Imitation learning (IL) with generative models, such as diffusion and flow matching,
has enabled robots to perform complex, long-horizon tasks. However, distribution
shifts from unseen environments or compounding action errors can still cause
unpredictable and unsafe behavior, leading to task failure. Early failure prediction during runtime is therefore essential for deploying robots in human-centered
and safety-critical environments. We propose FIPER, a general framework for
Failure Prediction at Runtime for generative IL policies that does not require
failure data. FIPER identifies two key indicators of impending failure: (i) out-of-distribution (OOD) observations detected via random network distillation in the
policy’s embedding space, and (ii) high uncertainty in generated actions measured
by a novel action-chunk entropy score. Both failure prediction scores are calibrated
using a small set of successful rollouts via conformal prediction. A failure alarm
is triggered when both indicators, aggregated over short time windows, exceed
their thresholds. We evaluate FIPER across five simulation and real-world environments involving diverse failure modes. Our results demonstrate that FIPER better
distinguishes actual failures from benign OOD situations and predicts failures
more accurately and earlier than existing methods. We thus consider this work
an important step towards more interpretable and safer generative robot policies.