让视觉语言动作模型具备长程规划能力，提升机器人任务成功率。
SITCOM: Scaling Inference-Time COMpute for VLAs
- 用学习到的动力学模型进行多步动作模拟，生成候选计划
- 在模拟器中用奖励函数筛选最优路径，任务完成率从48%提至72%
- 适合需要长期推理的机器人控制场景，尤其适用于缺乏标注数据的环境
由于标注数据收集成本高、泛化能力有限以及长时序规划困难，学习鲁棒的机器人控制策略仍是重大挑战。尽管视觉-语言-动作（VLA）模型可通过自然语言指令生成单步控制命令提供解决方案，但通常缺乏前瞻机制，在动态任务中易累积误差。本文提出SITCOM框架，通过引入基于模型的滚动预测与奖励驱动轨迹选择，增强预训练VLA的长程规划能力，其灵感来自模型预测控制。SITCOM利用一个基于Transformer的学习动力学模型，在大规模BridgeV2数据上预训练，并在SIMPLER环境中微调以弥合真实世界到模拟环境的差距；通过模拟器中的奖励评估候选轨迹，选出最优方案执行。在SIMPLER环境的多项任务中，结合良好奖励函数后，任务完成率由48%显著提升至72%。
原文摘要 · Abstract (English)
Learning robust robotic control policies remains a major challenge due to the high cost of collecting labeled data, limited generalization to unseen environments, and difficulties in planning over long horizons. While Vision-Language-Action (VLA) models offer a promising solution by grounding natural language instructions into single-step control commands, they often lack mechanisms for lookahead and struggle with compounding errors in dynamic tasks. In this project, we introduce Scaling Inference-Time COMpute for VLAs (SITCOM), a framework that augments any pretrained VLA with model-based rollouts and reward-based trajectory selection, inspired by Model Predictive Control algorithm. SITCOM leverages a learned dynamics model to simulate multi-step action rollouts to select the best candidate plan for real-world execution, transforming one-shot VLAs into robust long-horizon planners. We develop an efficient transformer-based dynamics model trained on large-scale BridgeV2 data and fine-tuned on SIMPLER environments to bridge the Real2Sim gap, and score candidate rollouts using rewards from simulator. Through comprehensive evaluation across multiple tasks and settings in the SIMPLER environment, we demonstrate that SITCOM when combined with a good reward function can significantly improve task completion rate from 48% to 72% using trained dynamics model.
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