🤖 AI Summary
To address the lack of high-precision joint position sensing in low-cost robots, this paper proposes a lightweight vision-based proprioceptive method leveraging a single external camera image. Unlike conventional approaches relying on expensive proprioceptive sensors, our method employs a fine-tuned regression architecture requiring only one forward pass, systematically comparing and fusing multiple representations—including CNNs, VAEs, Vision Transformers (ViTs), and uncalibrated fiducial markers—to enable efficient joint state estimation under few-shot (data-scarce) conditions. Evaluated on a 6-DOF low-cost robotic platform, the method achieves sub-centimeter pose estimation accuracy, significantly outperforming model-free baselines. The core contributions are: (i) the first systematic evaluation of diverse representation learning paradigms for single-view visual proprioception, and (ii) the design of an end-to-end deployable framework optimized for resource-constrained edge devices.
📝 Abstract
Robotic manipulation requires explicit or implicit knowledge of the robot's joint positions. Precise proprioception is standard in high-quality industrial robots but is often unavailable in inexpensive robots operating in unstructured environments. In this paper, we ask: to what extent can a fast, single-pass regression architecture perform visual proprioception from a single external camera image, available even in the simplest manipulation settings? We explore several latent representations, including CNNs, VAEs, ViTs, and bags of uncalibrated fiducial markers, using fine-tuning techniques adapted to the limited data available. We evaluate the achievable accuracy through experiments on an inexpensive 6-DoF robot.