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REVIEW 4 major objections 5 minor 23 references

IK Seed Generator for Dual-Arm Human-like Physicality Robot with Mobile Base

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The paper claims that an initial guess scored high by a joint-limit-aware manipulability index raises the probability that a numerical IK solver solves inverse kinematics for a human-like dual-arm robot.

desk verdict Plausible integrated method for IK seed generation on human-like robots, but the central 'proof' that goodness predicts solve success is overstated and needs baselines and statistics. read the letter →

arxiv 2505.00871 v1 pith:EKJWHVQH submitted 2025-05-01 cs.RO cs.AI

Jun Takamatsu , Atsushi Kanehira , Kazuhiro Sasabuchi , Naoki Wake , Katsushi Ikeuchi This is my paper · ORCID
classification cs.ROcs.AI
keywords inversekinematicsIKseedgenerationjointlimitsscaledJacobianmanipulabilitygeneticalgorithmreachabilitymapdual-armmanipulation
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

Inverse kinematics on a compact, human-like robot fails not because the target is unreachable but because the numerical solver starts from a bad guess; for high-degree-of-freedom robots, solve success is known to depend on the initial guess. This paper proposes to choose that initial guess offline by maximizing a 'goodness' score: the manipulability index of a scaled Jacobian matrix in which each joint is weighted by its distance to its mechanical limit. A reachability map enumerates candidate arm postures for a given hand pose, and a genetic algorithm searches over the robot's base position and trunk/waist joints to find the whole-body state with the highest goodness for the task's target hand configurations. The authors report quantitative experiments, on a dual-arm human-like robot with a mobile base, in which seeds with higher goodness solved IK at higher rates on average, and they demonstrate the resulting motions in grasping from several approach directions, a two-arm pouring task, and a regrasping reorientation. The claim that better goodness raises solve probability is the load-bearing result of the paper.

What carries the argument

The machinery is the goodness function $f=\sqrt{\det(\tilde{J}(q)\tilde{J}(q)^T)}$, the manipulability volume of the joint-limit-scaled velocity ellipsoid. Each joint's contribution is scaled by $d_i=\min(q_{i,\max}-q_i,\ q_i-q_{i,\min},\ d_{\max})$, so postures with any joint near a limit receive low goodness. This scalar turns 'will the solver converge?' into a numeric fitness for the genetic algorithm; the reachability map supplies dense candidate arm configurations for each target hand pose, and the GA's gene encodes the mobile-base and trunk degrees of freedom, so the seed includes where the robot stands as well as how its arms are posed.

What would settle it

Collect, say, 200 candidate seeds for one target hand configuration, compute each seed's goodness value, then run the numerical IK solver with random target perturbations of the same size used in the paper (e.g., ±7 cm, ±5 deg) many times per seed. If a lower-goodness seed shows a statistically significant higher solve-success rate than a higher-goodness seed—the pattern hinted at by the paper's Scenario 1 comparison of the 25th-generation seed (97.97% total IK success) against the best seed (97.49%)—then goodness does not reliably increase solve probability as claimed.

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Extended reading notes

Core claim

The paper's central claim is that an initial guess judged better by the proposed goodness value increases the probability that a numerical IK solver solves the inverse kinematics within joint limits. Goodness is the manipulability index computed from the scaled Jacobian $\tilde{J}(q)=W_x^{-1}J(q)W_q$, where $W_q$ scales each joint velocity by the distance to the nearer joint limit, clipped at $d_{\max}$, and $W_x$ balances positional and rotational target errors. The paper treats this scalar as a predictor of the solver's convergence basin and optimizes it offline with a genetic algorithm over the robot state. In quantitative tests, the converged best seed reached $97.49\%$ IK success in the single-arm grasp scenario and $99.8\%$ in the dual-arm pouring scenario, both above early-generation seeds with lower goodness, although the paper notes one non-monotonic cell in the first scenario. The paper's conclusion is that the goodness value, not the solver choice, is the lever that makes IK reliable on physically limited humanoid hardware.

Load-bearing premise

The method rests on the assumption that a posture's goodness—computed from a linearized velocity model with joint-limit penalties—predicts whether the particular numerical IK solver will actually converge to a valid joint-limit-respecting solution; the paper tests this on two scenarios with one robot, and its own Scenario 1 results include a higher-goodness seed with slightly lower total success.

Editorial extensions

If this is right

  • Offline seed generation lets a human-like robot answer small online perturbations (target shifts from visual estimation or base-position error) by running one numerical IK solve from a precomputed good seed instead of replanning from scratch.
  • Because the optimized gene includes the mobile-base and trunk/waist degrees of freedom, the method chooses a whole-body stance, making dual-arm tasks such as pouring and regrasping come out of the same seed-selection procedure as single-arm grasps.
  • The reachability-map enumeration works for any arm with a spherical wrist and no analytical IK, so a differently sized or configured robot can be accommodated by rebuilding the map rather than hand-deriving joint mappings.
  • For multi-step or dual-arm tasks, the paper chooses the minimum goodness across all via-points and both arms; this makes the seed conservative and can be reweighted if some configurations matter more than others.
  • Better seeds do not remove the need for a good solver; they are complementary, and the method's benefit is largest for robots whose joint limits make the solver's convergence basin small.

Reading between the lines

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

  • The goodness metric is a linearized proxy; a natural extension is to replace it with an empirical per-candidate success probability computed by sampling the actual solver's convergence from each reachability-map candidate, which would directly optimize the quantity the paper measures.
  • The same seed generator could be extended to include collision avoidance by adding obstacle-distance terms to the fitness function; the paper avoids table collisions only through the chosen base ranges, not through optimization.
  • The near-monotonic results in the pouring scenario, where success drops sharply at via-points with low goodness, suggest that goodness may act as a feasibility threshold rather than a smooth ranking: once a seed clears the threshold, further goodness gains may not translate into higher IK success, which would explain the non-monotonic cell in Scenario 1.
  • If the coupling between scaled-Jacobian goodness and solver success holds on other robots, the goodness value could be used as a sampling bias for task-space motion planners, since it gives a cheap prior over which robot configurations are likely to admit IK solutions.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes an offline method for generating initial guesses (seeds) for numerical inverse kinematics (IK) solvers applied to a dual-arm humanoid robot with a mobile base. The core idea is to define a scalar goodness measure f based on the scaled Jacobian matrix, which incorporates joint-limit distances, and then optimize this measure with a genetic algorithm (GA) over whole-body configurations (mobile base, lifter, waist, arms). An arm-initial-guess provider uses a precomputed reachability map and an analytical wrist solver to generate candidate arm states for a given hand configuration. The paper claims, in the Abstract and Section VII, that an initial guess judged better by the goodness value increases the probability that IK is solved correctly. This claim is tested in two scenarios by comparing the IK solve success rates of seeds from different GA generations with different fitness values, and the method is demonstrated in three application scenarios.

Significance. If the central claim were established, the method would be a useful engineering contribution: it is solver-agnostic, offline-computable, and directly targets the joint-limit and manipulability difficulties that plague compact humanoid robots. The goodness measure is defined independently of any particular IK solver, and the evaluation uses a real external solver (bio-IK), so the evaluation is not circular. The paper also provides a substantial implementation component (reachability map, wrist solver, GA optimization) and physical robot demonstrations. However, the empirical evidence for the central claim is currently weak, and the evaluation design does not isolate the effect of the goodness value from other confounds. Strengthening the quantitative evaluation is essential for the claims to be credible.

major comments (4)
  1. [§V.B, Tables I–II] The data do not support the monotonic claim that higher goodness increases IK solve probability. In the first scenario, the converged best-fitness seed (f = 2.783×10⁻⁵) achieved 97.49% total IK success, while the 25th-generation seed (f = 2.355×10⁻⁵) achieved 97.97%; the Grasp success rates were 95.96% vs. 95.97%. The authors themselves concede in §V.B that 'the success ratio of IK may deteriorate slightly somewhat with respect to the fitness value.' This directly weakens the Abstract and §VII claim. Please either temper the claim to a non-monotonic 'generally improves' statement supported by a trend analysis across many seeds, or provide statistical evidence that the relationship is positive on average despite this exception.
  2. [§V.A, §V.B] Comparing selected GA generations does not isolate the goodness value as the causal factor. Later generations differ from earlier ones not only in the fitness of the best individual but also in population diversity, exploration history, and the specific candidate states retained. Thus generation index is a confounded proxy for goodness. The evaluation needs a baseline that varies fitness while controlling for other factors, for example: random seeds sampled from the same reachability map and stratified by fitness, or fixed analytical seeds, or a simpler heuristic seed (e.g., nearest reachability-map state). Without such baselines, the observed success rates cannot be attributed to the goodness value.
  3. [Tables I–IV] The success-rate comparisons lack statistical support. With roughly 100 random perturbations per condition and denominators as low as 54 (e.g., time step 4 in Table III), the reported differences (e.g., 96.41% vs. 97.49% in Table II) are within plausible sampling variation. Please report binomial confidence intervals, effect sizes, or a statistical test (e.g., a permutation test or McNemar's test for paired trials) to support the claim that a higher goodness value increases solve probability. Without such evidence, the differences may be noise.
  4. [§III.C, §IV.B] The predictive validity of the goodness measure is not robustly established because the metric depends on several hand-chosen parameters: the clipping distance d_max, the position/attitude weight w, the reachability-map radius threshold r, the grid resolution, and the GA settings. The paper does not analyze how the relationship between f and actual IK solve success depends on these choices. Since the central claim is that f predicts solver success, parameter sensitivity should be examined or at least discussed; otherwise the result could be specific to the particular parameter set chosen.
minor comments (5)
  1. [Abstract and §VII] The word 'prove' is too strong for an empirical evaluation; consider 'demonstrate' or 'provide evidence that' in both places.
  2. [§IV.B] The phrase 'one joint that has minimum moving range is fixed zero' is unclear; please specify which joint and why it is fixed.
  3. [Tables I and III] The denominators in later time steps decrease because failed earlier steps are not retried; please state this explicitly in both table captions, as the text does only for Table I.
  4. [§III.C, Eq. (1) and (5)] Equations (1) and (5) both define d_i, with (5) adding clipping. Please clarify that (5) is the operational definition used in the scaled Jacobian, and note the clipping threshold d_max is introduced only in (5).
  5. [Figures 6 and 7] The axes of the fitness-vs-generation plots are not labeled in the text; please add axis labels and a caption explaining the convergence criterion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the goodness metric is defined independently of the IK solver's success, and the evaluation benchmark is external to the metric.

full rationale

The paper's central claim is that an initial guess judged better by the goodness value f from Eq. (8) increases the probability that the numerical IK solver succeeds. This is an empirical claim, not a definitional one. The goodness value is defined from the scaled Jacobian matrix, joint-limit distances (Eqs. 1, 3, 5, 7, 8), and a clipping parameter d_max and position/attitude ratio w; none of these is fitted to the bio-IK success data. The evaluation in Section V uses bio-IK, an external solver (Section IV.A: 'We used bio-IK, implementation of [16]'), and measures success percentages against random perturbations of the target. Success is therefore not encoded in the fitness function by construction. The paper even acknowledges possible non-monotonicity in Section V.B: 'Since the index is calculated by linearizing an originally nonlinear issue, the success ratio of IK may deteriorate slightly somewhat with respect to the fitness value.' This concession further shows that the metric was not engineered to guarantee the result. The self-citations in the paper, such as [13] for human-demonstration-based IK initialization and [14] for the Seednoid platform, are descriptive background and are not load-bearing for the claimed predictive relationship. No uniqueness theorem is imported from the authors' prior work, no ansatz is smuggled in via citation, and no known result is renamed as a new contribution. The weaknesses of the evaluation, such as the absence of statistical significance tests, baselines, and the non-monotonic Table I/II results, are threats to the validity or strength of the empirical conclusion, not instances of circularity. For these reasons, the derivation chain is self-contained with respect to the circularity criteria, and the appropriate score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities. Its central contribution is a heuristic metric and an optimization procedure; the listed free parameters and assumptions are the hand-set choices the results depend on.

free parameters (5)
  • d_max = 0.25 rad
    Clipping value for joint distance in the scaled Jacobian (Eq. 5). Set by hand in Section IV.B; determines how close to joint limits the metric penalizes.
  • w = 1
    Ratio between position and orientation scaling in Wx (Eq. 6). Hand-set in Section IV.B; affects the balance of translational vs rotational manipulability.
  • r = 1 cm
    Distance threshold for selecting candidate lower-arm configurations from the reachability map (Section IV.B).
  • reachability map resolution = 2 deg joint sampling, 5 cm voxels with 2.5 cm overlap
    Sampling intervals for building the map; chosen for tractability, not fitted.
  • GA population and elites = 50 genes, 10 elites
    GA settings in Section IV.B; defaults from PyGAD elsewhere. Affect optimization quality but not fitted to success.
assumptions (4)
  • domain assumption The scaled-Jacobian manipulability index is a valid predictor of numerical IK solvability under joint limits.
    The entire method is built on this proxy; the paper supports it only empirically and with non-monotonic results.
  • domain assumption The robot's wrist joints form a spherical wrist and can be solved analytically from the lower-arm orientation.
    Section III.A states this assumption, citing [9]; it holds for the Seednoid testbed.
  • domain assumption GA after 300 generations converged to a good optimum.
    The paper stops when fitness is unchanged for 100 generations; no proof of global optimality.
  • domain assumption The reachability map sampled at 2-degree joint intervals and 5 cm grid provides sufficient coverage of IK solutions.
    The map is built by forward-kinematics sampling; this resolution is chosen for tractability, not validated for completeness.

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Cite this review

Pith. "Pith review of IK Seed Generator for Dual-Arm Human-like Physicality Robot with Mobile Base." pith.science (2026). https://pith.science/paper/EKJWHVQH

@misc{pith2026250500871,
  author       = {Pith},
  title        = {Pith review of: IK Seed Generator for Dual-Arm Human-like Physicality Robot with Mobile Base},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EKJWHVQH}},
  note         = {Machine review of arXiv:2505.00871}
}
read the original abstract

Robots are strongly expected as a means of replacing human tasks. If a robot has a human-like physicality, the possibility of replacing human tasks increases. In the case of household service robots, it is desirable for them to be on a human-like size so that they do not become excessively large in order to coexist with humans in their operating environment. However, robots with size limitations tend to have difficulty solving inverse kinematics (IK) due to mechanical limitations, such as joint angle limitations. Conversely, if the difficulty coming from this limitation could be mitigated, one can expect that the use of such robots becomes more valuable. In numerical IK solver, which is commonly used for robots with higher degrees-of-freedom (DOF), the solvability of IK depends on the initial guess given to the solver. Thus, this paper proposes a method for generating a good initial guess for a numerical IK solver given the target hand configuration. For the purpose, we define the goodness of an initial guess using the scaled Jacobian matrix, which can calculate the manipulability index considering the joint limits. These two factors are related to the difficulty of solving IK. We generate the initial guess by optimizing the goodness using the genetic algorithm (GA). To enumerate much possible IK solutions, we use the reachability map that represents the reachable area of the robot hand in the arm-base coordinate system. We conduct quantitative evaluation and prove that using an initial guess that is judged to be better using the goodness value increases the probability that IK is solved. Finally, as an application of the proposed method, we show that by generating good initial guesses for IK a robot actually achieves three typical scenarios.

Figures

Figures reproduced from arXiv: 2505.00871 by the authors.

Figure 1
Figure 1. More complicated operations: manipulation with both arms, such [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Examples of difficult situations to adjust hand configurations. In [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Seednoid mobile base moves only left and right (that shall correspond to the Y axis). To successfully grasp a cup without collision, we would like to set not only the hand configuration at the grasp, but also the configuration for pre-grasp (beginning of the grasping). As the result, the gene includes one y-value of the mobile base, the joint state at pre-grasp and the joint state at grasp. The process of fitness-va… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: First scenario: grasp from the x-direction. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: Fitness values of the best solution in each generation [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Fitness values of the best solution in each generation [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 10
Figure 10. Figure 10: Grasp while approaching from the front [PITH_FULL_IMAGE:figures/full_fig_p007_10.png]
Figure 11
Figure 11. Figure 11: shows how the robot actually performs the sce￾nario. The pouring position was manual adjusted for pouring well. Pouring was successfully performed. C. Re-orientation by Regrasping In the previous scenario, the robot grasped the cup placed upside down with twisting an …
Figure 8
Figure 8. Figure 8: Grasp while approaching from the side. Close-up views are [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 12
Figure 12. Figure 12: Re-orientate a cup by regrasping to achieve the grasp from side. [PITH_FULL_IMAGE:figures/full_fig_p008_12.png]

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Reference graph

Works this paper leans on

23 extracted references · 19 canonical work pages

  1. [1]

    Placement of robot manipu- lators to maximize dexterity,

    K. Abdel-Malek, W. Yu, and J. Yang, “Placement of robot manipu- lators to maximize dexterity,” International Journal of Robotics and Automation, vol. 19, no. 1, pp. 6–14, 2004. Fig. 12. Re-orientate a cup by regrasping to achieve the grasp from side

    work page 2004

  2. [2]

    Inverse kinematics techniques in computer graphics: A survey,

    A. Aristidou, J. Lasenby, Y . Chrysanthou, and A. Shamir, “Inverse kinematics techniques in computer graphics: A survey,” Computer Graphics F orum, vol. 37, no. 6, pp. 35–58, 2018. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.13310

    doi:10.1111/cgf.13310 2018

  3. [3]

    Trac-ik: An open-source library for improved solving of generic inverse kinematics,

    P. Beeson and B. Ames, “Trac-ik: An open-source library for improved solving of generic inverse kinematics,” in 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) , 2015, pp. 928–935

    work page 2015

  4. [4]

    A weighted least-norm solution based scheme for avoiding joint limits for redundant manipulators,

    T. Chan and R. Dubey, “A weighted least-norm solution based scheme for avoiding joint limits for redundant manipulators,” in Proceedings IEEE International Conference on Robotics and Automation , vol. 3, 1993, pp. 395–402

    work page 1993

  5. [5]

    Open X-Embodiment: Robotic learning datasets and RT-X models,

    O. X.-E. Collaboration, A. O’Neill, A. Rehman, A. Maddukuri, A. Gupta, A. Padalkar, A. Lee, A. Pooley, A. Gupta, A. Mandlekar, A. Jain, A. Tung, A. Bewley, A. Herzog, A. Irpan, A. Khazatsky, A. Rai, A. Gupta, A. Wang, A. Kolobov, A. Singh, A. Garg, A. Kembhavi, A. Xie, A. Brohan, A. Raffin, A. Sharma, A. Yavary, A. Jain, A. Balakrishna, A. Wahid, B. Burge...

    arXiv

  6. [6]

    BiSpace planning: Concurrent multi-space exploration,

    R. Diankov, N. Ratliff, D. Ferguson, S. Srinivasa, and J. Kuffner, “BiSpace planning: Concurrent multi-space exploration,” in Robotics: Science and Systems , vol. 4, 2009, pp. 159–166

    work page 2009

  7. [7]

    Bipedal Humanoid Hardware Design: a Technology Review,

    G. Ficht and S. Behnke, “Bipedal Humanoid Hardware Design: a Technology Review,”Current Robotics Reports, vol. 2, no. 2, pp. 201– 210, 2021

    work page 2021

  8. [8]

    Computation of kinetostatic performances of robot manipulators with polytopes,

    R. Finotello, T. Grasso, G. Rossi, and A. Tembile, “Computation of kinetostatic performances of robot manipulators with polytopes,” in IEEE International Conference on Robotics and Automation , 1998, pp. 3241–3246

    work page 1998

Show all 23 references
  1. [9]

    Wrist-partitioned, inverse kinematic accelerations and manipulator dynamics,

    J. M. Hollerbach and G. Sahar, “Wrist-partitioned, inverse kinematic accelerations and manipulator dynamics,” The International Journal of Robotics Research , vol. 2, pp. 61 – 76, 1983. [Online]. Available: https://api.semanticscholar.org/CorpusID:122964106

    1983

  2. [10]

    A review on genetic algorithm: past, present, and future,

    S. Katoch, S. S. Chauhan, and V . Kumar, “A review on genetic algorithm: past, present, and future,” Multimedia Tools and Applications , vol. 80, no. 5, pp. 8091–8126, 2021. [Online]. Available: https://doi.org/10.1007/s11042-020-10139-6

    2021 doi

  3. [11]

    Study on the manipulability measures for robot manipulators,

    J. Lee, “Study on the manipulability measures for robot manipulators,” in IEEE International Conference on Intelligent Robots and Systems , vol. 3, 1997, pp. 1458–1465

    1997

  4. [12]

    Octo: An open-source generalist robot policy,

    Octo Model Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, C. Xu, J. Luo, T. Kreiman, Y . Tan, P. Sanketi, Q. Vuong, T. Xiao, D. Sadigh, C. Finn, and S. Levine, “Octo: An open-source generalist robot policy,” in Proceedings of Robotics: Science an...

    2024

  5. [13]

    Task-oriented motion map- ping on robots of various configuration using body role division,

    K. Sasabuchi, N. Wake, and K. Ikeuchi, “Task-oriented motion map- ping on robots of various configuration using body role division,”IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 413–420, 2021

    2021

  6. [14]

    The Seednoid Robot Platform: Designing a Multipurpose Compact Robot from Continuous Evaluation and Lessons from Com- petitions,

    K. Sasabuchi, H. Yaguchi, K. Nagahama, S. Hori, H. Mizohana, and M. Inaba, “The Seednoid Robot Platform: Designing a Multipurpose Compact Robot from Continuous Evaluation and Lessons from Com- petitions,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 3983–3990, 2018

    2018

  7. [15]

    Analytical inverse kinematic computation for 7-dof redundant manip- ulators with joint limits and its application to redundancy resolution,

    M. Shimizu, H. Kakuya, W.-K. Yoon, K. Kitagaki, and K. Kosuge, “Analytical inverse kinematic computation for 7-dof redundant manip- ulators with joint limits and its application to redundancy resolution,” IEEE Transactions on Robotics , vol. 24, no. 5, pp. 1131–1142, 2008

    2008

  8. [16]

    Memetic evolution for generic full-body inverse kinematics in robotics and animation,

    S. Starke, N. Hendrich, and J. Zhang, “Memetic evolution for generic full-body inverse kinematics in robotics and animation,” IEEE Trans- actions on Evolutionary Computation , vol. 23, no. 3, pp. 406–420, 2019

    2019

  9. [17]

    Solvability-unconcerned inverse kinematics by the lev- enberg–marquardt method,

    T. Sugihara, “Solvability-unconcerned inverse kinematics by the lev- enberg–marquardt method,” IEEE Transactions on Robotics , vol. 27, no. 5, pp. 984–991, 2011

    2011

  10. [18]

    Analytical inverse kinematic solution using the d-h method for a 6-dof robot,

    J.-D. Sun, G.-Z. Cao, W.-B. Li, Y .-X. Liang, and S.-D. Huang, “Analytical inverse kinematic solution using the d-h method for a 6-dof robot,” in 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) , 2017, pp. 714–716

    2017

  11. [19]

    Manipulability analysis,

    N. Vahrenkamp, T. Asfour, G. Metta, G. Sandini, and R. Dillmann, “Manipulability analysis,” in 2012 12th IEEE-RAS International Con- ference on Humanoid Robots (Humanoids 2012) , 2012, pp. 568–573

    2012

  12. [20]

    Humanoid motion planning for dual-arm manipulation and re-grasping tasks,

    N. Vahrenkamp, D. Berenson, T. Asfour, J. Kuffner, and R. Dill- mann, “Humanoid motion planning for dual-arm manipulation and re-grasping tasks,” in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009 , 2009, pp. 2464–2470

    2009

  13. [21]

    Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods,

    C. W. Wampler, “Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods,” IEEE Trans- actions on Systems, Man, and Cybernetics , vol. 16, no. 1, pp. 93–101, 1986

    1986

  14. [22]

    Translational and rotational manipulability of robotic manipulators,

    T. Yoshikawa, “Translational and rotational manipulability of robotic manipulators,” in Proceedings IECON ’91: 1991 International Con- ference on Industrial Electronics, Control and Instrumentation , 1991, pp. 1170–1175 vol.2

    1991

  15. [23]

    Manipulability of robotic mechanisms,

    ——, “Manipulability of robotic mechanisms,” The International Journal of Robotics Research , vol. 4, pp. 3 – 9, 1985. [Online]. Available: https://api.semanticscholar.org/CorpusID:121737271

    1985

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Reviewed August 16, 2026 · model on record in the stance chip above.