Pith. sign in

REVIEW 3 major objections 5 minor 46 references

Hierarchical Adaptive Motion Planning with Nonlinear Model Predictive Control for Safety-Critical Collaborative Loco-Manipulation

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A hierarchical planner with adaptive MPC and control barrier functions lets quadruped teams move unknown heavy objects through static and moving obstacles without knowing mass or friction.

desk verdict Adaptive NMPC + CBF planner for collaborative quadruped loco-manipulation is a real engineering contribution, but the safety guarantee doesn't follow from the implemented penalty-based constraints. read the letter →

arxiv 2411.10699 v1 pith:BIWXJLWU submitted 2024-11-16 cs.RO

Mohsen Sombolestan , Quan Nguyen This is my paper · ORCID
classification cs.RO
keywords safety-criticalmotionplanningleggedrobotscollaborativemanipulationloco-manipulationnonlinearMPCcontrolbarrierfunctionsadaptiveobstacleavoidance
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that a team of quadruped robots can safely carry a rigid object with unknown mass, inertia, center-of-mass location, and ground friction through an environment containing both static and moving obstacles, without any prior model of the payload. The argument is a hierarchical controller: a high-level nonlinear MPC planner generates collision-free object paths, pushing forces, and contact-point locations, while a decentralized loco-manipulation controller makes each robot track them. Safety is encoded with control barrier functions for both the manipulated object and each individual robot, and an adaptive law with a control Lyapunov constraint compensates for the unknown object properties. Comparative simulations and hardware experiments with a pair of different quadrupeds support the claim, showing that disabling either the adaptive law or the robot-level barrier functions leads to failure or collision. If correct, this moves multi-robot legged manipulation closer to unstructured, human-occupied environments.

What carries the argument

The load-bearing object is an adaptive, safety-critical nonlinear model predictive controller whose decision variables are the pushing-force magnitudes $f_{r,i}$ and contact-point positions $d_i$ for each robot. The planner uses a planar rigid-body model of the object split into a nominal part and a lumped uncertainty term $Y_{\Psi,r}\Psi$, estimated online by the update law $\dot{\hat{\Psi}} = -\Gamma_{\Psi} Y_{\Psi,r}^{\mathsf T} s$, where $s = \dot{q}_e + \lambda q_e$ is the composite tracking error. A control Lyapunov constraint built from $V = \tfrac{1}{2}(s^{\mathsf T} H s + \tilde{\Psi}^{\mathsf T} \Gamma_{\Psi}^{-1} \tilde{\Psi})$ keeps the tracking error asymptotically stable, while control barrier functions of relative degree 2 for the object and degree 1 for each robot, of the form $\|O_j - x_p\| - R_{j,m} \ge 0$ and $\|O_j - R_i\| - R_{j,r_i} \ge 0$, force collision avoidance. The hard constraints are relaxed as log-barrier penalties and the discretized problem is solved by sequential quadratic programming.

What would settle it

Run the two-robot hardware obstacle course while motion-capturing both the object and the robot bodies, and compare each robot's measured center position with the planned $R_i$ that feeds the robot–obstacle barrier; if the measured distance to the obstacle ever drops below the barrier radius while the planned barrier stays positive, the planner's safety certificate has been falsified.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that collaborative loco-manipulation by legged robots can be made both adaptive and safety-critical by blending a nonlinear MPC planner with control barrier functions and an adaptive dynamics model. The planner treats the object's configuration and each robot's contact point as states, optimizes perpendicular pushing forces and tangential contact-point velocities, and enforces (i) a CLF constraint that keeps the object tracking error asymptotically stable despite the unknown parameter vector, and (ii) CBF constraints that keep both the object and each robot outside obstacle safety radii. Because the adaptive estimator lumps all model uncertainty into a single regressor–parameter pair, the planner needs no mass, inertia, center-of-mass, or friction values. The paper validates this by comparing planner variants with and without the adaptive controller and with and without robot-level CBFs, and by hardware experiments in which two different quadruped models maneuver a box with an unknown 3 kg load around an obstacle.

Load-bearing premise

The whole safety argument depends on the robot actually being where the planner thinks it is; if the low-level controller's tracking error is larger than the collision margin, the planner's safety checks can pass while the real robot hits the obstacle.

Editorial extensions

If this is right

  • Teams of quadruped robots could carry payloads through warehouses and construction sites without re-tuning for each object's mass, inertia, center of mass, or ground friction, because those are lumped into the adaptive parameter vector.
  • The planner must track robot–obstacle safety separately from object–obstacle safety; the hardware comparison shows that removing only the robot barriers lets a robot collide even while the object path remains safe.
  • Formations with different numbers and models of robots are supported, since the low-level controller is decentralized and the planner only assumes unilateral pushing contacts at measured contact points.
  • Sharp turns and fast rotations are currently unreliable: because the robots cannot apply torque directly and only push, the hard interaction constraint can make the MPC infeasible for aggressive commands.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The safety certificate is only as good as the low-level tracking of the planned contact point; if the robot body lags the planner's $R_i$ by more than the barrier safety radius, the CBF constraint would certify a path that the physical robot does not follow, and the paper does not bound this tracking error.
  • Because the adaptive law lumps all uncertainty into one parameter vector, simultaneous changes in mass and friction could excite coupling effects; a useful extension would split the parameter vector by physical source and test identifiability.
  • The planner's CBF-and-CLF machinery is not tied to planar motion, so the same hierarchical design could in principle handle three-dimensional manipulation as long as the interaction remains unilateral and the dynamics remain control-affine.
  • A natural next experiment is to run the dynamic-obstacle scenario, currently shown only in simulation, on hardware with a person walking through the workspace; the fixed safety radii and replanning rate can then be tested against real motion-capture latency.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes a hierarchical control architecture for a team of quadruped robots collaboratively manipulating a rigid object with unknown mass, inertia, center-of-mass, and friction parameters. A high-level adaptive nonlinear MPC planner generates manipulation forces and contact-point locations while enforcing stability and safety through CLF and CBF constraints, and a decentralized loco-manipulation MPC tracks the planned contact forces and contact points on each robot. The authors validate the approach in Gazebo simulations with static and dynamic obstacles and in hardware experiments with a Unitree A1 and an Aliengo, and they release the code as open source. The paper argues that the adaptive planner handles unknown object properties and that the CBF layer prevents collisions between the object, the robots, and the environment.

Significance. If the claimed safety guarantees held in the implemented system, this would be a valuable step toward practical multi-robot legged loco-manipulation in uncertain environments. The adaptive parameterization of the object dynamics, the comparative studies with and without the adaptive controller and with and without the robot-level CBFs, and the hardware demonstrations with two different quadruped models are useful contributions, and the open-source code increases reproducibility. However, the central 'safety-critical' claim is not supported by the implementation: the CBF and CLF constraints are converted into soft penalties, and the robot-center quantity appearing in the robot-obstacle barrier is never defined as a function of the planner state. The paper is therefore best read as a strong experimental demonstration rather than a formal safety-guarantee result.

major comments (3)
  1. [Sec. 6.6, Eqs. (47), (50), (51)] The theoretical safety guarantee does not apply to the implemented planner. The paper states in the introduction and conclusion that the motion planner 'guarantees safety', and Theorem 1 with Definition 2 provide forward invariance when the CBF inequality (9)/(13) is satisfied pointwise. However, the solved NLP (50) retains only the dynamic equality constraint, because all inequality constraints, including h_cbf, are replaced by the penalty cost l_P in (48). Since the discretized problem (49) is not the problem actually solved, the condition required by Theorem 1 is not enforced at the solution, and the safety guarantee is not established. The hardware and simulation ablations (Figs. 7 and 9) show that the penalty term improves behavior in the tested scenarios, but they do not test the guarantee. Please either enforce the CBF constraints in the optimizer, provide a conservative penalty formulation with quantified violation bounds, or revise the 'guaranteed safety' claims to match the implemented soft-constrained formulation.
  2. [Sec. 6.5, Eq. (45b)] The robot-obstacle barrier B^{ri}_{oj} depends on R_i, the robot center position, but the paper never states R_i as a function of the MPC state x = [x_b^T, d^T]^T and input u. The only quantity defined in the state is the contact point r_i in Eq. (33b), while the actual robot body position is produced by the decentralized loco-manipulation controller of Sec. 3.3, whose tracking error is not bounded in the paper. Consequently, the Lie derivative in (46b) is not computable from the planner state, and the safety variable used in the CBF may not correspond to the true robot position. Please provide an explicit expression R_i(q_b, d_i) and a conservative safety margin or a tracking-error bound that ensures the actual robot body remains collision-free when the high-level barrier is satisfied.
  3. [Table 1 and Fig. 5] The reported maximum manipulation force is Fmax = 0.7 N, but the force plots in Fig. 5 show forces of roughly 10 to 50 N in the successful simulation run. If the force bound (35) is only a soft penalty through (47)-(48), the planner can violate it at nonzero cost; if Fmax is intended to be 50 N, the table is incorrect. Either way, the parameter set reported in Table 1 is inconsistent with the experimental data, and this inconsistency should be corrected and the penalty parameters (rho, epsilon)_bound explained for the force limit.
minor comments (5)
  1. [Sec. 6.6, Eqs. (49)-(50)] The presentation is inconsistent: problem (49) lists the inequality constraints h_bound, h_clf, and h_cbf as explicit constraints, but Sec. 6.6 and problem (50) state that they are replaced by penalties. The reader should be told clearly that (49) is the conceptual problem and (50) is the solved problem.
  2. [Sec. 5.1] The procedure that converts the estimated travel time t_avg and the horizon T into the subgoal configurations x_ref_b is not described. A definition of how subgoals are generated incrementally would improve reproducibility.
  3. [Eq. (16b)] The block structure of the matrix \bar{D}_i appears to have misaligned sub-block dimensions; the reader should be able to verify that the matrix is R^{15 x 15}.
  4. [Sec. 6.4, Eq. (44)] The CLF constraint is also implemented as a soft penalty, so the asymptotic stability conclusion of Sec. 6.4 holds only if the constraint is satisfied at every time step; this should be acknowledged alongside the CBF discussion.
  5. [Table 1] The CBF parameters (alpha, beta)_CBF are reported as a single pair, but Eqs. (46a) and (46b) use separate coefficients beta^m, alpha^m, and alpha^{ri} for the object and robot barriers; the table should clarify whether these are identical for all barriers.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: the adaptive planner, CLF, and CBF constraints are derived in-paper from standard Slotine-Li and Ames results; the main self-citation is the reused decentralized loco-manipulation controller, which is a component, not the predicted quantity.

full rationale

The paper's central claimed contribution, the adaptive safety-critical motion planner, is derived self-containedly. The adaptive dynamics decompose the uncertain object model into a regressor and unknown parameter vector (Eqs. 21-23), the adaptation law is stated in Eq. 24, and the CLF constraint in Eq. 44 is obtained by differentiating the Lyapunov candidate (38) and substituting the adaptation law, following the standard Slotine-Li machinery. The CBF constraints in Eqs. 45-46 are standard exponential CBF conditions with stated relative degrees, and the safety variables are the object position and the robot center/contact location, not quantities fitted to the demonstrated outcomes. The comparative simulations (with/without adaptive controller, with/without robot CBFs) provide external falsifiability: the planner without these components fails, showing the components are doing work rather than being renamed predictions. The main self-citation is the decentralized loco-manipulation controller imported from Sombolestan and Nguyen (2023b), used as a fixed low-level component; the paper's planner-level claims do not reduce to that citation, and prior work is independently published rather than an unverified uniqueness theorem. A correctness gap exists but is not circularity: Sec. 6.6 replaces all inequality constraints, including the CBF constraints (26d), with relaxed log-barrier penalties (47)-(48), so the implemented NLP (50) does not enforce h_cbf >= 0 pointwise; this undermines the forward-invariance guarantee of Theorem 1 but does not make the derivation equivalent to its inputs. Similarly, Sec. 7.4 states a real feasibility limitation of the interaction constraints, which is not a circular step. Overall, no load-bearing circular reduction was found; score reflects only the minor reuse of the authors' prior low-level controller.

Assumptions & free parameters 15 free parameters · 6 assumptions · 0 invented entities

The central result depends on several hand-tuned gains and penalties and on domain assumptions about planar motion, push-only contacts, and spherical obstacles. The safety guarantee assumes the estimated model is accurate enough for CBF evaluation and that the CLF constraint is satisfied, which the penalty formulation does not enforce.

free parameters (15)
  • Gamma_Psi = diag(3,2,1,1)x10^2
    Adaptation gain for uncertainty vector; hand-tuned in simulation, not fitted to a benchmark.
  • lambda = 3
    Composite tracking error weight in (19); tuned.
  • Qf = diag(150,150,3,3,3,8)
    Terminal cost weights for MPC; tuned.
  • Qxb = diag(20,22,2,3,3,1)x10^-1
    State tracking weights; tuned.
  • Qd = I_Nr x 10^-1
    Contact point smoothness weight; tuned.
  • Ru = I_{2xNr} x 10^-2
    Input regularization; tuned.
  • Fmax = 0.7 N (as printed)
    Maximum push force; inconsistent with force plots showing up to 50 N; likely unit error.
  • vmax = 1 m/s
    Max rate of contact point motion; tuned.
  • alpha_CBF, beta_CBF = 4, 4
    ECBF gains in (46); tuned.
  • rho_CBF, epsilon_CBF = 0.8, 0.5
    Penalty parameters for CBF constraints; tuned.
  • rho_CLF, epsilon_CLF = 1, 0.5
    Penalty parameters for CLF constraint; tuned.
  • rho_bound, epsilon_bound = 0.1, 0.01
    Penalty parameters for state/input bounds; tuned.
  • KD = 3I3
    CLF dissipation weight in (43); tuned.
  • v_avg, omega_avg = 0.5 m/s, 0.8 rad/s
    Assumed average velocities for reference trajectory time estimate in (18); chosen by hand.
  • Obstacle radii R_j,m, R_j,ri = not listed
    Barrier radii for safety margins; chosen by user per scenario but not reported.
assumptions (6)
  • domain assumption The object's motion is restricted to the plane; 2D dynamics (2) suffice.
    Invoked in Sec 3.1 to derive compact equation of motion; real object may tilt or leave plane.
  • domain assumption Friction at robot-object contacts is negligible; robots apply only normal push forces.
    Stated in Sec 2; allows scalar force representation (33).
  • domain assumption Obstacles are spheres with known centers and radii.
    Used to define barrier functions (45) in Sec 6.5.
  • ad hoc to paper The estimated parameter vector Psi_hat is bounded and the adaptation law (24) is active continuously.
    The CLF stability proof in Sec 6.4 assumes the adaptation law and the constraint V_dot+W <= 0 hold; in the implementation this constraint is only a penalty term in (48), so the assumption is not guaranteed.
  • ad hoc to paper The robot center R_i is a known function of the planned contact point d_i and object pose.
    Needed for the robot-obstacle CBF (45b), but the functional form is never written.
  • standard math Skew-symmetry of H_dot-2C and standard adaptive-control properties from Slotine and Li apply.
    Used in Sec 6.4 to derive the CLF constraint; standard result.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Hierarchical Adaptive Motion Planning with Nonlinear Model Predictive Control for Safety-Critical Collaborative Loco-Manipulation." pith.science (2026). https://pith.science/paper/BIWXJLWU

@misc{pith2026241110699,
  author       = {Pith},
  title        = {Pith review of: Hierarchical Adaptive Motion Planning with Nonlinear Model Predictive Control for Safety-Critical Collaborative Loco-Manipulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BIWXJLWU}},
  note         = {Machine review of arXiv:2411.10699}
}
read the original abstract

As legged robots take on roles in industrial and autonomous construction, collaborative loco-manipulation is crucial for handling large and heavy objects that exceed the capabilities of a single robot. However, ensuring the safety of these multi-robot tasks is essential to prevent accidents and guarantee reliable operation. This paper presents a hierarchical control system for object manipulation using a team of quadrupedal robots. The combination of the motion planner and the decentralized locomotion controller in a hierarchical structure enables safe, adaptive planning for teams in complex scenarios. A high-level nonlinear model predictive control planner generates collision-free paths by incorporating control barrier functions, accounting for static and dynamic obstacles. This process involves calculating contact points and forces while adapting to unknown objects and terrain properties. The decentralized loco-manipulation controller then ensures each robot maintains stable locomotion and manipulation based on the planner's guidance. The effectiveness of our method is carefully examined in simulations under various conditions and validated in real-life setups with robot hardware. By modifying the object's configuration, the robot team can maneuver unknown objects through an environment containing both static and dynamic obstacles. We have made our code publicly available in an open-source repository at \url{https://github.com/DRCL-USC/collaborative_loco_manipulation}.

Figures

Figures reproduced from arXiv: 2411.10699 by the authors.

Figure 1
Figure 1. Snapshots of collaborative object manipulation [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Schematic of collaborative object manipulation [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Schematic of object manipulation considering the [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Block diagram of our proposed approach. Our approach includes 1) state estimation for object and obstacle states; 2) a safety-critical motion planner that utilizes an adaptive controller, CLF, and CBFs for team safety and obstacle avoidance within an MPC framework; and…
Figure 5
Figure 5. Figure 5: Comparing the performance of the motion planner with and without the adaptive controller. In the snapshots, the green box is the manipulated object, the red cube is the user-defined target location, and the two blue boxes are static obstacles. The green line represents…
Figure 7
Figure 7. Figure 7: Performance comparison of the motion planner [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 6
Figure 6. Figure 6: Impact of Initial Robot Configuration on [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 8
Figure 8. Figure 8: Experimental Results. Two Unitree robots (an Aliengo and an A1) manipulate the object while avoiding the obstacle [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: Experimental Result without Employing the CBFs [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: Simulation of quadruped robots encountering dynamic obstacles. [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

46 extracted references · 20 canonical work pages

  1. [1]

    In: 2019 18th European Control Conference, ECC 2019

    Ames AD, Coogan S, Egerstedt M, Notomista G, Sreenath K and Tabuada P (2019) Control barrier functions: Theory and applications . In: 2019 18th European Control Conference, ECC 2019. IEEE. ISBN 9783907144008, pp. 3420--3431. doi:10.23919/ECC.2019.8796030

    arXiv 2019

  2. [2]

    Proceedings of the IEEE Conference on Decision and Control 2015-February(February): 6271--6278

    Ames AD, Grizzle JW and Tabuada P (2014) Control barrier function based quadratic programs with application to adaptive cruise control . Proceedings of the IEEE Conference on Decision and Control 2015-February(February): 6271--6278. doi:10.1109/CDC.2014.7040372

    work page arXiv 2014

  3. [3]

    IEEE Transactions on Automatic Control 62(8): 3861--3876

    Ames AD, Xu X, Grizzle JW and Tabuada P (2017) Control Barrier Function Based Quadratic Programs for Safety Critical Systems . IEEE Transactions on Automatic Control 62(8): 3861--3876. doi:10.1109/TAC.2016.2638961

    arXiv 2017

  4. [4]

    In: IEEE International Conference on Intelligent Robots and Systems

    Bledt G, Powell MJ, Katz B, Di Carlo J, Wensing PM and Kim S (2018) MIT Cheetah 3: Design and Control of a Robust, Dynamic Quadruped Robot . In: IEEE International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc. ISBN 9781538680940, pp. 2245--2252. doi:10.1109/IROS.2018.8593885

    arXiv 2018

  5. [5]

    In: Proceedings - IEEE International Conference on Robotics and Automation

    Chiu JR, Sleiman JP, Mittal M, Farshidian F and Hutter M (2022) A Collision-Free MPC for Whole-Body Dynamic Locomotion and Manipulation . In: Proceedings - IEEE International Conference on Robotics and Automation. IEEE. ISBN 9781728196817, pp. 4686--4693. doi:10.1109/ICRA46639.2022.9812280

    arXiv 2022

  6. [6]

    IEEE Transactions on Robotics 37(6)

    Culbertson P, Slotine JJ and Schwager M (2021) Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies; Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies . IEEE Transactions on Robotics 37(6). doi:10.1109/TRO.2021

    doi:10.1109/tro.2021 2021

  7. [7]

    In: IEEE International Conference on Intelligent Robots and Systems

    Di Carlo J, Wensing PM, Katz B, Bledt G and Kim S (2018) Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control . In: IEEE International Conference on Intelligent Robots and Systems. IEEE. ISBN 9781538680940, pp. 7440--7447. doi:10.1109/IROS.2018.8594448

    arXiv 2018

  8. [8]

    Elsevier Science

    Fadali MS and Visioli A (2012) Digital Control Engineering: Analysis and Design . Elsevier Science. ISBN 9780123983244

    work page 2012

Show all 46 references
  1. [9]

    ://github.com/leggedrobotics/ocs2

    Farshidian F and others (2017) OCS2: An open source library for Optimal Control of Switched Systems . ://github.com/leggedrobotics/ocs2

    2017

  2. [10]

    Automatica 80: 328--339

    Feller C and Ebenbauer C (2017) A stabilizing iteration scheme for model predictive control based on relaxed barrier functions . Automatica 80: 328--339. doi:10.1016/j.automatica.2017.02.001

    2017 doi

  3. [11]

    Mathematical Programming Computation 6(4): 327--363

    Ferreau HJ, Kirches C, Potschka A, Bock HG and Diehl M (2014) qpOASES: a parametric active-set algorithm for quadratic programming . Mathematical Programming Computation 6(4): 327--363. doi:10.1007/S12532-014-0071-1/TABLES/5

    2014 doi

  4. [12]

    Autonomous Robots 47(8): 1463--1481

    Ferrolho H, Ivan V, Merkt W, Havoutis I and Vijayakumar S (2023) RoLoMa: robust loco-manipulation for quadruped robots with arms . Autonomous Robots 47(8): 1463--1481. doi:10.1007/s10514-023-10146-0

    2023 doi

  5. [13]

    In: Proceedings - IEEE International Conference on Robotics and Automation

    Fink J, Ani Hsieh M and Kumar V (2008) Multi-robot manipulation via caging in environments with obstacles . In: Proceedings - IEEE International Conference on Robotics and Automation. IEEE. ISBN 9781424416479, pp. 1471--1476. doi:10.1109/ROBOT.2008.4543409

    2008

  6. [14]

    Autonomous Robots 41(1): 259--272

    Focchi M, del Prete A, Havoutis I, Featherstone R, Caldwell DG and Semini C (2017) High-slope terrain locomotion for torque-controlled quadruped robots . Autonomous Robots 41(1): 259--272. doi:10.1007/s10514-016-9573-1

    2017 doi

  7. [15]

    IEEE Transactions on Robotics 39(5): 3402--3421

    Grandia R, Jenelten F, Yang S, Farshidian F and Hutter M (2023) Perceptive Locomotion Through Nonlinear Model-Predictive Control . IEEE Transactions on Robotics 39(5): 3402--3421. doi:10.1109/TRO.2023.3275384

    2023

  8. [16]

    In: Proceedings of the 45th IEEE Conference on Decision and Control

    Hauser J and Saccon A (2006) A Barrier Function Method for the Optimization of Trajectory Functionals with Constraints . In: Proceedings of the 45th IEEE Conference on Decision and Control. IEEE. ISBN 1-4244-0171-2, pp. 864--869. doi:10.1109/CDC.2006.377331

    2006

  9. [17]

    The International Journal of Robotics Research 14(4): 351--365

    Hu Yr, Goldenberg AA and Zhou C (1995) Motion and Force Control of Coordinated Robots During Constrained Motion Tasks . The International Journal of Robotics Research 14(4): 351--365. doi:10.1177/027836499501400404

    1995 doi

  10. [18]

    In: Proceedings of the 4th International Symposium on Robotics Research

    Khatib O (1988) Object manipulation in a multi-effector robot system . In: Proceedings of the 4th International Symposium on Robotics Research. Cambridge, MA, USA: MIT Press. ISBN 0262022729, pp. 137--144

    1988

  11. [19]

    The International Journal of Robotics Research 18(7): 684--696

    Khatib O, Yokoi K, Brock O, Chang K and Casal A (1999) Robots in Human Environments: Basic Autonomous Capabilities . The International Journal of Robotics Research 18(7): 684--696. doi:10.1177/02783649922066501

    1999 doi

  12. [20]

    Journal of Robotic Systems 13(11): 755--764

    Khatib O, Yokoi K, Chang K, Ruspini D, Holmberg R and Casal A (1996) Coordination and decentralized cooperation of multiple mobile manipulators . Journal of Robotic Systems 13(11): 755--764. doi:10.1002/(SICI)1097-4563(199611)13:11<755::AID-ROB6>3.0.CO;2-U

    1996 doi

  13. [21]

    IEEE Transactions on Robotics 39(6): 4728--4748

    Kim J, Fawcett RT, Kamidi VR, Ames AD and Hamed KA (2023) Layered Control for Cooperative Locomotion of Two Quadrupedal Robots: Centralized and Distributed Approaches . IEEE Transactions on Robotics 39(6): 4728--4748. doi:10.1109/TRO.2023.3319896

    2023

  14. [22]

    In: Proceedings of the IEEE Conference on Decision and Control, volume 2021-Decem

    Li J and Nguyen Q (2021) Force-and-moment-based Model Predictive Control for Achieving Highly Dynamic Locomotion on Bipedal Robots . In: Proceedings of the IEEE Conference on Decision and Control, volume 2021-Decem. IEEE. ISBN 9781665436595, pp. 1024--1030. doi:10.1109/CDC4548...

    2021

  15. [23]

    Mechatronics 18(5-6): 239--250

    Li Z, Ge SS and Wang Z (2008) Robust adaptive control of coordinated multiple mobile manipulators . Mechatronics 18(5-6): 239--250. doi:10.1016/j.mechatronics.2008.01.001

    2008 doi

  16. [24]

    The International Journal of Robotics Research 17(3): 232--247

    Liu YH and Arimoto S (1998) Decentralized Adaptive and Nonadaptive Position/Force Controllers for Redundant Manipulators in Cooperations . The International Journal of Robotics Research 17(3): 232--247. doi:10.1177/027836499801700302

    1998 doi

  17. [25]

    In: Proceedings of the American Control Conference, volume 2016-July

    Nguyen Q and Sreenath K (2016) Exponential Control Barrier Functions for enforcing high relative-degree safety-critical constraints . In: Proceedings of the American Control Conference, volume 2016-July. IEEE. ISBN 9781467386821, pp. 322--328. doi:10.1109/ACC.2016.7524935

    2016

  18. [26]

    Springer Series in Operations Research and Financial Engineering

    Nocedal J and Wright SJ (2006) Numerical Optimization . Springer Series in Operations Research and Financial Engineering. Springer New York. ISBN 978-0-387-40065-5. doi:10.1007/978-0-387-40065-5

    2006 doi

  19. [27]

    ://arxiv.org/abs/2406.17279v1

    Pandit B, Gupta A, Gadde MS, Johnson A, Shrestha AK, Duan H, Dao J and Fern A (2024) Learning Decentralized Multi-Biped Control for Payload Transport . ://arxiv.org/abs/2406.17279v1

    2024 arXiv

  20. [28]

    In: Springer Handbook of Robotics

    Prattichizzo D and Trinkle JC (2008) Grasping . In: Springer Handbook of Robotics. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 671--700. 10.1007/978-3-540-30301-5 \_ 29

    2008 doi

  21. [30]

    IEEE Robotics and Automation Letters 6(3): 4688--4695

    Sleiman JP, Farshidian F, Minniti MV and Hutter M (2021) A Unified MPC Framework for Whole-Body Dynamic Locomotion and Manipulation . IEEE Robotics and Automation Letters 6(3): 4688--4695. doi:10.1109/LRA.2021.3068908

    2021

  22. [31]

    Prentice hall Englewood Cliffs, NJ

    Slotine JJE and Li W (1991) Applied nonlinear control , volume 199. Prentice hall Englewood Cliffs, NJ

    1991

  23. [32]

    In: IEEE International Conference on Intelligent Robots and Systems

    Sombolestan M, Chen Y and Nguyen Q (2021) Adaptive Force-based Control for Legged Robots . In: IEEE International Conference on Intelligent Robots and Systems. IEEE. ISBN 9781665417143, pp. 7440--7447. doi:10.1109/IROS51168.2021.9636393

    2021

  24. [33]

    In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    Sombolestan M and Nguyen Q (2023 a ) Hierarchical Adaptive Control for Collaborative Manipulation of a Rigid Object by Quadrupedal Robots . In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE. ISBN 978-1-6654-9190-7, pp. 2752--2759. doi:10...

    2023

  25. [34]

    In: Proceedings - IEEE International Conference on Robotics and Automation, volume 2023-May

    Sombolestan M and Nguyen Q (2023 b ) Hierarchical Adaptive Loco-manipulation Control for Quadruped Robots . In: Proceedings - IEEE International Conference on Robotics and Automation, volume 2023-May. IEEE. ISBN 9798350323658, pp. 12156--12162. doi:10.1109/ICRA48891.2023.10160523

    2023

  26. [35]

    IEEE Transactions on Robotics 40: 2462--2477

    Sombolestan M and Nguyen Q (2024) Adaptive-Force-Based Control of Dynamic Legged Locomotion Over Uneven Terrain . IEEE Transactions on Robotics 40: 2462--2477. doi:10.1109/TRO.2024.3381554

    2024

  27. [36]

    In: Blondel V, Sontag ED, Vidyasagar M and Willems JC (eds.) Open Problems in Mathematical Systems and Control Theory

    Sontag ED (1999) Control-Lyapunov functions . In: Blondel V, Sontag ED, Vidyasagar M and Willems JC (eds.) Open Problems in Mathematical Systems and Control Theory. London: Springer London. ISBN 978-1-4471-0807-8, pp. 211--216. 10.1007/978-1-4471-0807-8 \_ 40

    1999 doi

  28. [37]

    In: 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

    Tallamraju R, Verma P, Sripada V, Agrawal S and Karlapalem K (2019) Energy Conscious Over-actuated Multi-Agent Payload Transport Robot: Simulations and Preliminary Physical Validation . In: 2019 28th IEEE International Conference on Robot and Human Interactive Communication (R...

    2019

  29. [38]

    In: Proceedings

    Tarn T, Bejczy A and Yun X (1986) Coordinated control of two robot arms . In: Proceedings. 1986 IEEE International Conference on Robotics and Automation. IEEE. ISBN 0818606959, pp. 1193--1202. doi:10.1109/ROBOT.1986.1087606

    1986

  30. [39]

    ://arxiv.org/abs/2403.19862v2

    Turrisi G, Schulze L, Medeiros VS, Semini C and Barasuol V (2024) PACC: A Passive-Arm Approach for High-Payload Collaborative Carrying with Quadruped Robots Using Model Predictive Control . ://arxiv.org/abs/2403.19862v2

    2024 arXiv

  31. [40]

    IFAC-PapersOnLine 50(1): 1754--1759

    Verginis CK, Mastellaro M and Dimarogonas DV (2017) Robust Quaternion-based Cooperative Manipulation without Force/Torque Information . IFAC-PapersOnLine 50(1): 1754--1759. doi:10.1016/j.ifacol.2017.08.526

    2017 doi

  32. [41]

    In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    Wolfslag WJ, McGreavy C, Xin G, Tiseo C, Vijayakumar S and Li Z (2020) Optimisation of Body-ground Contact for Augmenting the Whole-Body Loco-manipulation of Quadruped Robots . In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE. ISBN 978-...

    2020

  33. [42]

    IEEE Robotics and Automation Letters 7(4): 10041--10048

    Yang C, Sue GN, Li Z, Yang L, Shen H, Chi Y, Rai A, Zeng J and Sreenath K (2022) Collaborative Navigation and Manipulation of a Cable-Towed Load by Multiple Quadrupedal Robots . IEEE Robotics and Automation Letters 7(4): 10041--10048. doi:10.1109/LRA.2022.3191170

    2022

  34. [43]

    In: Proceedings - IEEE International Conference on Robotics and Automation, volume 2021-May

    Zimmermann S, Poranne R and Coros S (2021) Go Fetch! - Dynamic Grasps using Boston Dynamics Spot with External Robotic Arm . In: Proceedings - IEEE International Conference on Robotics and Automation, volume 2021-May. IEEE. ISBN 9781728190778, pp. 1170--1176. doi:10.1109/ICRA4...

    2021

  35. [44]

    , " * write output.state after.block = add.period write newline

    ENTRY address author booktitle chapter doi edition editor eid howpublished institution isbn journal key month note number organization pages publisher school series title type url volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence...

  36. [45]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

  37. [46]

    , " * write output.state after.block = add.period write newline

    ENTRY address archive author booktitle chapter doi edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type url volume year label INTEGERS output.state before.all mid.sentence after.sentence aft...

  38. [47]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.