Vision–Language–Action (VLA) models have become a dominant paradigm for generalist robot manipulation, yet most are effectively memoryless: they map the current observation and a language instruction to an action without an explicit mechanism for retaining and reusing information across long-horizon, partially observable episodes. This thesis makes two contributions toward memory-augmented VLA policies. First, we unify three heterogeneous VLA backbones—pi0.5, Cosmos, and DreamZero— under a single BasePolicy interface, so the same evaluation and rollout infrastructure applies uniformly to all three. Second, we introduce DynaMem, an input-level, single-point dynamic memory injection mechanism that augments any backbone behind this interface without modifying its internal architecture or requiring backbone-specific engineering. We evaluate the resulting system with closed-loop rollouts on the RoboMemArena (RMA) benchmark. Preliminary experiments indicate that input-level memory injection...
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