让机器人动态决定何时继续或重规划，提升复杂操作成功率。
Continue or Replan? Bernoulli-Continuation Policy Learning for Adaptive Horizon Execution
- 用伯努利机制决定每段动作是否继续或重规划，实现自适应执行时长。
- 在50个任务上成功率从89.88%提升至93.94%，复杂任务提升11.08%。
- 轻量插件式设计，可适配不同模型，在真实机器人上显著提效。
现有基于块的视觉-语言-动作（VLA）模型在固定动作数后才重规划，导致关键操作阶段可能使用过时策略。为此，我们提出伯努利延续策略（BCP），一种轻量、即插即用的自适应执行时长框架，保持基础VLA不变。给定固定长度的动作块，其延续头将执行时长选择分解为一系列继续或重规划的决策，引入有序前缀共享归纳偏置，而非独立分类。由于每个块的最佳时长不可观测，我们通过轨迹级结果进行强化学习训练，并引入重规划效率奖励，同时奖励任务成功与高效使用VLA，防止策略退化为过短时长。在RoboTwin 2.0上，以LingBot-VLA为基础策略，BCP使13个低成功率任务平均提升11.08%，全部50个任务成功率从89.88%升至93.94%（+4.06%）。虽仅在洁净设置下训练，仍可泛化至随机设置，平均成功率提升+4.06%。还可迁移至另一基础策略π_{0.5}，在LIBERO上提升+1.7%，在更难的LIBERO-PRO上提升+6.8%。在真实机器人上，两个操作任务的成功率分别从74%和44%提升至92%和84%。其几乎无额外开销，整体运行时间反而低于固定时长基线。
原文摘要 · Abstract (English)
Existing chunk-based Vision-Language-Action (VLA) models execute a fixed number of actions (i.e., execution horizon) before replanning, turning replanning into a task-agnostic periodic schedule that is independent of task progress. As a result, when no replanning boundary falls before a critical manipulation stage, it is executed from a stale chunk rather than a freshly replanned one. To address this limitation, we propose Bernoulli-Continuation Policy (BCP), a lightweight, plug-and-play framework for adaptive horizon execution that keeps the base VLA frozen. Given a fixed-length action chunk, its continuation head decomposes execution-horizon selection into a sequence of continue-or-replan decisions, which imposes an ordinal, prefix-sharing inductive bias over candidate horizons rather than treating them as independent classes. Since the optimal horizon for each chunk is not observable, we train this head with reinforcement learning from trajectory-level outcomes and introduce a Replanning-Efficiency Reward that jointly rewards task success and efficient VLA usage, discouraging the policy from collapsing to unnecessarily short horizons. On RoboTwin 2.0 with LingBot-VLA as the base policy, BCP improves the average success rate by +11.08% on 13 low-success tasks and from 89.88% to 93.94% (+4.06%) across all 50 tasks. Although trained only under the Clean setting, BCP generalizes to the Randomized setting, raising the average success rate by +4.06%. It also transfers to a different base policy $π_{0.5}$, achieving a better result on LIBERO (+1.7%) and, notably, on the harder LIBERO-PRO (+6.8%). On a real robot, BCP lifts success from 74% to 92% and from 44% to 84% on two manipulation tasks. Meanwhile, its negligible overhead, combined with higher success, makes BCP's overall runtime even lower than the fixed-horizon baselines.
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