Intent-guided Adaptive Stiffening for Visuomotor Policies under Unknown Resistance
Authors: Francesco Cufino, Carmela Mariniello, Mario Selvaggio, Andreas Sochopoulos, Fabio Ruggiero
Organizations: PRISMA Lab, Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy. · The University of Edinburgh, Edinburgh, UK.
Manipulation tasks such as unscrewing a cap or sliding a part in contact with a surface require imposing force along a high-friction direction: the robot must overcome a resistance rather than yield to it. Generative visuomotor policies predict the motion for such tasks but not the authority needed to realize it, and that authority is bounded by the impedance controller beneath them: when the resistance exceeds what a compliant controller can exert under a bounded tracking error, the task is unattainable however accurate the prediction is. The required amount cannot be anticipated either, since it depends on a contact state the observation does not reveal. In this paper, we propose a residual stiffening layer placed underneath an unmodified policy. Resistance is recognized, without force sensing, from the mismatch between the motion the policy commands and the motion the robot realizes, and a four-mode supervisor raises the stiffness along a single direction read off the policy's own action chunk, retains it while the task progresses and releases it afterwards. The layer needs no compliance labels, no retraining and no re-collected demonstrations. On two real tasks, unscrewing a bottle cap and pushing a T-nut along a T-slot groove against non-homogeneous friction, evaluated at six resistance levels of which two were withheld from the demonstrations, the layer raises the success rate from 52% to 90% while producing 61% less peak off-axis force than a uniformly stiff controller at the same resistance.
Figures & tables
Fig. 1: Example task demonstrated by our method. A robot leverages a learned visuomotor policy to slide an angle bracket mounted onto a T-nut through a high-friction aluminum profile groove. Our adaptive method dynamically regulates robot stiffness to overcome non-homogeneous resistance, extracting the intended stiffening direction directly from policy action predictions.
Fig. 2: Supervisor state machine. Predicates B,M,E are defined in Sec. III-D , C in ( 8 ).
L1
L2
L3
L4
L5
L6
Unscrewing [Nm]
1.42
1.58
1.36
1.54
2.19
2.76
Pushing [N]
17.2
26.5
31.3
41.2
51.1
63.2
TABLE I: Authority demanded by each level.
Unscrewing
Pushing
Policy
chunk length H / rate fs
8 / 20 Hz
demonstrations per level
15
12
Baseline controller
Ka,p [s -2 ]
500
300
Ka,o [s -2 ]
250
600
TABLE II: Parameters of the policy and of the stiffening layer.
Task
Condition
L1
L2
L3
L4
L5
L6
Unscrewing
K0
100
100
100
100
0
0
Prop.
100
100
100
100
100
60
Pushing
K0
100
100
20
0
0
0
Prop.
100
100
80
80
100
60
TABLE III: Success rate [%] over five trials per level. L1–L4 were demonstrated, L5–L6 withheld.
Fig. 3: Representative trials at the highest resistance level, unscrewing (left) and pushing (right). The snapshots are taken as the supervisor enters Stf , and two seconds after it enters Hld , having overcome the resistance, and report the recovered task direction, the mode and the elapsed time. Below: commanded and realized rates of progress s˙d,s˙ ; supervisor mode σ ; added stiffness k .
Condition
Along task [N]
Off-axis [N]
Ratio
Proposed
62.0 [58.0, 64.5]
18.3 [13.9, 21.0]
0.30
Fixed Khigh
75.2 [64.2, 112.7]
47.5 [30.8, 106.6]
0.63
TABLE IV: Peak force along and orthogonal to the task direction, pushing at L6. Median over five successful trials, range in brackets.
All authors are with Meta Reality Labs Research · Yeping Wang is with University of Wisconsin-Madison, Madison, WI, USA · Zhengtong Xu is with Purdue University, West Lafayette, IN, USA
The authors are with the Faculty of Mechanical Engineering, Sungkyunkwan University, Suwon 2066, South Korea · The authors are with the Faculty of Intelligent Robotics, Sungkyunkwan University, Suwon 2066, South Korea