Can Astra Solve RoboMME without Breaking the Bank?
Overview
This research introduces a three-tier system for robotic manipulation that addresses memory challenges by using a vision-language planner to select subtasks from stored history, an action policy to execute them, and a monitor to verify completion. On the RoboMME benchmark, the system achieves a 79.13% success rate with an average of 3.63 calls to the large model per episode and 80.84 seconds per episode, outperforming the best non-oracle baseline by 34.62 points.
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Event evidence and corrections
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Reported quantity · average_astra_calls: 3.63 other · Basis not reported
“in 79.13% of episodes with an average of 3.63 Astra calls and 80.84 seconds per episode. This is 34.62 points above the best published non-oracle baseline and 4.95 points below GroundSG+Oracle, which gives the low-level policy ground-truth subgoals.”
Exact source · revision 1Source owner not reported
Reported quantity · average_episode_duration_seconds: 80.84 other · Basis not reported
“in 79.13% of episodes with an average of 3.63 Astra calls and 80.84 seconds per episode. This is 34.62 points above the best published non-oracle baseline and 4.95 points below GroundSG+Oracle, which gives the low-level policy ground-truth subgoals.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalCan Astra Solve RoboMME without Breaking the Bank?
This research introduces a three-tier system for robotic manipulation that addresses memory challenges by using a vision-language planner to select subtasks from stored history, an action policy to execute them, and a monitor to verify completion. On the RoboMME benchmark, the system achieves a 79.13% success rate with an average of 3.63 calls to the large model per episode and 80.84 seconds per episode, outperforming the best non-oracle baseline by 34.62 points.
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