AxisGuide: Grounding Robot Action Coordinate System in RGB Observations for Robust Visuomotor Manipulation

Researchers introduce AxisGuide, a lightweight method that improves robot manipulation by explicitly visualizing action coordinates in camera views. The technique augments visual observations with cues showing robot base-frame axes, enabling better generalization when objects are placed in unseen locations despite identical scene layouts.

AxisGuide addresses a fundamental challenge in visuomotor robotics: the gap between semantic scene understanding and reliable action execution. While large-scale behavior cloning has enabled robots to understand visual scenes effectively, performance degrades dramatically when objects appear in novel positions—even in controlled environments with identical lighting and camera angles. This disconnect reveals that current models struggle to map visual information to the robot's action coordinate system in image space.

The solution leverages readily available information: camera parameters and end-effector poses. By rendering the robot's base-frame axes (+x, +y, +z) as visual cues overlaid on RGB observations, AxisGuide makes the relationship between image space and action space explicit. This bridges the semantic-to-action interpretation gap without requiring architectural changes or additional training data, making it a practical lightweight augmentation suitable for deployment.

The implications extend beyond academic robotics. Improved generalization in manipulation tasks directly impacts scalability of robotic systems in warehouses, manufacturing, and service industries where objects vary in position. The method's effectiveness in both simulation (LIBERO) and real-world environments suggests genuine transfer potential rather than benchmark gaming. For developers building robotic systems, this represents a simple yet effective technique to improve reliability under distribution shifts—a critical requirement for autonomous systems in uncontrolled environments.

Future work should explore whether similar coordinate-visualization approaches apply to other embodied AI tasks involving different action spaces, such as navigation or multi-arm coordination. The core insight—that explicit visualization of abstract action spaces improves learning—may generalize beyond manipulation.

• →AxisGuide visualizes robot action coordinates in image space, bridging the gap between visual understanding and reliable action execution
• →The method augments RGB observations with lightweight cue channels showing +x, +y, +z axis meanings without architectural changes
• →Performance improves substantially even in controlled environments when objects shift to unseen locations, addressing a critical failure mode
• →Real-world validation demonstrates the technique transfers beyond simulation, indicating practical deployment potential
• →The approach highlights that explicit action-coordinate cues are essential for learning transferable visuomotor policies