Hindsight relabeling usually turns achieved future states into exact goals, which can overconstrain offline robot learning when task success depends only on a subset of the state. We propose Goal-Set Hindsight Relabeling (GS-HER), a predicate-level generalization of HER in which achieved states certify query-defined goal sets rather than singleton goal states. A binary query specifies which variables define success, making the goal predicate an inference-time input while leaving the underlying offline GCRL algorithm unchanged. Across OGBench tasks and five offline goal-conditioned learners, GS-HER improves performance when full-state goals are bottlenecked by nuisance dimensions and turns hindsight relabeling into a reusable goal interface: one checkpoint can answer multiple robot goal predicates without retraining.

翻译：后见重标定通常将未来实现的状态转化为精确目标，这在任务成功仅取决于状态子集时可能过度约束机器人离线学习。我们提出目标集后见重标定（GS-HER），这是HER在谓词层面上的泛化：实现的状态可验证查询定义的目标集，而非单一目标状态。二元查询指定哪些变量决定成功，使目标谓词成为推理时输入，同时保持底层离线GCRL算法不变。在OGBench任务与五种离线目标条件学习器上的实验表明，当全状态目标受无关维度瓶颈制约时，GS-HER能提升性能，并将后见重标定转变为可复用的目标接口：单个检查点无需重新训练即可响应多个机器人目标谓词。