Trajectory planning and control in field robotics rely on predicting how propulsion and steering affect vehicle motion when contact points undergo slip. For articulated vehicles, the point-contact kinematic model (PCK) accounts for the linkage geometry but neglects the rotational resistance distributed along the contacts. We propose a finite-support quadratic model (FSQ) for single-track, center-articulated vehicles that incorporates this resistance through a quasi-static balance of lateral slip. Our approach generalizes the standard PCK formulation by relaxing the contact-point assumption. An exact reduction of the quadratic slip cost to contact moments gives a compact closed-form solution for real-time prediction of lateral velocity and yaw rate. We evaluate the proposed method in real-world experiments across asphalt, grass, ice, and mixed routes, using more than 7 km of data. For five-second predictions, FSQ reduces the weighted median translation and yaw errors by 53.5% and 68.9%, respectively, relative to PCK.
Figures & tables
Fig. 1: Articulated motion prediction during a field turn. The purple point-contact prediction, based on pointwise no-slip constraints, deviates markedly from the black traveled path. Blue annotations show longitudinal body velocity vb and articulation φ in the overhead view, while the lower-left inset identifies the two actuation mechanisms: the driven track and hydraulic hitch.
Fig. 2: Articulated geometry and lateral contact slip. The tractor is depicted in teal and the trailer in plum, with body frames Fi centered at Pi . Distances ℓi connect these origins to hitch H , φ is articulation, and vb is tractor longitudinal velocity. Headings θi are measured from the world xW direction. Dashed lines perpendicular to xi lead to the off-frame ICR ( ICR ), with turning radius rPCK for steady point-contact motion. Gray guides locate the contact domains Pi enlarged below, with both inset frames oriented alike. In the insets, curved arrows indicate positive yaw rates ωi , and straight arrows show the contribution of rotation about Pi to lateral slip s⊥,i .
Fig. 3: Field platform and instrumentation over a lidar map. Green labels identify (A) the RS Airy lidar, (B) the Hesai XT-32 lidar, (C) the Xsens MTi-100 IMU ( IMU ), and (D) the ZED 2i camera. A provides articulation and trailer pose estimates in SE(3) , while measurements from B , C , and D are combined to estimate tractor odometry. Blue insets locate the driven track and hydraulic hitch, with body velocity vb and articulation angle φ .
Characteristic
Symbol
Value
Unit
Robot
Tractor mass
m1
365
kg
Trailer mass
m2
75
kg
Front center–hitch distance
ℓ1
0.8342
m
Hitch–rear center distance
ℓ2
1.5145
m
Front contact length
lt
1.225
m
Rear axle separation
dr
0.455
m
TABLE I: Platform geometry and contact parameters. Only the rear/front contact-weight ratio is fitted from motion data.
Fig. 4: Prediction error versus horizon. Lines and bands show weighted medians and the IQR ( IQR ) of translation RPE ( RPE ) at P1 (top) and tractor yaw RPE (bottom), with equal weight per run. Triangles mark bands extending beyond the view.
Fig. 5: Five-second prediction errors and contact calibration. The violins show translation RPE (top) and yaw RPE (bottom) on logarithmic axes, with width representing error density. White marks, boxes, and whiskers indicate weighted medians, IQR , and fifth to 95 th percentiles, with equal total weight per run within each group. Triangles mark individual segment errors outside the view. FSQ uses a global rear/front contact-weight ratio α or a separate ratio fitted on each terrain’s evaluated runs.
Fig. 6: Five-second prediction errors grouped by longitudinal velocity. At each plotted velocity, lines and bands summarize the weighted medians and IQR of segments whose starting velocity lies within ±0.75ms−1 of that value, with equal total weight per run in that neighborhood. Direction and speed may change during prediction. Dashed lines mark zero and ∣vb∣=1\text{,}\mathrm{m}\text{,}{\mathrm{s}}^{-1}$$ , while triangles indicate IQR extending beyond the vertical limits.
Fig. 7: Campus trajectory with three five-second prediction examples. Boxes on the mapped reference route locate (a) a near-straight segment, (b) a sharp turn, (c) a milder turn. Background point colors indicate height. Insets use a common metric scale, with dashed black for the reference, purple for the point-contact model ( PCK ), and orange for the FSQ ( FSQ ). Hollow circles mark the common starts and arrowheads the endpoints. Scale bars show 50m above and 5m below.
LIPM is everywhere in legged-locomotion control, but almost always as a modeling choice rather than as something the controller's cost actually prefers. This note tries to make that link more explicit. Working from a small centroidal OCP that penalizes the rate of angular momentum, we look at what its optimum tends to look like. Three things come out. With full-rank stance, the optimum drifts toward a pendular force pattern at a rate determined by the SVD of the moment Jacobian; the constant is set by foot-span geometry and matches the experiments to within 16%. With N=2 stance, as in trot, the friction cone introduces a lower bound on ∥H˙G∥ that no amount of weight tuning fixes; we also see a non-smooth feasibility kink at a critical horizontal acceleration that we can write in closed form. Adding a task term that asks for a nonzero H˙G moves the optimum off the pendular set in a predictable way. None of this is far from the classical ZMP/DCM picture. We test these claims on a point-mass quadruped and on the Unitree Go1 in MuJoCo (open-loop QP and a torque-level closed-loop controller), and we note where the asymptotic story stops being a good description of what the closed loop actually does.
Lingxue Lyu, Zihui Liu
School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA · Department of Aeronautics & Astronautics, Stanford University, Stanford, CA 94305, USA
Non-prehensile planar manipulation, including pushing and press-and-slide, is critical for diverse robotic tasks, but notoriously challenging due to hybrid contact mechanics, under-actuation, and asymmetric friction limits that traditionally necessitate computationally expensive iterative control. In this paper, we propose a mode-aware framework for planar manipulation with one or two robotic arms based on contact topology selection and reduced-order kinematic modeling. Our core insight is that complex wrench-twist limit surface mechanics can be abstracted into a discrete library of physically intuitive models. We systematically map various single-arm and bimanual contact topologies to simple non-holonomic formulations, e.g. unicycle for simplified press-and-slide motion. By anchoring trajectory generation to these reduced-order models, our framework computes the required object wrench and distributes feasible, friction-bounded contact forces via a direct algebraic allocator. We incorporate manipulator kinematics to ensure long-horizon feasibility and demonstrate our fast, optimization-free approach in simulation across diverse single-arm and bimanual manipulation tasks. Supplementary videos and additional information are available at: https://sites.google.com/view/pushpressslide
Melih Özcan, Umut Orguner, Ozgur S. Oguz
Dept. of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkiye. · Dept. of Computer Engineering, Bilkent University, Ankara, Turkiye.
Foot slip is a major source of instability in bipedal locomotion on low-friction or uncertain terrain. Standard control approaches typically assume no-slip contact and therefore degrade when slip occurs. We propose a control framework that explicitly incorporates slip into the locomotion model through virtual nonholonomic constraints, which regulate the tangential stance-foot velocity while remaining compatible with the virtual holonomic constraints used to generate the walking gait. The resulting closed-loop system is formulated as a hybrid dynamical system with continuous swing dynamics and discrete impact events. A nonlinear feedback law enforces both classes of constraints and yields a slip-compatible hybrid zero dynamics manifold for the reduced-order locomotion dynamics. Stability of periodic walking gaits is characterized through the associated Poincaré map, and numerical results illustrate stabilization under slip conditions.
Centre for Automation and Robotics (CSIC-UPM), Ctra. M300 Campo Real, Km 0,200, Arganda del Rey - 28500 Madrid, Spain. · the Department of Applied Mathematics, Comillas Pontifical University, Madrid, 28015 - Madrid, Spain. · School of Science and Technology, IE University, Madrid, Spain. +1