DexAnyTwist: Learning General Dexterous Twisting with Hybrid Manipulation System Identification

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ID: 316916
2026
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Abstract
Abstract Developing general-purpose dexterous manipulation, particularly for intricate twisting tasks, remains a long-standing challenge. Large-scale training on massive datasets encompassing a vast spectrum of geometric and physical properties is a promising path. Yet, the hybrid dynamical space inherent in heterogeneous datasets creates optimization conflicts, where standard learning paradigms fail to synthesize a cohesive control strategy across disparate physical laws. This paper presents DexAnyTwist, a framework that reformulates the general twisting task as a problem of identification and control within a hybrid dynamical system. DexAnyTwist employs an iterative strategy to progressively partition the hybrid manipulation task into dynamically consistent subsystems. This approach utilizes forward partitioning to isolate data modes based on expert performance and backward refinement to optimize specialized policies. A learned gating mechanism then dynamically composes these experts, effectively mitigating interference between distinct dynamic modes. Beyond achieving state-of-the-art performance on our large-scale dataset, our framework spontaneously evolves manipulation primitives consistent with human twisting, and demonstrates superior zero-shot generalization to novel real-world geometries. This study not only solves a complex class of twisting tasks but also establishes a scalable pathway for robots to acquire general-purpose contact-rich manipulation skills.
Reference Key
openalex_W7164194402 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xing Liu, Yunlong Dong, Jun Wan, Linan Deng, Feng Hua, Y Shen, Min Yu, Guijun Ma, Cheng Cheng, Haitao Song, Han Ding, Ye Yuan
Journal national science review
Year 2026
DOI
10.1093/nsr/nwag351
URL
https://doi.org/10.1093/nsr/nwag351
Keywords Keywords not found

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