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REVIEW 3 major objections 4 minor 33 references

Mobile manipulators can be planned to open and traverse doors as a single continuous MPC problem, with a soft reachability penalty standing in for arm kinematics.

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2026-08-04 00:59 UTC pith:7Y2FEBMV

load-bearing objection A plausible but overclaimed single-NMPC planner for door traversal; the 'ensured feasible' line rests on a soft penalty with no weight or bound given. the 3 major comments →

arxiv 2608.00206 v1 pith:7Y2FEBMV submitted 2026-07-31 cs.RO cs.SYeess.SY

Motion Planning for Mobile Manipulators Navigating Doorways via Model Predictive Control

Kasra Sinaei , Kasun Weerakoon , Christopher Bradley , Seyed Abolfazl Fakoorian , Donald Ebeigbe This is my paper
classification cs.RO cs.SYeess.SY
keywords motion planningmodel predictive controlmobile manipulatordoor traversalnonlinear optimizationmanipulation feasibilitycollision avoidancepush-pull doors
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper claims that opening and traversing a door can be planned as a single continuous nonlinear optimization rather than a staged sequence of subtasks. The robot base and the door are treated as one dynamical system, with the door angle as a state and door angular velocity as a control. Arm reachability is encoded not by modeling the arm, but by a penalty that allows door rotation only when the base is within an annular region around the handle. The planner is validated in simulation and on a physical mobile manipulator for both push and pull doors. If this holds, it means door crossing becomes a routine input to an MPC solver instead of a hand-crafted skill.

Core claim

The central claim is that the switched dynamics with an integer reachability flag can be replaced by a soft penalty without losing feasibility. Inside the nonlinear program, the door obeys a simple single-integrator dynamic at every step, and the optimizer is steered toward opening the door only when the handle distance lies between an inner and outer reachability radius. A projection-based constraint keeps the base outside the door panel plus a safety margin. The same formulation is shown to produce a pushing trajectory and a pulling trajectory, with the arm's job reduced to tracking the planned door angle through differential inverse kinematics.

What carries the argument

The load-bearing objects are the coupled state vector x=[x,y,θ_base,θ_door] with inputs [v,ω_base,ω_door]; the manipulation penalty C_manip = (max{0,d²-r²_max}+max{0,r²_min-d²})·|ω_door|; and the geometric door-clearance constraint built from the scalar projection t_k = (w_k·v_k)/||v_k||² and the closest-point function p_closest,k. Together they remove the need for integer variables and for explicit arm kinematic constraints in the planner.

Load-bearing premise

The claim that the planned door motion is executable rests on the soft penalty being weighted heavily enough that the optimizer never chooses to rotate the door while the base is outside the reachable annulus; no hard constraint enforces this, and no value or guarantee is supplied.

What would settle it

Examine the optimizer's output: if any planned time step has nonzero door angular velocity while the base-handle distance is outside [r_min, r_max], the manipulation-feasibility claim is violated. Concretely, re-solve the problem with the reachability weight reduced by an order of magnitude and observe whether the door swings while the base remains far from the handle.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Door traversal can be planned as one nonlinear program solved once in a few seconds, replacing staged state machines or behavior trees.
  • The same planner covers push doors and pull doors without switching formulations, because the door direction emerges from the goal state and costs.
  • Arm-specific detail reduces to two radii (r_min, r_max); the planner can be retargeted to a new manipulator by changing these numbers.
  • Additional obstacles can be added as projection-based distance constraints, so the method should extend beyond the empty-corridor setting.
  • The planned door-angle trajectory provides a direct reference for a Jacobian-based arm tracker, bridging planning and low-level execution.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Because reachability is only a soft penalty, the planner has no hard guarantee against commanding door motion out of reach; a hard constraint or a small mixed-integer program would close that gap at extra solve cost.
  • The same coupling idea might apply to other articulated objects (cabinets, valves, drawers) by replacing the door-angle state with the articulation angle and reusing the annular reachability penalty.
  • A natural stress test would be a door with stiction or an unexpected obstruction: the geometric model here has no force or torque model, so the planner may need to be embedded in a feedback loop rather than executed open-loop.
  • Retargeting to arms with non-annular workspaces (e.g., long thin arms) may degrade because the penalty assumes radial symmetry; one could substitute a measured reachability map.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes a motion-planning framework for a mobile manipulator opening and traversing push and pull doors. The planner is formulated as a nonlinear MPC whose state includes the mobile-base pose and the door angle, with controls for base velocity and door angular velocity. The switched reachability condition from the motivating model is removed and replaced by a weighted soft penalty based on the distance between the base and the door handle. Collision avoidance with the door panel is imposed through a closest-point nonlinear constraint. The resulting NLP is solved once with IPOPT/CasADi, and the planned trajectory is tracked by a separate base MPC and differential IK. The paper reports Isaac Sim simulations and a single hardware experiment.

Significance. If the central claim were fully established, the contribution would be a modest but practically useful planning formulation: a single continuous optimization that handles push and pull doors without integer variables or full arm kinematics. The paper has some strengths: the geometric model is clearly explained, the use of a soft reachability penalty is an interesting way to avoid hybrid dynamics, and the demonstration includes both simulation and hardware. However, the load-bearing claim that generated trajectories are guaranteed feasible is not supported by the presented formulation, and the evaluation is anecdotal. The contribution is therefore plausible but not yet verified to the standard implied by the paper's wording.

major comments (3)
  1. [Section 2.2, Eqs. (6), (9), (12), (14); Section 3, first sentence] The statement that “Trajectories generated by the nonlinear program are ensured to be feasible and collision-free” is not supported. In Eq. (14), the door dynamics are the plain single integrator θdoor_dot = ωdoor; the reachability condition δdoor from Eq. (1) is removed and appears only as the soft penalty w_m * C_manip in Eq. (9), with C_manip given by Eq. (6). Since C_manip is multiplied by |ωdoor|, the optimizer can rotate the door when d_k is outside [r_min, r_max] whenever the resulting progress toward the goal outweighs the finite penalty. No value or bound for w_m/w_manip is given, and no hard constraint prevents door motion outside the reachable annulus. Consequently, the downstream IK may be commanded to track a handle trajectory that the arm cannot execute. Please either restore a hard switch (e.g., complementarity or a bilinear constraint), provide a rigorous bound under whic
  2. [Abstract, Section 1.1, Eq. (14)] The abstract and Section 1.1 describe the robot and door as a “coupled dynamical system” and a “unified nonlinear model.” However, in Eq. (14) the base obeys unicycle dynamics and the door obeys a single integrator that is dynamically independent; the coupling is only through the cost term C_manip and the geometric constraint (15). This is a meaningful distinction: the contribution is a joint optimization over two dynamically decoupled subsystems with coupling in the objective/constraints, not a coupled dynamical model. Please revise the claims to match the actual formulation, or introduce true dynamic coupling if that is intended.
  3. [Section 3 and Figures 3–7] The experimental and simulation evaluation is not quantitative enough to support the claims of feasibility and versatility. No values are reported for r_min, r_max, w_m, w_manip, dt, N, Qf, R, or d_s, so the results are not reproducible from the paper. The hardware experiment appears to be a single successful run, and no tracking errors, success criteria, or statistics are given. For the central reachability concern, the paper should report the time history of d_k during the manipulation phase and show that door rotation occurs only when d_k ∈ [r_min, r_max]. Without such evidence, the planner's practical reliability and the claimed advantage over the switched formulation remain unsubstantiated.
minor comments (4)
  1. [Eq. (12)] Eq. (12) defines Cm(xk) = w_manip * C_manip(xk, u_k), while Eq. (9) already multiplies Cm by w_m. This double weighting is confusing; either remove one weight or explain the role of each. Also, C_manip in Eq. (6) is written without a subscript k and without u_k as an argument, though it clearly depends on d_k and |ωdoor|.
  2. [Eq. (6)] The penalty contains nonsmooth max terms. IPOPT assumes smoothness, and the nonsmoothness may cause convergence issues or suboptimal local solutions. Consider reformulating with slack variables or a smooth approximation, and report solver status.
  3. [Section 2.2, Eq. (14)] Calling the planner “MPC” is somewhat misleading because the optimization is solved once in open loop (“only needs to be solved once”) rather than in a receding horizon. The tracking controller in Eq. (19) is the receding-horizon part. Clarify terminology.
  4. [Section 3] The link to videos is helpful but the paper itself should include quantitative data: trajectory duration, computation time, base tracking error, and final door angle. The current statement “takes a few seconds” is too vague.

Circularity Check

0 steps flagged

No significant circularity: the MPC planner is self-contained; the reachability penalty is a design choice, not a fitted input disguised as a prediction.

full rationale

The paper's derivation chain is an open-loop MPC formulation, not a statistical or first-principles prediction. The switched dynamics (1)-(2) and the soft reachability penalty (6) are modeling/design choices; the cost (9) and constraints (13)-(17) define an optimization problem whose output is planned trajectories. No parameter is fitted to a target output and then reported as a prediction, no load-bearing self-citation is present, and no uniqueness theorem is imported from the authors' prior work. The only noted issue is that Section 3's statement that trajectories are 'ensured to be feasible and collision-free' is under-supported because the soft penalty replaces the hard switch δdoor and no value for w_m is given. That is a correctness/robustness risk, not circularity: the claim does not reduce by construction to an input, and the feasibility of the planner is demonstrated externally through simulation and hardware rather than by assuming the conclusion. The paper is therefore self-contained with respect to circularity concerns.

Axiom & Free-Parameter Ledger

7 free parameters · 4 axioms · 0 invented entities

The central claim depends on standard geometric formulas plus domain assumptions about the door, robot shape, and unicycle dynamics. The most fragile item is the soft-penalty replacement of the hard reachability switch, which is introduced ad hoc for this paper and is not backed by a formal guarantee or reported parameter choice.

free parameters (7)
  • r_min
    Inner radius of the arm's reachable annulus; required to define δdoor and the manipulation penalty in Eq. (6). No value or measurement procedure is reported.
  • r_max
    Outer radius of the arm's reachable annulus; same role as r_min. No value or measurement procedure is reported.
  • w_m / w_manip
    Weight(s) on the manipulation penalty in Eqs. (9) and (12). Called 'high gain' but no numeric value is given; the entire reachability enforcement depends on this choice.
  • R_base
    Robot base collision radius used in constraint (15). No value is reported.
  • d_s
    Safety margin added to R_base in constraint (15). No value is reported.
  • dt, N
    Discretization step and horizon length for the MPC. No values are reported, though trajectory feasibility and computation time depend on them.
  • Q_f, R
    Terminal and control cost weights in Eqs. (10)-(11). No values are reported.
axioms (4)
  • domain assumption Door is a rigid body rotating about a fixed hinge, described by a single angle θdoor, with the handle at a fixed distance dhandle from the hinge.
    Section 2 Assumption 1; excludes latches, hinge friction, door deformation, and sensor-based door estimation errors.
  • domain assumption Robot base is represented as a circle and the door panel as a line segment for collision checking.
    Section 2 Assumption 4; the collision constraint (15) covers only the base footprint, not the arm, which is left to the execution-time IK barrier.
  • domain assumption Mobile base follows unicycle dynamics.
    Section 2 Assumption 3; used in Eq. (1) and for the tracking controller in Eq. (19). Valid for the differential-drive Husky and Realman robots, but limits applicability.
  • ad hoc to paper The soft penalty C_manip can replace the integer switch δdoor and enforce reachability.
    Eq. (6) removes δdoor from the dynamics while Eq. (14) keeps θdoor_dot = ωdoor at every step. This is the paper's central modeling relaxation, and it is not verified with a hard constraint or formal guarantee.

pith-pipeline@v1.3.0-alltime-deepseek · 7568 in / 11481 out tokens · 113966 ms · 2026-08-04T00:59:51.354520+00:00 · methodology

0 comments
read the original abstract

Navigating doorways is a fundamental capability for mobile manipulators operating in human environments, requiring coordinated motion between the mobile base and manipulator arm. This paper presents a motion planning framework that generates dynamically feasible and collision-free trajectories for autonomously opening and traversing both push and pull doors. The proposed method formulates the robot and door as a coupled dynamical system within a nonlinear Model Predictive Control (MPC) optimization framework. Manipulation feasibility is enforced through a penalty-based constraint, avoiding explicit arm kinematic modeling in the planner. Simulations and a hardware experiment demonstrate that the approach successfully plans feasible trajectories for door traversal.

Figures

Figures reproduced from arXiv: 2608.00206 by Christopher Bradley, Donald Ebeigbe, Kasra Sinaei, Kasun Weerakoon, Seyed Abolfazl Fakoorian.

Figure 1
Figure 1. Figure 1: Geometric parameters of the mobile manipulator [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Workspace of a mobile manipulator robotic sys [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Planned trajectory for a push-type door. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Simulation of a mobile manipulator in NVIDIA Isaac Sim; demonstration of MPC on push-type door. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Simulation of a mobile manipulator in NVIDIA Isaac Sim; demonstration of MPC on pull-type door. [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Snapshots of the hardware experiment using a commercial mobile manipulator. The robot is traversing a [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

33 extracted references · 3 linked inside Pith

  1. [1]

    IEEE Robotics and Automation Letters , volume=

    Motion planning of mobile manipulator for navigation including door traversal , author=. IEEE Robotics and Automation Letters , volume=. 2023 , publisher=

    2023

  2. [2]

    2010 IEEE International Conference on Robotics and Automation , pages=

    Planning for autonomous door opening with a mobile manipulator , author=. 2010 IEEE International Conference on Robotics and Automation , pages=. 2010 , organization=

    2010

  3. [3]

    URL https://github

    Pink: Python inverse kinematics based on Pinocchio, 2024 , author=. URL https://github. com/stephane-caron/pink , year =

    2024

  4. [4]

    Mathematical Programming Computation , volume=

    CasADi: a software framework for nonlinear optimization and optimal control , author=. Mathematical Programming Computation , volume=. 2019 , publisher=

    2019

  5. [5]

    Science Robotics , volume =

    Steven Macenski and Tully Foote and Brian Gerkey and Chris Lalancette and William Woodall , title =. Science Robotics , volume =. 2022 , doi =

    2022

  6. [6]

    Carpentier and G

    J. Carpentier and G. Saurel and G. Buondonno and J. Mirabel and F. Lamiraux and O. Stasse and N. Mansard , booktitle=. The

  7. [7]

    Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems

    An experiment on opening-door-behavior by an autonomous mobile robot with a manipulator , author=. Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots , volume=. 1995 , organization=

    1995

  8. [8]

    IEEE Transactions on Automation Science and Engineering , volume=

    Manipulator motion planning for part pickup and transport operations from a moving base , author=. IEEE Transactions on Automation Science and Engineering , volume=. 2020 , publisher=

    2020

  9. [9]

    IEEE Access , volume=

    Enhancing autonomous door traversal for mobile manipulators using behavior trees , author=. IEEE Access , volume=. 2024 , publisher=

    2024

  10. [10]

    2019 International Conference on Robotics and Automation (ICRA) , pages=

    Door opening and traversal with an industrial cartesian impedance controlled mobile robot , author=. 2019 International Conference on Robotics and Automation (ICRA) , pages=. 2019 , organization=

    2019

  11. [11]

    2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=

    Real-time whole-body motion planning for mobile manipulators using environment-adaptive search and spatial-temporal optimization , author=. 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=. 2024 , organization=

    2024

  12. [12]

    Mathematical Problems in Engineering , volume=

    Optimal Energy Consumption for Mobile Manipulators Executing Door-Opening Task , author=. Mathematical Problems in Engineering , volume=. 2018 , publisher=

    2018

  13. [13]

    arXiv preprint arXiv:2508.12980 , year=

    Scaling Whole-body Multi-contact Manipulation with Contact Optimization , author=. arXiv preprint arXiv:2508.12980 , year=

    arXiv

  14. [14]

    Machines , volume=

    Motion planning for mobile manipulators—a systematic review , author=. Machines , volume=. 2022 , publisher=

    2022

  15. [15]

    arXiv preprint arXiv:2401.02117 , year=

    Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation , author=. arXiv preprint arXiv:2401.02117 , year=

    Pith/arXiv arXiv

  16. [16]

    Journal of Mechanisms and Robotics , volume=

    A survey of wheeled mobile manipulation: A decision-making perspective , author=. Journal of Mechanisms and Robotics , volume=. 2023 , publisher=

    2023

  17. [17]

    Sensors , volume=

    Mobile manipulators in industry 4.0: A review of developments for industrial applications , author=. Sensors , volume=. 2023 , publisher=

    2023

  18. [18]

    Annual Review of Control, Robotics, and Autonomous Systems , volume=

    Physically assistive robots: A systematic review of mobile and manipulator robots that physically assist people with disabilities , author=. Annual Review of Control, Robotics, and Autonomous Systems , volume=. 2023 , publisher=

    2023

  19. [19]

    arXiv preprint arXiv:2401.14403 , year=

    Adaptive mobile manipulation for articulated objects in the open world , author=. arXiv preprint arXiv:2401.14403 , year=

    Pith/arXiv arXiv

  20. [20]

    2020 IEEE International Conference on Robotics and Automation (ICRA) , pages=

    Aerial manipulation using model predictive control for opening a hinged door , author=. 2020 IEEE International Conference on Robotics and Automation (ICRA) , pages=. 2020 , organization=

    2020

  21. [21]

    IEEE Robotics and Automation Letters , volume=

    Combining navigation and manipulation costs for time-efficient robot placement in mobile manipulation tasks , author=. IEEE Robotics and Automation Letters , volume=. 2022 , publisher=

    2022

  22. [22]

    Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

    Momanipvla: Transferring vision-language-action models for general mobile manipulation , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

  23. [23]

    2024 3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC) , pages=

    Self-adaptive robust motion planning for high dof robot manipulator using deep mpc , author=. 2024 3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC) , pages=. 2024 , organization=

    2024

  24. [24]

    2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages=

    Nmpc-mp: Real-time nonlinear model predictive control for safe motion planning in manipulator teleoperation , author=. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages=. 2021 , organization=

    2021

  25. [25]

    IEEE/CAA Journal of Automatica Sinica , volume=

    MPC-based motion planning and control enables smarter and safer autonomous marine vehicles: Perspectives and a tutorial survey , author=. IEEE/CAA Journal of Automatica Sinica , volume=. 2022 , publisher=

    2022

  26. [26]

    IFAC-PapersOnLine , volume=

    Robust Data-Driven Receding Horizon Control1 , author=. IFAC-PapersOnLine , volume=. 2025 , publisher=

    2025

  27. [27]

    IEEE Transactions on Automation Science and Engineering , volume=

    Learning robust model predictive control for voltage control of islanded microgrid , author=. IEEE Transactions on Automation Science and Engineering , volume=. 2024 , publisher=

    2024

  28. [28]

    arXiv preprint arXiv:2504.09358 , year=

    DoorBot: Closed-Loop Task Planning and Manipulation for Door Opening in the Wild with Haptic Feedback , author=. arXiv preprint arXiv:2504.09358 , year=

    Pith/arXiv arXiv

  29. [29]

    2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages=

    Estimating door shape and manipulation model for daily assistive robots based on the integration of visual and touch information , author=. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages=. 2018 , organization=

    2018

  30. [30]

    2017 , publisher=

    Predictive control for linear and hybrid systems , author=. 2017 , publisher=

    2017

  31. [31]

    Nonlinear systems , isbn =

    Khalil, Hassan K , year =. Nonlinear systems , isbn =

  32. [32]

    Introduction to mobile robot control , pages=

    Mobile robot control I , author=. Introduction to mobile robot control , pages=

  33. [33]

    IEEE Transactions on Automatic Control , volume=

    Control barrier function based quadratic programs for safety critical systems , author=. IEEE Transactions on Automatic Control , volume=. 2016 , publisher=

    2016