AttenA+: Rectifying Action Inequality in Robotic Foundation Models

Figure 4: Qualitative comparison of task execution with/without AttenA+. (a) The original baseline fails due to accumulated errors in slow, critical manipulation steps (clip, align, release), which receive equal loss weight to fast transitional motions. (b) AttenA+ prioritizes these high-precision segments with larger attention weights, leading to stable task completion.

Figure 5: Overview of experimental tasks. I. Simulation: (a) Four Libero benchmark tasks; (b) 50 diverse RoboTwin tasks, including clean and randomized environments. II. Real-world experiments on Franka Panda: (a)–(d) Four representative tasks (drawer opening, pick-and-place, multi-objects, and sequential manipulation), showing AttenA+ enhanced policy execution.

Figure 6: Real robot experimental results on Franka (Each task is tested over 50 trials): (a) Quantitative success rates (%); (b) Qualitative performance visualization.

Figure 11: Third-person views of example Libero manipulation tasks. Frames labeled ‘critical’ highlight slow, high-precision actions (e.g., grasping, alignment) where AttenA+ applies increased attention weights to improve task success.

Figure 1: Overview of AttenA+ . AttenA+ is a paradigm-agnostic enhancement framework for action robotic foundation models, introducing velocity-field-based action attention to prioritize slow, critical manipulation steps. It seamlessly plugs into mainstream discriminative (e.g., OpenVLA-OFT) and generative ( \pi_{0} , \pi_{0.5} , Diffusion Policy) architectures, as well as emerging World-Action Models (WAM). Without modifying core backbones or relying on data/model scaling, AttenA+ generalizes across diverse robotic datasets including Libero Liu et al. ( 2023 ) and RoboTwin Chen et al. ( 2025

Figure 2: Analysis of velocity fields revealing the inherent action inequality. We observe that the informational density of the robot dataset is non-uniformly distributed : rapid motions are often redundant transitions, while slow-motion phases dominate task success or failure. The discovery of this kinematic hierarchy motivates the development of AttenA+, a plug-and-play mechanism designed to rectify the uniform weighting bias in current robotic foundation models.

Figure 3: Overview of AttenA+. Given visual and language observations from datasets, we derive a velocity field. With attention weighting function F_{A} , this field assigns higher attention weights to slow, critical manipulation steps and lower weights to fast transitional motions, prioritizing learning on error-sensitive actions while training the models.