JDSG: Jitter Decreasing Shortcut Guidance for Diffusion Policy in Precision-sensitive Manipulation Tasks

Generative behavior cloning methods such as Diffusion Policy can produce intra-chunk trajectory jitter that may compromise contact stability in precise manipulation. We first show that post-hoc trajectory-preserving jitter-minimization improves task success, suggesting that intra-chunk trajectory jitter is a contributing factor to policy failure. Our analysis indicates that effective smoothing must balance jitter reduction with preservation of the generated trajectory. However, post-hoc smoothing modifies only the completed action chunk, leaving no opportunity for iterative denoising steps to refine the trajectory. Incorporating a smoothing objective into diffusion sampling offers an alternative, but directly differentiating through the policy network introduces additional inference cost. We introduce Jitter Decreasing Shortcut Guidance (JDSG), an inference-time method that applies trajectory-preserving jitter-minimization during diffusion sampling without policy-network backpropagation. At each denoising step, JDSG adjusts the intermediate clean action estimate using a quadratic objective that penalizes third-order temporal differences and deviations from the unmodified estimate. This adjustment requires only a small linear-system solve, after which sampling proceeds using the policy’s predicted noise. On the evaluated simulation tasks, JDSG achieves higher average success rates than both the base Diffusion Policy and post-hoc jitter-minimizing optimization, including under the tested visual distribution shift. Real-world experiments further show improvements over the base policy. These findings support sampling-time, trajectory-preserving jitter-minimization as a useful approach to improving precise manipulation.