Chunk-Aligned Recovery Synthesis for Robust Generative Control Policies

Generative control policies, such as diffusion and flow-matching policies, have demonstrated strong performance in behavior cloning from multimodal demonstra- tions, but remain susceptible to covariate shift: small action errors accumulate over time, driving the policy into states outside the demonstration distribution, where its predictions become unreliable. Existing corrective-augmentation methods address this issue by synthesizing recovery data as isolated transitions. Such transitions are poorly suited to action-chunking policies, which are trained to produce temporally coherent sequences rather than individual corrective steps. We propose ReSynC (Reciprocal Synthesis and Cycle Consistency), which synthesizes recovery behav- ior in a chunk-aligned form: a truncated backward diffusion policy and a backward dynamics model generate a plausible deviation together with its recovery, and this segment replaces the prefix of an expert action chunk, so that the resulting training sample starts from an off-distribution state and ends in expert behavior. Chunk-aligned placement and an expressive backward generator interact: neither provides a consistent benefit without the other. At inference time, ReSynC reuses these dynamics models to score candidate action chunks by their cycle-consistency error and contrastively selects among the low-error candidates. We find that this score reflects whether a candidate remains within the data distribution used to fit the dynamics, rather than how accurate those dynamics are, and that this signal remains reliable under limited demonstrations. Across five simulated manipulation tasks and a physical robot arm, ReSynC improves average robustness under limited demonstrations without additional human supervision or environment interaction.