Visual and action data are interconnected in robotic tasks, forming a perception-action loop. Robots rely on control parameters for movement, while VFMs excel at processing visual data. However, there is a modality gap between visual and action data that arises from fundamental differences in their sensory modalities, levels of abstraction, temporal dynamics, contextual dependence, and susceptibility to noise. These differences make it difficult to directly relate visual perception to action control, requiring intermediate representations or learning algorithms to close the gap. Currently, robots are represented by geometric primitives such as triangular meshes, and kinematic structures describe their morphology. While VFMs provide generalizable control signals, passing these signals to robots has been a challenge.
Researchers from Columbia University and Stanford University proposed “Dr. Robot”, a differentiable robot rendering method that integrates Gaussian splattering, implicit linear blend skinning (LBS), and pose-conditioned appearance deformation to enable differentiable robot control. The key innovation is the ability to calculate gradients from robot images and transfer them to action control parameters, making it compatible with various robot shapes and degrees of freedom. This method allows robots to learn actions from VFMs, bridging the gap between visual inputs and control actions, which was previously difficult to achieve.
The main components of Dr. Robot include Gaussian splatter to model the appearance and geometry of the robot in a canonical pose and implicit LBS to adapt this model to different poses of the robot. The robot's appearance is represented by a set of 3D Gaussians, which morph and deform depending on the robot's pose. A differentiable direct kinematics model allows these changes to be tracked, while a deformation function adapts the appearance of the robot in real time. This method produces high-quality gradients for learning robotic control from visual data, as demonstrated by the superior performance of state-of-the-art robot posture reconstruction tasks and robot action planning via VFM. In several evaluation experiments, Dr. Robot shows improved accuracy in reconstructing robot poses from videos and outperforms existing methods by more than 30% in estimating joint angles. The framework is also demonstrated in applications such as robot action planning using language cues and motion retargeting.
In conclusion, the research presents a robust solution for controlling robots using basic visual models by developing a fully differentiable robot representation. Dr. Robot serves as a bridge between the visual world and the robotic action space, allowing effective planning and control directly from images and pixels. By creating an efficient and flexible method that integrates forward kinematics, Gaussians Splatting, and implicit LBS, this paper establishes a new foundation for the use of vision-based learning in robotic control tasks.
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Pragati Jhunjhunwala is a Consulting Intern at MarktechPost. He is currently pursuing his B.tech from the Indian Institute of technology (IIT), Kharagpur. She is a technology enthusiast and has a keen interest in the scope of data science software and applications. You are always reading about the advancements in different fields of ai and ML.
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