Foundation and Small Models Coordination for Visuomotor Policy Learning
Authors: Haoran Ding, Liang Ma, Yaxun Yang, Wen Yang, Tianyu Liu, Xiaodan Liang, Dezhen Song, Ivan Laptev, +2 more
Organizations: Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE
Abstract
Visuomotor policy learning enables robots to perform a wide range of tasks, but small policy models often remain sensitive to changes in object and background appearance. In this work, we investigate the coordination of pretrained vision foundation models with small policy models to improve appearance generalization. We propose a framework in which a small policy model operates on task-relevant visual observations constructed through semantic repainting. A segmentation foundation model identifies the robot and target object, which are rendered with fixed role colors on a constant background. An alternative representation replaces the target's role color with normalized monocular depth predicted by a depth foundation model, providing additional geometric cues. The perception models are adapted using in-distribution data where needed and held fixed during policy training. This design combines the perceptual capabilities of foundation models with a small policy model trained on the resulting observations for action prediction. Evaluations with flow matching policies on simulation benchmarks, together with experiments on two real-world robotic tasks, demonstrate substantial improvements in task success under the evaluated appearance shifts.
End-to-end visuomotor policies provide little opportunity for humans to understand or correct the policy's visual attention. We propose GuidedAttention, a visuomotor imitation learning framework that introduces interpretable and correctable visual attention as an explicit intermediate representation. Task-relevant attention keypoints are predicted from camera images and condition a diffusion-based action policy. Users can inspect and optionally correct selected keypoints once at rollout initialization, after which the corrected attention is automatically propagated throughout execution by a tracking module. Experiments in simulation and the real world demonstrate that GuidedAttention consistently improves robot manipulation performance, particularly under positional and appearance out-of-distribution (OOD) conditions. https://mmurooka.github.io/guided-attention-project-page
Recent advances in robot imitation learning have produced powerful visuomotor policies that manipulate diverse objects from visual inputs. However, monocular observations lack depth information, which is critical for precise manipulation in cluttered or geometrically complex scenes. Explicit depth maps and point clouds are often noisy and fragile in real-world manipulation. We introduce StereoPolicy, a visuomotor policy learning framework that directly leverages synchronized stereo image pairs to improve geometric reasoning without constructing explicit 3D representations. StereoPolicy processes each image with pretrained 2D vision encoders and fuses left-right features through a cross-attention-based Stereo Transformer, capturing spatial correspondence and disparity cues implicitly. The framework integrates with diffusion-based and pretrained vision-language-action (VLA) policies, delivering consistent improvements over RGB, RGB-D, point cloud, and multi-view baselines across three simulation benchmarks and seven real-robot tabletop and bimanual mobile manipulation tasks. Our results show that stereo vision bridges 2D pretrained representations and 3D geometric understanding for robotic manipulation.
Visuomotor manipulation policies trained via large-scale behavior cloning have achieved strong semantic scene understanding, yet often fail to reliably execute correct low-level actions under distribution shifts. For example, even in a simple pickup task with identical scene layouts, camera viewpoints, and illumination, performance can degrade substantially when the object is placed at unseen locations. We argue that this gap arises from insufficient action understanding, namely the inability to interpret the robot's base-frame action coordinate system in image space. To address this issue, we introduce AxisGuide, a lightweight guidance method that bridges semantic scene understanding and action-coordinate interpretation. Using camera parameters and end-effector poses, AxisGuide renders the robot base-frame axes in each camera view and augments RGB observations with a small set of cue channels that explicitly visualize the meaning of the +x, +y, and +z motions in image space. Extensive evaluations in both the LIBERO simulation and real-world environments demonstrate that AxisGuide yields substantial performance gains and improved generalization, highlighting the effectiveness of explicit action-coordinate cues for learning reliable and transferable generalist visuomotor policies.