aiqbench analysis
This paper introduces B2FF, a framework that enhances Vision-Language-Action (VLA) policies by using pre-generated milestones to recover from execution failures without destabilizing the action sequence. By selecting familiar future states as fixed visual goals, the method improves success rates on failure-injected benchmarks while avoiding the need for fine-tuning low-level generators. This represents a significant step forward in robustness and reliability for embodied AI systems operating in dynamic environments.
Back to the Familiar Future: Failure Recovery for VLA Policies via Pre-Imagined Milestone Selection
arXiv:2606.09258v2 Announce Type: replace Abstract: Vision-language-action (VLA) policies can deviate from nominal trajectories during manipulation, even when tasks remain physically feasible. Recovering from these deviations is challenging, as they push the policy into unfamiliar state spaces where direct re-planning frequently destabilizes action sequences. We propose Back to the Familiar Future (B2FF), a recovery framework for foresight-driven VLAs that leverages future visual conditioning as a recovery interface. Before execution, the VLA generates a milestone bank of familiar future states conditioned on the clean initial observation. At recovery time, a recoverability-aware selector selects a recovery milestone from this bank and enforces it as a fixed visual goal. This enables the VLA to robustly map off-trajectory observations 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.
Abstract:Vision-language-action (VLA) policies can deviate from nominal trajectories during manipulation, even when tasks remain physically feasible. Recovering from these deviations is challenging, as they push the policy into unfamiliar state spaces where direct re-planning frequently destabilizes action sequences. We propose Back to the Familiar Future (B2FF), a recovery framework for foresight-driven VLAs that leverages future visual conditioning as a recovery interface. Before execution, the VLA generates a milestone bank of familiar future states conditioned on the clean initial observation. At recovery time, a recoverability-aware selector selects a recovery milestone from this bank and enforces it as a fixed visual goal. This enables the VLA to robustly map off-trajectory observations 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.
Robotics (cs.RO)
arXiv:2606.09258 [cs.RO]
(or arXiv:2606.09258v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.09258 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 244 words