DexLink: Synthesizing Dexterous Demonstrations from chained Hand-object Poses

Synthesizing diverse dexterous manipulation policies is challenging: existing simulation-based approaches rely on human demonstrations which are difficult to scale, or use generic reward functions, which provide limited control over the hand-object interactions. We propose \framework, which addresses these limitations by synthesizing diverse dexterous behaviors from sparse, reusable hand-object interaction templates. Given key task waypoints, DexLink retargets static interaction templates to target objects using an optimization that preserves spatial hand-object relationships. It then links the retargeted poses into an approximate reference trajectory and uses residual reinforcement learning to refine this reference into a closed-loop policy in simulation. Experiments spanning single-hand, bimanual, articulated, and dynamic tasks demonstrate that DexLink provides fine-grained control over task timing, grasp types, and high-level execution. The modular interaction templates transfer reliably across object scales, instances, and categories; without requiring object-specific human demonstrations, the framework also matches the performance of dense reference-tracking methods. Finally, zero-shot deployment of policies trained purely in simulation shows promise for simulation-based generation.