Fig. 1 End effector ((a) Parallel jaws; (b) Dexterous hand)

Fig. 2 Grasp pose for hammer with different intentions ((a) Use-intention; (b) Pass-intention)

Fig. 3 The algorithms of grasp pose generation for dexterous hands with integrated knowledge transfer

Fig. 4 The network structure of IntContact

Fig. 5 Intra-class instrument alignment ((a) Primordial point cloud; (b) Post-aligned point cloud)

Fig. 6 Implicit shape interpolation

Fig. 7 Explicit contact mapping

Fig. 8 Iterative pose refinement solves for grasp pose

Fig. 9 MANO hand model

Fig. 10 Mapping rule ((a) Mapping rules based on fingertips; (b) Mapping rules based on fingertips and proximal interphalangeal key points; (c) Optimized mapping rules)

Fig. 11 The flowchart of grasp mapping

Fig. 12 Division of hand collision regions

Fig. 13 Examples of different algorithms for hammer and drill grasp pose generation under different intents ((a1, a2) GrasspTTA_use; (b1, b2) IntGen_use; (c1, c2) IntContact_use; (d1, d2) GraspTTA_pass; (e1, e2) IntGen_pass; (f1, f2) IntContact_pass)

Fig. 14 Example of grasp pose of hammer_2 and drill_2 in ablation experiments ((a1, a2) IntContact_use; (b1, b2) IntContact+Tink_use; (c1, c2) Ours_use; (d1, d2) IntContact_pass; (e1, e2) IntContact+Tink_pass; (f1, f2) Ours_pass)

Fig. 15 Example of dexterous hand grasp hammer_2 and drill_2 under different mapping rules ((a1, a2) A_use; (b1, b2) B_use; (c1, c2) C_use; (d1, d2) A_pass; (e1, e2) B_pass; (f1, f2) C_pass)

Fig. 16 Example of different brands of dexterous hands grasp hammer_2, drill_2 under mapping rule c ((a1, a2) Schunk_use; (b1, b2) Shadow_use; (c1, c2) Ability_use; (d1, d2) Schunk_pass; (e1, e2) Shadow_pass; (f1, f2) Ability_pass)

Fig. 17 Dexterous hand robot grasp platform

Fig. 18 Example of real machine grasp with different intents ((a) Hammer_pass; (b) Hammer_use; (c) Power_pass; (d) Power_use)