Revolution in Robot Hand Control: PoseLess Enables Depth-Free Image-to-Joint Mapping

The control of robot hands has made considerable progress in recent years. A new approach, known as PoseLess, now promises to significantly reduce the complexity of this control. Traditional methods often require elaborate pose estimation to determine the hand's position and orientation in space. PoseLess bypasses this step by mapping images directly to joint angles. This allows for more direct and efficient control, which is particularly beneficial for real-time applications.

How PoseLess Works

The core of PoseLess is a transformer-based decoder that works with projected visual inputs. Instead of reconstructing the three-dimensional pose of the hand, the system analyzes two-dimensional images and directly derives the corresponding joint angles. This innovative approach significantly simplifies the control process and reduces latency. Another advantage lies in the robustness against depth ambiguity, a known problem in image processing. Since PoseLess does not require depth information, it is less susceptible to errors caused by inaccurate depth estimations.

Synthetic Training Data and Zero-Shot Generalization

A remarkable feature of PoseLess is the use of synthetic training data. By generating data from randomized joint configurations, the system can cover a wide variety of hand poses and movements. This enables so-called "zero-shot generalization," meaning that PoseLess also works in real-world scenarios without prior training with real data. This aspect is particularly important for application in unpredictable environments.

Cross-Morphology Transfer: From Robot to Human Hand

The flexibility of PoseLess is also demonstrated by the possibility of cross-morphology transfer. The system can be used not only for controlling robot hands, but also for analyzing human hand movements. This transferability opens up new possibilities in areas such as human-robot interaction and medical rehabilitation.

Experimental Results and Outlook

Initial experimental results confirm the effectiveness of PoseLess. The system achieves competitive accuracy in predicting joint angles without relying on manually labeled datasets. This underscores the potential of PoseLess to advance the development of efficient and robust robot hand controls. Future research could focus on further optimizing the system and exploring new application areas. The direct mapping of images to joint angles offers a promising foundation for the next generation of robot hand controls.

Bibliography: - Dao, A., Vu, D. B., Anh, T. L. D., & Huy, B. Q. (2025). PoseLess: Depth-Free Vision-to-Joint Control via Direct Image Mapping with VLM. arXiv preprint arXiv:2503.07111.