Reliability-Aware Future Conditioning for Temporally Robust Robot Manipulation

Key points

• A generated video of a task the robot is about to perform is useful guidance only if it depicts the phase the robot is actually in.1
• We introduce Reliability-Aware Future Conditioning (RAFC), which treats this as a control problem rather than a generation problem.1
• At every step, RAFC estimates how far to trust the received clip and which nearby temporal hypothesis to prefer, falling back toward a static branch when neither fits, and it learns both from task reward alone without shift labels or alignment supervision.1
• Under deliberately off-grid phase shifts and rate mismatch, RAFC substantially improves success under temporal mismatch.1

Sources (1)

• [1]Reliability-Aware Future Conditioning for Temporally Robust Robot Manipulation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 01:40 PM

  We show that temporal misalignment can turn a task-consistent generated future into actively harmful guidance.

  A generated video of a task the robot is about to perform is useful guidance only if it depicts the phase the robot is actually in.

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