Daniel Rakita

In robotics, it is common to check whether a given robot state results in self-intersection (i.e., a self-collision query) or to assess its distance from such an intersection (i.e., a self-proximity query). These checks are typically performed between pairs of shapes attached to different robot links. However, many of these shape pairs can be excluded in advance, as their configurations are known to always or never result in contact. This information is typically encoded in a self-collision matrix, where each entry (i, j) indicates whether a check should be performed between shape i and shape j. While the MoveIt Setup Assistant is widely used to generate such matrices, current tools are limited by static visualization, lack of proximity support, rigid single-geometry assumptions, and tedious refinement workflows, hindering flexibility and reuse in downstream robotics applications. In this work, we introduce an interactive tool that overcomes these limitations by generating and visualizing self-collision matrices across multiple shape representations, enabling dynamic inspection, filtering, and refinement of shape pairs. Outputs are provided in both JSON and YAML for easy integration. The system is implemented in Rust and uses the Bevy game engine to deliver high-quality visualizations. We demonstrate its effectiveness on multiple robot platforms, showing that matrices generated using diverse shape types yield faster and more accurate self-collision and self-proximity queries.

In robotics, it is common to check whether a given robot state results in self-intersection (i.e., a self-collision query) or to assess its distance from such an intersection (i.e., a self-proximity query). These checks are typically performed between pairs of shapes attached to different robot links. However, many of these shape pairs can be excluded in advance, as their configurations are known to always or never result in contact. This information is typically encoded in a self-collision matrix, where each entry (i, j) indicates whether a check should be performed between shape i and shape j. While the MoveIt Setup Assistant is widely used to generate such matrices, current tools are limited by static visualization, lack of proximity support, rigid single-geometry assumptions, and tedious refinement workflows, hindering flexibility and reuse in downstream robotics applications. In this work, we introduce an interactive tool that overcomes these limitations by generating and visualizing self-collision matrices across multiple shape representations, enabling dynamic inspection, filtering, and refinement of shape pairs. Outputs are provided in both JSON and YAML for easy integration. The system is implemented in Rust and uses the Bevy game engine to deliver high-quality visualizations. We demonstrate its effectiveness on multiple robot platforms, showing that matrices generated using diverse shape types yield faster and more accurate self-collision and self-proximity queries.

High-quality visualizations are an essential part of robotics research, enabling clear communication of results through figures, animations, and demonstration videos. While Blender is a powerful and freely available 3D graphics platform, its steep learning curve and lack of robotics-focused integrations make it difficult and time-consuming for researchers to use effectively. In this work, we introduce a lightweight software library that bridges this gap by providing simple scripting interfaces for common robotics visualization tasks. The library offers three primary capabilities: (1) importing robots and environments directly from standardized descriptions such as URDF; (2) Python-based scripting tools for keyframing robot states and visual attributes; and (3) convenient generation of primitive 3D shapes for schematic figures and animations. Together, these features allow robotics researchers to rapidly create publication-ready images, animations, and explanatory schematics without needing extensive Blender expertise. We demonstrate the library through a series of proof-of-concept examples and conclude with a discussion of current limitations and opportunities for future extensions.

High-quality visualizations are an essential part of robotics research, enabling clear communication of results through figures, animations, and demonstration videos. While Blender is a powerful and freely available 3D graphics platform, its steep learning curve and lack of robotics-focused integrations make it difficult and time-consuming for researchers to use effectively. In this work, we introduce a lightweight software library that bridges this gap by providing simple scripting interfaces for common robotics visualization tasks. The library offers three primary capabilities: (1) importing robots and environments directly from standardized descriptions such as URDF; (2) Python-based scripting tools for keyframing robot states and visual attributes; and (3) convenient generation of primitive 3D shapes for schematic figures and animations. Together, these features allow robotics researchers to rapidly create publication-ready images, animations, and explanatory schematics without needing extensive Blender expertise. We demonstrate the library through a series of proof-of-concept examples and conclude with a discussion of current limitations and opportunities for future extensions.