Drift is a Sampling Error: SNR-Aware Power Distributions for Long-Horizon Robotic Planning

• URL: http://arxiv.org/abs/2605.09537v1
• Date: Sun, 10 May 2026 13:49:56 GMT
• Title: Drift is a Sampling Error: SNR-Aware Power Distributions for Long-Horizon Robotic Planning
• Abstract summary: We propose Context-Aware Power Sampling (CAPS), a training-free inference-time computation framework.<n>CAPS sharpens global trajectory probabilities, enabling lookahead search over the model's conditional generative trajectory distribution.<n> experiments on RoboTwin, Simpler-WindowX, and Libero-long benchmarks show that CAPS achieves substantial improvements over strong baselines.
• Score: 6.615277432526784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite rapid progress in Vision-Language-Action (VLA) models for robotic control, instruction drift remains a persistent failure mode in long-horizon tasks. This paper reconceptualizes this phenomenon, positing that instruction drift is fundamentally a systematic sampling error: local greedy sampling is prone to collapsing into "Negative Pivotal Windows"--irreversible local optima with high local probability that sever global success pathways. To address this, we propose Context-Aware Power Sampling (CAPS), a training-free inference-time computation framework. CAPS leverages power distributions to sharpen global trajectory probabilities, enabling lookahead search over the model's conditional generative trajectory distribution. Furthermore, we introduce a metacognitive control mechanism based on Signal-to-Noise Ratio (SNR). This mechanism triggers adaptive MCMC search solely when drift risk is detected, enabling a dynamic transition from "intuitive fast thinking" to "rational slow search." Experiments on RoboTwin, Simpler-WindowX, and Libero-long benchmarks show that CAPS achieves substantial improvements over strong baselines, including OpenVLA and TACO, without parameter updates. These results support the effectiveness of adaptive inference-time computation for improving long-horizon robustness in embodied control.

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