VOLT: 面向超演示速度策略的视觉与语言轨迹分割
VOLT: Vision and Language Trajectory Segmentation for Faster-than-Demonstration Policies
AI总结：
提出VOLT方法，通过视觉与语言线索对演示轨迹进行分割，选择性下采样安全加速部分，保留需要精细操作的慢速段，从而训练出比演示更快的机器人策略。
摘要：
人类演示任务所需的时间通常比机器人执行任务的时间长。许多工业和实际应用要求机器人尽可能快地执行任务，而不是学习以相同速度复制演示。本文研究了实现超演示速度策略的几种假设。实验表明，最有效的策略是对记录的演示进行下采样，并在加速后的数据上训练机器人策略。然而，均匀下采样整个轨迹可能存在问题：任务的某些部分可以安全加速（例如无约束运动），而其他部分则需要更慢、更精确的运动（例如物体交互或精细操作）。为解决这一挑战，我们提出了VOLT，一种视觉与语言轨迹分割方法，它推理视频演示，并利用上下文线索确定何时加速合适以及何时需要小心精确。VOLT识别需要缓慢、谨慎运动的分段，然后选择性地对剩余分段进行下采样。得到的重新格式化轨迹可用于标准模仿学习方法，如扩散策略。我们的结果强调分割质量至关重要——基线方法常常错误判断何时可以加速，导致策略过于谨慎或不可靠。与最先进的替代方法相比，VOLT使机器人能够更快地执行任务，同时保持强劲性能。
英文摘要：
Humans often take longer to demonstrate a task than a robot would need to execute it. Rather than learning to replicate the demonstration at the same pace, many industrial and practical applications require robots to perform tasks as quickly as possible. In this paper, we investigate several hypotheses for learning policies that operate faster-than-demonstrations. Our experiments show that the most effective strategy is to downsample recorded demonstrations and train the robot's policy on this accelerated data. However, uniformly downsampling an entire trajectory can be problematic. Some parts of a task can be safely sped up (e.g., unconstrained motion), while others demand slower, more precise motion (e.g., object interactions or fine manipulation). To address this challenge, we introduce VOLT, a vision-and-language trajectory segmentation method that reasons over video demonstrations, and leverages contextual cues to determine when acceleration is appropriate and when careful precision is required. VOLT identifies segments where slow, deliberate motion is necessary, then selectively downsamples the remaining segments. The resulting reformatted trajectories can be used with standard imitation learning approaches, such as diffusion policies. Our results highlight that segmentation quality is critical---baseline methods often misidentify when acceleration is possible, leading to overly cautious or unreliable policies. Compared to state-of-the-art alternatives, VOLT allows robots to execute tasks faster while maintaining strong performance. See our project website: https://volt2026.github.io/VOLT2026
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
