5 accepted papers
This work presents DemoBot, a learning framework that enables a dual-arm, multi-finger robotic system to acquire complex manipulation skills from a single unannotated RGB-D video demonstration. The method extracts structured motion trajectories of both hands and objects from raw video data. These tr…
Achieving human-like dexterous manipulation is essential for general-purpose robots but remains a challenge. Recent advances in Vision-Language-Action (VLA) models offer the potential to learn flexible skills from demonstration data. However, training effective VLAs requires a large amount of high-q…
This paper proposes a novel method, named MagShield, designed to address the issue of magnetic disturbances in sparse inertial motion capture (MoCap) systems. Existing Inertial Measurement Units (IMUs) are prone to orientation estimation errors in magnetically disturbed environments, limiting the pr…
Existing wearable motion capture methods typically demand tight on-body fixation (often using straps) for reliable sensing limiting their application in everyday life. In this paper we introduce Loose Inertial Poser a novel motion capture solution with high wearing comfortableness by integrating fou…
Motion capture from sparse inertial sensors has shown great potential compared to image-based approaches since occlusions do not lead to a reduced tracking quality and the recording space is not restricted to be within the viewing frustum of the camera. However, capturing the motion and global posit…