Tsinghua-led team launches HandsOn benchmark to test bimanual robot dexterity across 66 tasks

A research team led by Tsinghua University has introduced HandsOn, a simulation platform and evaluation benchmark designed for cross-embodiment dexterous manipulation. Built with collaborators from Beijing University of Posts and Telecommunications, Peking University, and researchers from Nature Will, the benchmark includes 66 manually validated tasks and 18,000 human teleoperation demonstrations collected in simulation. The setup is aimed at measuring how well robot hands can handle tool use, fine contact, bimanual coordination, and task transfer. HandsOn splits evaluation into Core-50 and Transfer-16. Core-50 provides demonstrations for policy learning and then tests whether a model can reliably execute the learned tasks. Transfer-16 withholds target-task demonstrations and focuses on generalization to unseen objects, skill compositions, and deformable objects. The platform supports five robot arms, six dexterous hands, and about 200 rigid, articulated, and deformable assets. The first round of results was modest. On Core-50, the best trained multitask policy reached a 29.1% average task success rate. On Transfer-16, the average success rate for eight trained multitask policies stayed below 1%. GPT-6, tested without HandsOn fine-tuning and controlled through the move_eef interface, posted success rates of 5.2% on Core-50 and 1.9% on Transfer-16 across 660 trials. The paper, code, and dataset have been released publicly.