Back to the Familiar Future: Failure Recovery for VLA Policies via Pre-Imagined Milestone Selection
Overview
This paper introduces Back to the Familiar Future (B2FF), a recovery framework for vision-language-action (VLA) policies that uses pre-imagined milestone states conditioned on clean initial observations. At runtime, a recoverability-aware selector chooses a familiar future state from a generated bank and enforces it as a fixed visual goal to guide the policy back from off-trajectory observations. Evaluated on failure-injected LIBERO with recovery timing aligned to injected failures, B2FF increases the average success rate of a baseline VLA from 56.3% to 74.0% without fine-tuning the low-level action generator.
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Reported quantity · Reported success rate: 56.3 percent · reported trials · Differing source assertions
“ations back to a familiar future. On failure-injected LIBERO, under controlled recovery timing aligned with the injected failure, B2FF increases the average success rate of a baseline VLA from 56.3% to 74.0%, demonstrating that pre-imagined milestones can guide recovery without fine-tuning the low-level action generator.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 74 percent · reported trials · Differing source assertions
“ations back to a familiar future. On failure-injected LIBERO, under controlled recovery timing aligned with the injected failure, B2FF increases the average success rate of a baseline VLA from 56.3% to 74.0%, demonstrating that pre-imagined milestones can guide recovery without fine-tuning the low-level action generator.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalBack to the Familiar Future: Failure Recovery for VLA Policies via Pre-Imagined Milestone Selection
This paper introduces Back to the Familiar Future (B2FF), a recovery framework for vision-language-action (VLA) policies that uses pre-imagined milestone states conditioned on clean initial observations. At runtime, a recoverability-aware selector chooses a familiar future state from a generated bank and enforces it as a fixed visual goal to guide the policy back from off-trajectory observations. Evaluated on failure-injected LIBERO with recovery timing aligned to injected failures, B2FF increases the average success rate of a baseline VLA from 56.3% to 74.0% without fine-tuning the low-level action generator.
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