A stable grasp does not guarantee kinematically feasible robotic insertion because the object-in-gripper transform may force the robot towards singularities or joint limits along the prescribed insertion path. We study post-grasp kinematic feasibility repair through object-in-gripper reorientation. Given an achieved grasp and a fixed insertion path, we seek a small reorientation that restores kinematic feasibility. Sequential IK can miss such candidates by following an unfavorable joint-space path, while the nonsmooth feasibility landscape makes the search computationally expensive. We evaluate candidates using a branch-aware IK graph that maximizes the minimum feasibility margin over the discretized insertion path and use a learned task-conditioned prior to improve query ordering. The selected reorientation is executed through tactile-based extrinsic manipulation. In UR5e simulations, the planner without learned ranking reduces mean reorientation over successful trials by 51.5% compared with grid-based sequential IK. Adding learned ranking reduces this planner's mean planning time by an additional 51.1%. Real-robot experiments validate the complete pipeline.
Figures & tables
Fig. 2 : Illustration of a constrained pickup scenario with four test tubes placed in a holder. The robot cannot reach an alternative grasp pose for the target object without colliding with a neighboring tube.
Fig. 3 : Overview of the proposed framework for post-grasp kinematic repair for robotic insertion via object-in-gripper reorientation.
ID
Method
Search
Verifier
Success (%)
Repair (deg)
Median (deg)
Cand. evals
IK calls
Time (s)
M0
No repair
–
Seq. IK
20.0
0.00
0.00
1.00
6.0
0.03
M0-B
No repair
–
Branch graph
28.8
0.00
0.00
1.00
87.5
0.37
M1
Grid-seq.
5 ∘ grid
Seq. IK
94.4
15.92
8.54
441.00
2646.0
16.37
M2
Active-seq.
Ray active
Seq. IK
94.8
11.98
6.88
59.47
356.8
2.03
M3
Mesh-seq.
Adaptive mesh
Seq. IK
94.8
11.68
6.56
73.53
441.2
2.57
M4
Grid-branch
5 ∘ grid
Branch graph
94.4
13.00
7.07
441.00
39288.7
204.36
TABLE I : UR5e insertion repair benchmark on 250 category-controlled test instances. Repair statistics are computed over successful trials for each method.
OOD factor
Condition
Success (both)
Calls M6
Calls M7
Red. (%)
Goal position
Nominal
20/20
69.15
34.60
50.0
+100 mm
18/20
67.50
41.10
39.1
+150 mm
17/20
67.50
49.55
26.6
Cylinder length
−15%
20/25
51.12
30.72
39.9
−30%
20/25
39.44
25.00
36.6
−60%
20/25
39.52
25.40
35.7
TABLE II : Frozen-model sensitivity under controlled OOD variations. M6 and M7 achieved identical success in each condition. Calls denotes the mean number of verifier evaluations, and Red. denotes the percentage reduction achieved by M7 relative to M6.
Fig. 4 : Real-robot experimental setup with three objects: a test tube, an Allen key, and a USB stick. The planned reorientation ρ was executed by pushing the grasped object against the environment using tactile object-state feedback.
Proposed pipeline (Successes/Attempts)
Object
Configs.
Trials
Reorientation success
Post-action verifier pass
End-to-end insertion
Allen key
3
15
15/15
15/15
13/15
USB stick
3
15
15/15
15/15
12/15
Test tube
3
15
15/15
15/15
15/15
Overall
9
45
45/45
45/45
40/45
TABLE III : Real-robot evaluation results for three objects.