cs.ROAug 21, 2026

Force/Torque-Based Kinematic Adaptation for Robotic Manipulation Tasks

Authors: Carl Glen Henshaw

Organizations: US Naval Research Laboratory · U.S. Naval Research Laboratory

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Abstract

Contact-rich robotic manipulation requires an accurate model of the kinematic relationship between a robot's joints and the task features it senses. This relationship is rarely known exactly: it changes with each tool the robot picks up and shifts, sometimes almost instantaneously, as contact modes change --- especially for multi-fingered hands that make and break contact at points that are not exactly prescribed, as in full-hand grasping. This paper develops an adaptive scheme that estimates that relationship online, using only joint-angle sensing and a wrist-mounted force/torque sensor, with no exteroceptive measurement of the tool tip. We derive a provably stable kinematic update law that identifies the kinematics of an unknown tool from force/torque feedback alone, and prove stability of both the rigid case and the case with a compliance controller as an inner loop. We show that identification is confined to the directions the motion excites --- so that, for example, a tool's length is unobservable under a rigid insertion push, while a compliant loop's passive yielding partially excites it; and that with a second-order admittance the compliant certificate holds unconditionally in continuous time. We also pose the combined control and estimation problem as a Quadratic Program (QP): the formulation yields the prediction term of the update law exactly but, instructively, cannot reproduce the tracking adaptation term. We validate the scheme in simulation on a peg-in-hole insertion. This work is the first step in a research program aimed at factoring manipulation learning into a task policy which can be learned in isolation of the robot, for instance by reinforcement learning, and an adaptive kinematic component that adapts online to the particular robot, hand, or tool in use.

Figures & tables

Figure 1: A planar peg–in–hole task, with Whitney’s sensor definitions [ 21 ]
Figure 2: A successful force/torque–only insertion. The peg is seated using only the sensor vector s=[ d, Flat, M ]⊤\mathbf{s}=[\,d,\ \mathbf{F}_{\mathrm{lat}},\ \mathbf{M}\,]^{\top} : no tip position is measured. Sensor depth tracks the forward–kinematic depth (small bias b\mathbf{b} ), the contact force and wrist moment remain within the success limits, and the stability margin λmin⁡(J^J^⊤)\lambda_{\min}(\hat{J}\hat{J}^{\top}) stays above the 10−610^{-6} rank gate throughout, so the full–row–rank hypothesis of Theorem 1 holds along the trajectory. All three success channels close simultaneously at t≈5.1 st\approx 5.1\,\mathrm{s} .
Figure 3: Observability spectrum under the second–order admittance: eigenvalues of the regressor Gram ⟨Y⊤Y⟩\langle Y^{\top}Y\rangle over the insertion, with no commanded rotation. The lateral direction carries O(1)O(1) excitation throughout. The axial (tool–length) direction — structurally unexcited under a rigid insertion push (separation ∼ ⁣4×105\sim\!4\times 10^{5} on the stiff loop) — is passively excited by the compliance yielding to the contact moment, rising during the contact–rich phases and collapsing the separation to ∼ ⁣5×101\sim\!5\times 10^{1} : the free excitation of Remark 3 , observed directly.
Figure 4: Augmented energy VaugV_{\mathrm{aug}} (log scale) across two decades of adaptation gain, Γ=100\Gamma=100 (left) and Γ=8000\Gamma=8000 (right), for the composite law (blue) against a prediction–only ablation (grey). At both gains the composite trace descends to its floor and overlays the prediction–only trace: the tracking term, evaluated on the realized (admittance–state) velocity as the compliant certificate requires, adds no instability at either gain, and the seat transition is gain–flat per the normalized–LMS gain ( 23 ).
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