Differentiable Robot Rendering (2410.13851v1)

Abstract: Vision foundation models trained on massive amounts of visual data have shown unprecedented reasoning and planning skills in open-world settings. A key challenge in applying them to robotic tasks is the modality gap between visual data and action data. We introduce differentiable robot rendering, a method allowing the visual appearance of a robot body to be directly differentiable with respect to its control parameters. Our model integrates a kinematics-aware deformable model and Gaussians Splatting and is compatible with any robot form factors and degrees of freedom. We demonstrate its capability and usage in applications including reconstruction of robot poses from images and controlling robots through vision LLMs. Quantitative and qualitative results show that our differentiable rendering model provides effective gradients for robotic control directly from pixels, setting the foundation for the future applications of vision foundation models in robotics.

• The paper introduces Dr. Robot, a differentiable rendering model that connects pixel-level visual data with control parameters to optimize robotic pose estimation.
• It employs Gaussian splatting, implicit linear blend skinning, and pose-conditioned appearance deformation to accurately model robot geometry and motion.
• Experimental results reveal improved joint angle accuracy and visual fidelity, enabling advanced tasks like text-guided pose estimation and visual motion retargeting.

Differentiable Robot Rendering: Bridging Visual Data and Robotic Control

The paper "Differentiable Robot Rendering" presents an innovative approach to integrating vision foundation models with robotic control tasks. The authors introduce a method termed Dr. Robot, which represents a robot's self-embodiment through a differentiable framework from visual appearance to control parameters. This advancement addresses the modality gap hindering the application of vision models to robotics tasks.

Core Contributions

The principal contribution of this paper is the development of a differentiable rendering model that connects pixel data with action parameters. This integration enables optimization through image gradients. The model incorporates three essential components:

1. Gaussians Splatting: Utilized to model a robot's geometry and texture in a canonical pose. This method supports a differentiable rasterizer for image rendering from varied viewpoints.
2. Implicit Linear Blend Skinning (LBS): Adapts traditional LBS to work with Gaussian splatting, facilitating accurate projection of 3D Gaussians for diverse poses via differentiable forward kinematics.
3. Pose-Conditioned Appearance Deformation: Models the visual changes a robot undergoes across various poses, altering spherical harmonics, scale, opacity, and covariance matrices accordingly.

Experimental Validation

The paper details extensive experiments validating the model's capability. The robot pose reconstruction from in-the-wild videos demonstrated superiority over previous state-of-the-art methods, indicated by a significant improvement in joint angle estimation accuracy. The model's PSNR and Chamfer distance metrics further highlight its visual and geometric fidelity across multiple robotic systems.

Applications and Implications

Beyond accurate modeling, the differentiable nature of Dr. Robot enables novel applications:

• Text to Robot Pose with CLIP: By optimizing joint angles to align rendered images with text prompt similarities, it showcases direct integration with vision-LLMs.
• Text to Action Sequences Using Generative Video Models: Allows for the extraction of robot actions informed by videos predicted from text prompts, fostering new avenues for robotic planning.
• Visual Motion Retargeting: Establishes motion transfer capabilities by matching tracked point trajectories from video demonstrations, thus bypassing traditional kinematic requirements.

Practical and Theoretical Implications

The implications of this research stretch across both theoretical understanding and practical applications in robotics. The differentiable nature of the proposed model facilitates highly efficient computation of control signals directly from visual data. This is particularly significant given the expanding capabilities of visual models in spatial reasoning. Dr. Robot thus serves as an effective interface between these models and robotic control.

Future Directions

Potential future work highlighted includes addressing environmental lighting via adaptive lighting models and integrating differentiable physics to simulate physical interactions with higher precision. These enhancements promise to further bridge the gap between simulation and real-world applications.

In conclusion, this paper presents significant methodological advancements in the intersection of differentiable rendering and robotics, paving the way for more seamless integration of visual learning models with robotic systems. As visual models continue to advance, the applicability of Dr. Robot is poised to expand, making it an influential tool in the field of robotic learning and control.