AutoSpeed: Annotation-Free Stage-Adaptive Motion Speed Learning for Robot Manipulation

Different stages of manipulation tasks exhibit varying levels of difficulty, suggesting stage-dependent motion speeds and temporal prediction horizons. However, existing IL-based visuomotor policies typically imitate the execution speed of expert demonstrations and operate with a fixed temporal prediction horizon, limiting flexibility and overall task throughput. In this paper, we introduce AutoSpeed, a model-agnostic learning framework that enables existing visuomotor policies to predict trajectories with stage-adaptive motion speeds, without requiring speed or stage annotations. We treat future trajectories at different speeds as candidate optimization targets, evaluate each candidate using a composite cost that trades off prediction error against prediction horizon, and optimize the policy toward the minimum-cost candidate. With a fixed-length action sequence, speed modulation adjusts the effective temporal prediction horizon: simple stages are executed faster with a longer prediction horizon, whereas complex stages are executed more slowly with a shorter prediction horizon. Specifically, we implement speed modulation in the frequency domain via the discrete cosine transform (DCT), which enables smooth, non-integer speed scaling and thus preserves motion continuity. Extensive evaluations show that AutoSpeed substantially reduces task execution time while also improving success rates. Under the AutoSpeed framework, the inferred motion speeds exhibit a strong correspondence with task stages.

Resolving State Ambiguity in Robot Manipulation via Adaptive Working Memory Recoding

State ambiguity is common in robotic manipulation. Identical observations may correspond to multiple valid behavior trajectories. The visuomotor policy must correctly extract the appropriate types and levels of information from the history to identify the current task phase. However, naively extending the history window is computationally expensive and may cause severe overfitting. Inspired by the continuous nature of human reasoning and the recoding of working memory, we introduce PAM, a novel visuomotor Policy equipped with Adaptive working Memory. With minimal additional training cost in a two-stage manner, PAM supports a 300-frame history window while maintaining high inference speed. Specifically, a hierarchical frame feature extractor yields two distinct representations for motion primitives and temporal disambiguation. For compact representation, a context router with range-specific queries is employed to produce compact context features across multiple history lengths. And an auxiliary objective of reconstructing historical information is introduced to ensure that the context router acts as an effective bottleneck. We meticulously design 7 tasks and verify that PAM can handle multiple scenarios of state ambiguity simultaneously. With a history window of approximately 10 seconds, PAM still supports stable training and maintains inference speeds above 20Hz. Project website: https://tinda24.github.io/pam/