VLA-MOPD: On-Policy Distillation for Multi-Task VLA Policy Consolidation
Overview
This work introduces VLA-MOPD, a method for consolidating multi-task Vision-Language-Action policies through on-policy distillation. The approach uses frozen instruction-routed teachers queried at each visited observation and intermediate ODE state to supervise action chunks via masked local matching loss, with an optional reference-policy term to retain skills across tasks. Starting from one trajectory per LIBERO task, the method raises mean success-once from 75.4% to 98.9%, compared with 98.15% for endpoint action distillation. It improves mean RoboTwin 2.0 success from 73.0% to 89.3%; on a Piper robot, two consolidation rounds improve average success from 70.0% to 90.0%. In a near-matched Spatial-adaptation study, the reference term limits the Object suite decrease to 3.0 points versus 13.2 without it.
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Reported quantity · Object suite decrease with reference matching: 3 other · Basis not reported
“an RoboTwin 2.0 success from 73.0% to 89.3%; on a Piper robot, two consolidation rounds improve average success from 70.0% to 90.0%. In a near-matched Spatial-adaptation study, reference matching limits the Object suite decrease to 3.0 points, versus 13.2 without the reference term.”
Exact source · revision 1Source owner not reported
Reported quantity · Object suite decrease without reference term: 13.2 other · Basis not reported
“an RoboTwin 2.0 success from 73.0% to 89.3%; on a Piper robot, two consolidation rounds improve average success from 70.0% to 90.0%. In a near-matched Spatial-adaptation study, reference matching limits the Object suite decrease to 3.0 points, versus 13.2 without the reference term.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalVLA-MOPD: On-Policy Distillation for Multi-Task VLA Policy Consolidation
This work introduces VLA-MOPD, a method for consolidating multi-task Vision-Language-Action policies through on-policy distillation. The approach uses frozen instruction-routed teachers queried at each visited observation and intermediate ODE state to supervise action chunks via masked local matching loss, with an optional reference-policy term to retain skills across tasks. Starting from one trajectory per LIBERO task, the method raises mean success-once from 75.4% to 98.9%, compared with 98.15% for endpoint action distillation. It improves mean RoboTwin 2.0 success from 73.0% to 89.3%; on a Piper robot, two consolidation rounds improve average success from 70.0% to 90.0%. In a near-matched Spatial-adaptation study, the reference term limits the Object suite decrease to 3.0 points versus 13.2 without it.
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