DexCompose

DexCompose: Role-aware residual framework for composing dexterous manipulation policies with a single hand

Researchers introduce DexCompose, a framework for reusing pretrained dexterous manipulation policies to perform multiple tasks simultaneously with a single robotic hand. The approach assigns explicit finger-level action ownership and trains asymmetric residual modules — a bounded stabilizer for preserving existing skill states and a context-aware residual for new task execution — to avoid destructive interference between overlapping skills. Evaluated on 16 composite manipulation tasks, DexCompose achieves a 77.4% average composite success rate, outperforming conventional policy chaining approaches.