Game-Enhanced Embodied Learning with Event and Foresight Pretraining
Overview
This research paper introduces a method for enhancing embodied learning using human gameplay data. The authors study event-and-foresight pretraining for the InternVLA-A1.5 model, combining keyboard/mouse event prediction with visual supervision from a frozen video model. Using a corpus of 394 recordings across 20 games totaling 223.36 hours and 2,072,084 training anchors, the study demonstrates that after 12,400 pretraining updates, the model achieves an aggregate success rate increase from 85.90% to 90.55% across four LIBERO suites. The results highlight the practical value of game-pretraining while noting heterogeneous task outcomes and unisolated effects of optimization and training variability.
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Event evidence and corrections
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Reported quantity · training anchors: 2072084 other · Basis not reported
“s, and an attention mask prevents teacher-forced future actions from entering foresight queries. The selected corpus contains 394 recordings across 20 games, with 223.36 hours of recorded timestamp span and 2,072,084 training anchors. After 12,400 game-pretraining updates, we compare the resulting initialization with the public base under the same suite-specific robot fine-tuning protocol. Across four LIBERO suites,”
Exact source · revision 1Source owner not reported
Reported quantity · recorded timestamp span: 223.36 hours · Basis not reported
“s, and an attention mask prevents teacher-forced future actions from entering foresight queries. The selected corpus contains 394 recordings across 20 games, with 223.36 hours of recorded timestamp span and 2,072,084 training anchors. After 12,400 game-pretraining updates, we compare the resulting initialization with the public base under the same suite-specific robot fine-tuning protocol. Across four LIBERO suites,”
Exact source · revision 1Source owner not reported
Reported quantity · game-pretraining updates: 12400 other · Basis not reported
“s, and an attention mask prevents teacher-forced future actions from entering foresight queries. The selected corpus contains 394 recordings across 20 games, with 223.36 hours of recorded timestamp span and 2,072,084 training anchors. After 12,400 game-pretraining updates, we compare the resulting initialization with the public base under the same suite-specific robot fine-tuning protocol. Across four LIBERO suites,”
Exact source · revision 1Source owner not reported
Reported quantity · success count (Spatial): 1718 other · reported trials
“aggregate success increases from 1,718/2,000 (85.90%) to 1,811/2,000 (90.55%), a gain of 4.65 percentage points. Suite gains are 9.2, 6.8, 0.2, and 2.4 points on Spatial, Object, Goal, and Long. The 40-task breakdown shows 24 improvements, 10 declines, and six ties. These results support the practical value of the complete game-pretraining stage in this evaluated setting, while revealing heterogeneous task outcomes.”
Exact source · revision 1Source owner not reported
Reported quantity · success count (Object): 1811 other · reported trials
“aggregate success increases from 1,718/2,000 (85.90%) to 1,811/2,000 (90.55%), a gain of 4.65 percentage points. Suite gains are 9.2, 6.8, 0.2, and 2.4 points on Spatial, Object, Goal, and Long. The 40-task breakdown shows 24 improvements, 10 declines, and six ties. These results support the practical value of the complete game-pretraining stage in this evaluated setting, while revealing heterogeneous task outcomes.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalGame-Enhanced Embodied Learning with Event and Foresight Pretraining
This research paper introduces a method for enhancing embodied learning using human gameplay data. The authors study event-and-foresight pretraining for the InternVLA-A1.5 model, combining keyboard/mouse event prediction with visual supervision from a frozen video model. Using a corpus of 394 recordings across 20 games totaling 223.36 hours and 2,072,084 training anchors, the study demonstrates that after 12,400 pretraining updates, the model achieves an aggregate success rate increase from 85.90% to 90.55% across four LIBERO suites. The results highlight the practical value of game-pretraining while noting heterogeneous task outcomes and unisolated effects of optimization and training variability.
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