Robotic hands can have very different numbers of joints yet face similar problems: hold an object, turn it and coordinate both wrists and fingers. Xiaoyu Yang and Nan Wu propose AcrossDEX 1.0, a pipeline combining human-motion pretraining, simulation^[1]-based expert learning, robot demonstration collection and visual feedback^[2] during execution.

During pretraining, the system predicts one second of bimanual motion from five images and thirty annotated hand states recorded over the preceding second. Shared wrist representations are retained when adapting to robots, while finger control accounts for the mechanics of each hand. The researchers tested six-channel Inspire hands and sixteen-joint Allegro hands, with control predictions revised from new camera observations.

In their evaluations, direct regression achieved 40.6% success on opening and returning a lid, 100% on Inspire object reorientation and 93.3% on Allegro reorientation. Flow matching achieved 90% on Allegro reorientation. These figures describe different tasks and conditions, not an overall ranking of robotic hands. Constraining new plans with previous plans can improve continuity.

The engineering value lies in reproducibility rather than a headline number alone. A replication needs the hardware configuration, training data, evaluation protocol, control frequency and stopping conditions. Comparisons should use consistent compute budgets and attempt counts. Rare failures deserve separate analysis rather than disappearing into an average. Until an independent team reproduces the result on another setup, an improvement is evidence within the authors' experimental conditions, not a universal property of the method. Shared wrist representations and hand-specific finger control support transfer studies. Results still depend on the object and control method.