On Shaky Ground: Characterising Non-Rigid Base Effects in Mobile Manipulation

Zongyuan Zhang; Christopher Lehnert; Will N. Browne; Jonathan M. Roberts

cs.ROOct 27, 2026

On Shaky Ground: Characterising Non-Rigid Base Effects in Mobile Manipulation

Authors: Zongyuan Zhang, Christopher Lehnert, Will N. Browne, Jonathan M. Roberts

Organizations: Faculty of Engineering, Queensland University of Technology, 2 George St, Brisbane, QLD 4000, Australia · Australian Cobotics Centre, Queensland University of Technology, 2 George St, Brisbane, QLD 4000, Australia · QUT Centre for Robotics, Queensland University of Technology, 2 George St, Brisbane, QLD 4000, Australia

Abstract

Mobile robot manipulators extend contact-task capability beyond the workspace of fixed robot installations, but their bases must also sustain the forces generated by the task. On lightweight platforms, base motion and support compliance can change the force and motion realised at the workpiece. How these non-rigid base effects manifest during sustained contact remains insufficiently characterised. This paper investigates non-rigid base effects in mobile robot manipulators through a sanding case study combining exploratory simulation with experiments on two lightweight, unactuated wheeled testbeds. Joint measurements of contact force, tool motion, and base vibration show that sustained contact coexisted with substantial task error: under low base-ground resistance, only 54-70% of a 100 N command was realised, with tool-centre-point errors reaching 100 mm. Increasing base-ground resistance during contact improved force realisation and arrested further tool displacement, while base vibration did not vary consistently with force-tracking error in the tested conditions. These observations illustrate how non-rigid base behaviour can affect contact-task execution and highlight the value of examining force, motion, and vibration together when characterising lightweight mobile manipulation.

Figures & tables

Figure 1: Contact forces acting on a mobile manipulator during sanding. The contact forces $F_{n}$ and $F_{t}$ and the tool torque $\tau_{tool}$ are transmitted through the manipulator to the base and balanced by the ground support forces $N_{1}$ – $N_{3}$ and the friction forces $F_{f}$ , with the weight $mg$ acting at the centre of mass (CoM). Limited traction and compliance at the base–ground interface can lead to base motion, loss of stability, and vibration.

Panel of Figure 2 — Figure 2: Simulation environment and representative non-rigid base effects.

	Simulation	Cart	Trolley
Arm	UR10 model	UR10	UR5
Base	Rigid wheels	Aluminium	Plywood
Support	Rigid floor	Concrete	Foam
Resistance	Varied	Low, high	Low, high
Sander speed	Varied	Low, high	High

Table 1: Simulation model and physical platforms.

Panel of Figure 3 — Figure 3: Physical testbeds. The cart has an aluminium-extrusion frame with a slotted aluminium deck; the trolley has a plywood deck with the compliant layer beneath the base. Panel (c) shows the force/torque sensor, deck-mounted inertial measurement unit, tripod-mounted camera, and tool-mounted ArUco marker used with the trolley; the same sensors were deployed on the cart.

Condition

Result

Resistance

Posture

Speed

F^{*}

\bar{F}

\rho_{F}

e_{F}

\sigma_{F}

Slope

e_{\mathrm{TCP}}

a_{pp}

(m s -2 )

(N)

(N s -1 )

(mm)

med.

max

Experiment 1 (cart)

Low

Standard

Low

100

58.8

0.59

41.5

2.6

0.42

n/a

0.19

0.69

Low

100

68.3

0.68

32.1

3.7

0.32

n/a

0.08

0.12

High

Standard

Low

100

94.3

0.94

6.9

3.2

0.21

n/a

0.29

0.77

Table 2: Trials by experiment, with force realisation, tool centre point (TCP) error, and base vibration during contact, using the metrics defined in Section 4.5 .

Figure 4: Experiment 1: single-condition trials, each shown during contact up to retraction. Top: measured normal force and command. Bottom: base acceleration envelope normal to the deck. In each row, (a)–(d) share the vertical axis.

Figure 5: Experiment 2: switch from low to high resistance part-way through contact. The bar above each panel marks the low-resistance (shaded) and high-resistance phases; the dotted line marks the switch. Top: normal force. Middle: TCP error, the displacement of the tool marker from its position at the start of the record, tracked by the fixed camera. Bottom: base acceleration envelope normal to the deck. In each row, (a) and (b) share the vertical axis.

Figure 6: Experiment 3. Top: normal force. Middle: TCP error, the displacement of the tool marker from its position at the start of the record. Bottom: base acceleration envelope normal to the deck. In each row, (a) and (b) share the vertical axis.

Aspect	Simulation	Hardware observations
Base motion	Slip when the required wheel–ground tangential forces exceed the available traction	About 100 mm TCP error at low resistance (T5–T7), accompanied by $\rho_{F}$ of 0.54–0.70 (T1, T2, T5–T7), on both platforms
Posture sensitivity	Extended posture increases excitation and reduces the stability margin	Low posture reduced median base vibration from 0.19 to 0.08 m s -2 and raised $\rho_{F}$ from 0.59 to 0.68 (T1, T2)
Base–ground interaction	Higher wheel friction suppresses slip	High resistance raised $\rho_{F}$ to 0.88–1.01 on both platforms (T3, T5, T6, T8); the switch arrested TCP-error growth within about 1 s (T5, T6)
Oscillation / tool excitation	Tool excitation produced tool and base oscillation; tool speed shifted the dominant frequency	High tool speed produced substantially higher base vibration than low tool speed, both out of contact and at high resistance (T5, T6)
Support compliance	Not represented in the rigid-wheel model	Foam nearly tripled median base vibration relative to the hard floor (Figure 7 )
Loss of stability	Occurred when the zero-moment point moved outside the support polygon	Not reached

Table 3: Qualitative comparison of simulated behaviours and hardware observations.

Figure 7: Experiment 3, support comparison (low resistance, 20 N command): base acceleration envelope normal to the deck.

Mobile Manipulation

Mobile manipulation studies robots that navigate through environments while using arms or other effectors to interact with objects.

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Open paper AskPDF arXiv

Contact-rich manipulation requires robots to balance accurate motion tracking with compliant interaction, yet most visual-action policies leave compliance fixed at the controller level. We present Imp-ACT, a methodologically grounded and practical approach to incorporating direction-dependent Cartesian stiffness modulation directly into demonstration collection, without manual stiffness selection or offline target reconstruction. During teleoperation, a self-tuning impedance controller adapts stiffness along the instantaneous direction of motion while maintaining compliance in orthogonal directions. The adapted stiffness is applied and recorded alongside visual observations and motion commands, capturing motion and compliance under the same dynamics. We implement this pipeline using Action Chunking with Transformer (ACT) to predict end-effector pose, gripper action, and motion-direction stiffness from visual, proprioceptive, and wrench observations. The performance of Imp-ACT is evaluated on wiping and plug insertion using both success rate and quantitative measures of contact behavior. Compared with fixed low- and high-stiffness baselines, Imp-ACT achieves comparable or higher success while maintaining low interaction forces. In wiping, it reduces contact-force vibration by approximately

29\times

relative to the compliant baseline and

180\times

relative to the stiff baseline. In plug insertion, it reduces forces orthogonal to the insertion direction by

43\%

relative to the better fixed-stiffness baseline. These results highlight the benefit of maintaining sufficient stiffness along the direction needed for task execution while preserving compliance in other directions to limit contact forces and accommodate environmental constraints.

Abstract

Mobile Manipulation

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