; Kamishima et al., 2012; Jiang model predictions on two groups cannot reflect et al., 2020). To measure the violation of DP, most of existing their distribution disparity, and (ii) the overall calculation works adopt DP metric, which calculates the difference along all possible predictions conceals in average predictions between the two demographic the extreme local disparity at or around certain groups (Zemel et al., 2013; Chuang & Mroueh, 2021; Li predictions. In this work, we propose a novel et al., 2023b). However, since having the same values in fairness metric called Maximal Cumulative ratio average predictions between the two groups cannot ensure Disparity along varying Predictions' neighborhood that the distributions are also the same, we argue that the (MCDP), for measuring the maximal local widely used DP may fail to detect the violation of demographic disparity of the fairness-aware classifiers.