用目标集合替代单一目标，让机器人学习更灵活高效
Goal Sets, Not Goal States: Queryable Robot Goals through Goal-Set Hindsight Relabeling
- 用二元查询定义成功条件，将目标从状态变为可查询的集合
- 在多个任务上提升性能，尤其当部分状态干扰学习时效果显著
- 同一模型可响应多种目标查询，无需重新训练
传统回溯重标注将达成的未来状态直接设为唯一目标，当任务成功仅依赖状态子集时，这会过度约束离线机器人学习。我们提出目标集回溯重标注（GS-HER），在谓词层面推广HER：达成状态仅需满足预定义的目标集合而非单一状态。通过二元查询指定成功所依赖的状态变量，使目标谓词成为推理时的输入，同时保持底层离线目标条件强化学习算法不变。在OGBench任务及五种离线目标条件学习器上，当全状态目标受无关维度干扰时，GS-HER显著提升性能，并将回溯重标注转化为可复用的目标接口——单个检查点即可响应多种机器人目标查询而无需重训。
原文摘要 · Abstract (English)
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.
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