Pith. sign in

REVIEW 5 major objections 4 minor 55 references

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning

T0 review · 5 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read The paper claims that making a latent world model's hidden state explicitly carry energy and momentum—through a port-Hamiltonian transition with dissipation and control ports—produces physically reusable predictions, and that conditioning…

desk verdict Plausible integration of a port-Hamiltonian world model with neural time fields, but the abstract overstates the Eikonal mechanism and the numbers have inconsistencies. read the letter →

arxiv 2608.09876 v1 pith:ESDVWJU6 submitted 2026-08-10 cs.RO cs.AI

Yapeng Liu , Yuanzhao Zhai , Bo Ding , Huaimin Wang , Lin Wang This is my paper · ORCID
classification cs.ROcs.AI
keywords latentworldmodelsport-HamiltoniandynamicsneuraltimefieldsEikonalequationmotionplanningopen-worldnavigationphysics-informedpredictionenergy-structuredstate
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 tries to establish that a latent world model whose hidden dynamics are explicitly structured around energy, momentum, dissipation, and control can turn a robot's interaction history into reusable physical knowledge, and that this knowledge can improve open-world motion planning when it is fed into a neural arrival-time field. The proposed system, ELWM+PC-NTF, uses a port-Hamiltonian latent transition so that energy change equals injected work minus non-negative dissipation, and a Physics-Conditioned Neural Time Field that adds the world model's predicted traversal delay to the geometric time-to-go of each candidate action. On held-out scenes, the system reduces 0.8-second motion-prediction NRMSE from 0.36 to 0.29 relative to a capacity-matched generic world model, raises navigation success from 81.3% to 89.7% and success weighted by path length (SPL) from 0.64 to 0.73 relative to geometry-only Active Neural Time Fields, and cuts the physical collision rate from 12.1% to 5.8%. The point of caring is that physically consistent structure, not extra model capacity, is what makes the learned predictions reusable enough to change the planner's decisions.

What carries the argument

The load-bearing mechanism is the port-Hamiltonian latent transition: a controlled dynamical system in which the latent state carries configuration and momentum, and energy changes only through an antisymmetric interconnection, a dissipative port, and a control port. ELWM parameterizes the stored energy $E_\theta$ and the dissipation and control operators so that the identity $\frac{dE_\theta}{dt} = -\nabla_Z E_\theta^\top R_\theta \nabla_Z E_\theta + u_t^\top y_t$ holds as a property of the vector field, which guarantees causal, dissipative latent rollouts. On the planning side, the Physics-Conditioned Neural Time Field (PC-NTF) is the mechanism that transfers this structure to navigation: it leaves the geometric neural time field intact but re-weights each candidate action by the world model's predicted terminal position and traversal delay, so the Eikonal constraint effectively slows arrival time through physically difficult regions without an explicit penalty or retraining the field.

What would settle it

Measure the robot's maximum stably tracked speed at one fixed location on a slope or a directionally rough surface for at least eight headings with the same payload and command window. If the scalar speeds differ materially with heading, the isotropic traversal-speed hypothesis is violated; conversely, if a heading-conditioned variant of the same planner gives no collision-rate or SPL improvement, the scalar assumption is not the mechanism behind the reported gains.

Watch

Extended reading notes

Core claim

The central claim is that explicit physical structure in the latent space—not additional capacity or scene-specific tuning—is what makes a world model's predictions trustworthy enough to condition motion planning. ELWM organizes the latent state as a phase pair $(Q_t, P_t)$ with a learned stored energy $E_\theta(Q_t,P_t;M_t,\mu_r)$, and its transition is a port-Hamiltonian system with skew-symmetric interconnection $J$, dissipation $R_\theta \succeq 0$, and a control port $B_\theta$, so the energy-balance identity $\dot E_\theta = -\nabla_Z E_\theta^\top R_\theta \nabla_Z E_\theta + u_t^\top y_t$ holds by construction. PC-NTF then conditions the Eikonal time field $s_{\mathrm{eff},t}(x;\zeta_t,\mu_r)\|\nabla_x T\|_2=1$ on the model's readout and scores each candidate by $T^{(i)} = T(\hat x^{(i)}_{t+H},x_g)+\Delta T^{(i)}_{\mathrm{phys}}$, treating the world model's traversal delay as an action-conditioned stage cost and the neural time field as a geometric terminal value. The reported reductions in NRMSE (0.36 to 0.29), improvements in success (81.3% to 89.7%) and SPL (0.64 to 0.73), and drops in physical collision rate (12.1% to 5.8%) and Eikonal residual (0.083 to 0.031) are the paper's evidence that this structure, rather than the underlying generic predictor, is doing the work.

Load-bearing premise

The load-bearing premise is that, within one replanning window, the fastest speed the robot can stably sustain at a given location is a single direction-independent number, so that orientation-dependent friction, turning-radius limits, and acceleration state can all be squeezed into one scalar and otherwise handled by an execution-time constraint layer.

Editorial extensions

If this is right

  • A generic latent world model can be upgraded to an energy-structured one without changing the planner or retraining the neural time field, and the resulting system predicts future motion more accurately at fixed horizon (NRMSE 0.29 vs 0.36).
  • A geometry-only arrival-time planner gains physical sensitivity: the same Eikonal field, once conditioned on ELWM readouts, raises navigation success from 81.3% to 89.7% and SPL from 0.64 to 0.73.
  • Physical collision rate drops from 12.1% to 5.8% under identical maps, candidate sets, and planning budgets, which is consistent with the claim that physically slow or hard-to-execute candidates are being deprioritized.
  • The energy-balance residual of the learned transition remains at the $10^{-7}$ scale and predicted dissipated power is nonnegative on evaluated rollouts, so the structure-preserving rollout does not drift into unphysical regimes.
  • The same gains appear on held-out scenes and held-out physical regimes, indicating the structured transition generalizes to unseen geometry and interaction conditions rather than memorizing training configurations.

Reading between the lines

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

  • A consequence the authors leave implicit is that the same port-Hamiltonian latent could serve as a differentiable physics prior for other downstream tasks, such as manipulation or legged locomotion, since the exposed dissipation and control ports are not navigation-specific; a test would be fine-tuning on a different embodiment and measuring whether the energy-balance residual and NRMSE transfer.
  • A practical diagnostic follows from the energy-balance identity: if the learned dissipation term $R_\theta$ ever becomes negative during deployment, the model is being asked to extrapolate outside its training distribution, so PC-NTF could use that as an online reliability flag and fall back to the geometric field.
  • The scalar isotropic speed hypothesis is the most likely first point of failure in uneven terrain; an anisotropic, state-space Hamilton-Jacobi version of PC-NTF is the natural next experiment, and the paper's own analysis points to that formulation.
  • Because the same geometric time field changes its effective preference when conditioned on different payload or dissipation readouts, the framework could also detect and react to physical changes online—such as picking up payload—simply by updating the latent physical regularities $M_t$.
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

5 major / 4 minor

Summary. This paper proposes ELWM, a latent world model whose transition is structured as a port-Hamiltonian system with explicit latent stored energy, dissipation, and control ports, and PC-NTF, a navigation module that combines ELWM predictions with neural time fields. The paper reports reduced 0.8-s motion-prediction NRMSE relative to a generic world model and improved navigation success, SPL, physical collision rate, and Eikonal residual against Active Neural Time Fields, evaluated in iGibson, HM3D, and terrain-variation environments.

Significance. If substantiated, the ELWM latent structure would be a useful step toward reusable physical priors in latent world models, and the idea of making candidate evaluation depend on learned interaction consequences is well motivated. The paper has several strengths: the energy-balance identity in Eq. (6) is derived cleanly, the implicit-midpoint integration is appropriate for the non-separable energy, the ablation study in Table 4 attempts to separate the contributions of pH structure, physical conditioning, and candidate correction, and the held-out regime/scene splits are a reasonable evaluation design. However, the central PC-NTF mechanism as implemented does not match the Eikonal construction claimed in Eq. (8) and the abstract, the Eikonal residual metric is never defined, and several key quantitative results are inconsistent across the abstract, figures, and tables. These issues are load-bearing and must be resolved before the reported gains can be interpreted.

major comments (5)
  1. [Method: PC-NTF System Construction, Eqs. (8)-(12)] The central claim that PC-NTF integrates ELWM into an arrival-time field via the Eikonal equation is inconsistent with the described implementation. Equation (8) defines a physics-conditioned Eikonal problem with spatially varying s_eff,t(x; zeta_t, mu_r), but Eq. (11) computes T^(i) = T(x_hat^(i)_{t+H}, x_g) + DeltaT^(i)_phys using the unchanged ANTF geometric field T; no re-solve of Eq. (8) is described anywhere. As written, the method is a candidate-wise value augmentation over a fixed geometric field, and the reported 'Eikonal residual' improvement (0.083 to 0.031) lacks a definition of the residual and of the field to which it is applied. Please either implement and evaluate the Eikonal solve with s_eff,t, or rewrite the method and abstract to state that PC-NTF performs terminal-value correction on a frozen ANTF field, and define the residual metric explicitly.
  2. [Method: Eq. (8) and Eq. (10)] The Eikonal equations in Eq. (8) and Eq. (10) are written as s(x)||grad T||^2 = 1. If s is a propagation speed, the standard Eikonal relation is s(x)||grad T|| = 1; the squared form changes units and propagates into Eq. (12), where the implied speed is reported as 1/(||grad T||^2 + epsilon). Please correct these equations and the implied-speed diagnostic, or explicitly define s as a squared-speed quantity if that is the intended convention.
  3. [Abstract / Experiments: Table 3 vs. Figure 4; Introduction vs. Experimental Results] Key quantitative results are internally inconsistent. The abstract and Figure 4 report an ANTF physical collision rate of 12.1%, while Table 3 reports 14.4% and Table 4 reports 14.35% for the same baseline. The Introduction states an energy-balance residual of 7.12e-7, while the Experiments section reports 1.88e-7 for the trained ELWM checkpoint. Please reconcile these numbers so that every headline quantity has a unique value in the tables and figures.
  4. [Method: ELWM Training, Eq. (6)] The physical-consistency evidence is weaker than claimed. Equation (6) is an algebraic identity of the port-Hamiltonian vector field in Eq. (5), so a near-zero energy-balance residual primarily checks that the implicit-midpoint integrator respects the model's internal structure, not that the learned latent dynamics match the robot's physical behavior. The manuscript should state this limitation and provide additional validation, such as comparisons against measured energy or dissipation quantities, before claiming that the model 'guarantees physically consistent predictions.'
  5. [Experiments: Table 2] Table 2's random-initialization comparison is difficult to interpret: the motion NRMSE for a randomly initialized ELWM is reported as 7.753 and 7.699, which is orders of magnitude larger than the 0.29 NRMSE reported in Table 1, and no explanation of the different normalization or units is given. As reported, the 90.9% relative reduction is an artifact of comparing incomparable metrics. Please clarify the metric definition and report the random-init baseline under the same normalization used in Table 1.
minor comments (4)
  1. [Title] The title contains a typo: 'Constistent' should be 'Consistent.'
  2. [Abstract] The abstract states that 'PC-NTF reduces 0.8-s motion-prediction NRMSE from 0.36 to 0.29,' but this reduction is attributable to ELWM at the world-model level, not to PC-NTF; please attribute the numbers to the correct component.
  3. [Method: ELWM Training, Eq. (7)] In Eq. (7), the target notation 'Z+_{t+k} = E_theta(H_phy_{t+k})Z' contains a stray trailing 'Z' after the encoder call; this should be removed.
  4. [Experiments: Reproducibility] The experiments omit several details needed for reproduction, including the number of candidate trajectories generated at each replanning step, the rollout horizon H, the network architecture sizes, training dataset sizes, and the exact definition of the physical collision rate.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's predictions are evaluated against independent targets, and reported residuals are self-consistency diagnostics rather than fitted outputs.

full rationale

The derivation chain is not circular. ELWM is trained to predict future latent encodings (Eq. 7), and its motion-prediction NRMSE is evaluated on observable pose/twist errors against a capacity-matched generic world model; the energy-balance residual (Eq. 6) is an identity of the port-Hamiltonian vector field and is reported as a numerical self-consistency check, not as an independently fitted prediction. PC-NTF's planning cost (Eq. 11) adds a learned traversal delay to the geometric ANTF arrival-time field, and the reported Eikonal residual is a diagnostic of the geometric field rather than a quantity forced by the learned delay. The self-citations to Active Neural Time Fields (Liu, Ni, and Qureshi 2025) and port-Hamiltonian works are external published baselines or standard formalisms; they are not invoked as uniqueness theorems and do not by themselves determine the reported success-rate or SPL gains. The internal tension between the claimed physics-conditioned Eikonal mechanism (Eq. 8) and the implemented candidate-wise value augmentation (Eq. 11) is a correctness or mechanism concern, not a circular reduction of any result to its own input.

Assumptions & free parameters 4 free parameters · 5 assumptions · 1 invented entities

The central claim rests on the port-Hamiltonian latent structure, the isotropic scalar-speed hypothesis, and the reliability of future latent encodings as training targets. These are domain assumptions rather than standard math. The learned networks and the SIGReg coefficient are free parameters, and the latent energy is an invented entity without independent evidence.

free parameters (4)
  • Learned network weights of ELWM (encoder Eθ, inertia map Gθ, potential Uθ, dissipation Rθ, control port Bθ, action… = not reported
    All are fitted to interaction-history prediction targets; no numbers or architecture details are given.
  • SIGReg coefficient λ_sig = not reported
    Regularization weight chosen by hand; the scalability study varies it but no final value is stated.
  • Implicit midpoint integration step Δt = not reported
    Discretization step for the port-Hamiltonian rollout, chosen by hand and not specified.
  • Physics-speed envelope targets s*_t for the decoder = derived from data
    The Huber-trained decoder for traversal delay ΔT_WM depends on a labeled speed envelope that is itself a modeling choice.
assumptions (5)
  • domain assumption Port-Hamiltonian form with skew-symmetric J, dissipative R⪰0, and control port B is a valid description of robot-environment interaction in latent space.
    Invoked in Eq. (5); the latent state is assumed to behave like a physical Hamiltonian system.
  • domain assumption The latent stored energy Eθ(Q,P; M, μr) with positive-definite inertia Gθ correctly represents the robot's physical energy landscape.
    Eqs. (3)-(4); no external validation that latent energy corresponds to measurable physical energy.
  • domain assumption Within a replanning window, robot traversal can be represented by an isotropic scalar speed field per location; orientation-dependent effects are absorbed and handled by a separate constraint layer.
    Method, Hypothesis paragraph; this is the stated simplification and the load-bearing premise for the Eikonal speed model.
  • standard math The Eikonal equation with the physics-conditioned speed has a viscosity solution that yields a valid arrival-time field for planning.
    Standard theory (Bardi and Dolcetta 1997); the PINN approximates the solution in Eq. (10).
  • domain assumption Future latent encodings from the same encoder are reliable training targets for physically meaningful prediction.
    Training objective Eq. (7); if the encoder is not consistent, the targets inherit its errors.
invented entities (1)
  • Latent stored-energy function Eθ and latent phase state (Q, P)
    purpose: Provides the Hamiltonian structure whose energy-balance identity is claimed as physical consistency.
    The energy and momentum are internal latent quantities with no direct measurement; the paper does not tie them to externally measurable quantities.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning." pith.science (2026). https://pith.science/paper/ESDVWJU6

@misc{pith2026260809876,
  author       = {Pith},
  title        = {Pith review of: Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ESDVWJU6}},
  note         = {Machine review of arXiv:2608.09876}
}
read the original abstract

Physically consistent motion planning remains a fundamental challenge in embodied AI, as generated trajectories must strictly conform to real-world execution dynamics. While latent world models offer a promising approach by predicting these dynamics, existing methods learn unconstrained future representations where absorbed physics remains implicit. Therefore, they fail to form reusable physical knowledge, which compromises reliability in unpredictable open-world navigation. To address this, we propose a novel Energy-Structured Latent World Model (ELWM). Our key idea is to structure the ELWM latent state to explicitly carry energy and momentum, ensuring strictly causal transitions via dissipation and control ports. Trained on multimodal RGB-D and inertial interaction histories, our model guarantees physically consistent predictions. We further implement this for motion planning by constructing Physics-Conditioned Neural Time Fields (PC-NTF), a key technical cornerstone that integrates ELWM into an arrival time field via the Eikonal equation to yield a physically-informed navigation policy. Across held-out scenes, our evaluation reveals significant improvements. Compared to generic latent models, PC-NTF reduces 0.8-s motion-prediction NRMSE from 0.36 to 0.29. Against Active Neural Time Fields, it improves navigation success from 81.3% to 89.7% and SPL from 0.64 to 0.73, while cutting the physical collision rate from 12.1% to 5.8% and the Eikonal residual from 0.083 to 0.031. Beyond these targeted gains, our results demonstrate that embedding explicit physical structures into latent spaces intrinsically bridges the gap between predictive world models and safe, dynamically feasible motion planning.

Figures

Figures reproduced from arXiv: 2608.09876 by the authors.

Figure 1
Figure 1. ELWM–PC-NTF bridges action-conditioned physical prediction and motion planning. (A) Robot interac￾tions need action–response capability, (B) ELWM predicts future motion and latent physical dynamics, and PC-NTF combines them with neural time fields. (C) System closed￾loop execution produces obstacle-aware, physics-informed navigation towards the goal. system responds to execution (Ha and Schmidhuber 2018; Kumar et al… view at source ↗
Figure 2
Figure 2. Overview of ELWM. Physical history and interaction dynamics are encoded into physical and latent state; a controlled, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. PC-NTF motion planning pipeline. (a) NTF proposes shared motion candidates -> (b) ELWM predicts their action [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Motion-planning and navigation results com￾parison, PC-NTF indicates ELWM+PC-NTF system. Method SR (%)↑ SPL↑ Phys. Coll. (%)↓ Eik.↓ Latency (ms)↓ ANTF 81.3 0.64 14.4 0.083 35.3 Generic WM + MPC 87.1 0.74 6.5 0.143 371.7 ELWM + PC-NTF 89.7 0.73 5.8 0.031 193.2 [PITH_FU…

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

55 extracted references · 26 canonical work pages

  1. [1]

    Advances in neural information processing systems , volume=

    Recurrent world models facilitate policy evolution , author=. Advances in neural information processing systems , volume=

  2. [2]

    International conference on machine learning , pages=

    Learning latent dynamics for planning from pixels , author=. International conference on machine learning , pages=. 2019 , organization=

    2019

  3. [3]

    arXiv preprint arXiv:1912.01603 , year=

    Dream to control: Learning behaviors by latent imagination , author=. arXiv preprint arXiv:1912.01603 , year=

    arXiv 1912

  4. [4]

    arXiv preprint arXiv:2301.04104 , year=

    Mastering diverse domains through world models , author=. arXiv preprint arXiv:2301.04104 , year=

    arXiv

  5. [5]

    arXiv preprint arXiv:2511.08544 , year=

    Lejepa: Provable and scalable self-supervised learning without the heuristics , author=. arXiv preprint arXiv:2511.08544 , year=

    arXiv

  6. [6]

    International Conference on Machine Learning , pages=

    DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning , author=. International Conference on Machine Learning , pages=. 2025 , organization=

    work page 2025

  7. [7]

    2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    Navigation World Models , author=. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=. 2025 , organization=

    work page 2025

  8. [8]

    arXiv preprint arXiv:2606.15768 , year=

    Lawam: Latent world action models for efficient dynamics-aware robot policies , author=. arXiv preprint arXiv:2606.15768 , year=

    arXiv

Show all 55 references
  1. [9]

    Advances in Neural Information Processing Systems , volume=

    Roboscape: Physics-informed embodied world model , author=. Advances in Neural Information Processing Systems , volume=

  2. [10]

    Advances in neural information processing systems , volume=

    Hamiltonian neural networks , author=. Advances in neural information processing systems , volume=

  3. [11]

    arXiv preprint arXiv:2003.04630 , year =

    Lagrangian Neural Networks , author =. arXiv preprint arXiv:2003.04630 , year =

    2003 arXiv

  4. [12]

    arXiv preprint arXiv:2002.08860 , year=

    Dissipative SymODEN: Encoding Hamiltonian dynamics with dissipation and control into deep learning , author=. arXiv preprint arXiv:2002.08860 , year=

    2002 arXiv

  5. [13]

    arXiv preprint arXiv:2107.08024 , year=

    Port-Hamiltonian neural networks for learning explicit time-dependent dynamical systems , author=. arXiv preprint arXiv:2107.08024 , year=

    arXiv

  6. [14]

    arXiv preprint arXiv:2606.23444 , year=

    Skyjepa: Learning long-horizon world models for zero-shot sim-to-real control of quadrotors , author=. arXiv preprint arXiv:2606.23444 , year=

    arXiv

  7. [15]

    Journal of Computational physics , volume=

    Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , author=. Journal of Computational physics , volume=. 2018 , publisher=

    2018

  8. [16]

    , author=

    A fast marching level set method for monotonically advancing fronts. , author=. proceedings of the National Academy of Sciences , volume=

  9. [17]

    The international journal of robotics research , volume=

    Sampling-based algorithms for optimal motion planning , author=. The international journal of robotics research , volume=. 2011 , publisher=

    2011

  10. [18]

    IEEE Transactions on Robotics , volume=

    Motion planning networks: Bridging the gap between learning-based and classical motion planners , author=. IEEE Transactions on Robotics , volume=. 2020 , publisher=

    2020

  11. [19]

    IEEE Transactions on Geoscience and Remote Sensing , volume=

    Eikonet: Solving the eikonal equation with deep neural networks , author=. IEEE Transactions on Geoscience and Remote Sensing , volume=. 2020 , publisher=

    2020

  12. [20]

    Computers & Geosciences , volume=

    PINNeik: Eikonal solution using physics-informed neural networks , author=. Computers & Geosciences , volume=. 2021 , publisher=

    2021

  13. [21]

    arXiv preprint arXiv:2210.00120 , year=

    Ntfields: Neural time fields for physics-informed robot motion planning , author=. arXiv preprint arXiv:2210.00120 , year=

    arXiv

  14. [22]

    Robotics: Science and Systems , year =

    Progressive Learning for Physics-informed Neural Motion Planning , author =. Robotics: Science and Systems , year =

  15. [23]

    IEEE Transactions on Robotics , year=

    Physics-informed neural mapping and motion planning in unknown environments , author=. IEEE Transactions on Robotics , year=

  16. [24]

    arXiv preprint arXiv:2606.22143 , year=

    Physics-Informed Eikonal Caging for Whole-Arm Manipulation Planning , author=. arXiv preprint arXiv:2606.22143 , year=

    arXiv

  17. [25]

    IEEE Robotics and Automation Letters , volume=

    Badgr: An autonomous self-supervised learning-based navigation system , author=. IEEE Robotics and Automation Letters , volume=. 2021 , publisher=

    2021

  18. [26]

    IEEE Robotics and Automation Letters , volume=

    Wayfast: Navigation with predictive traversability in the field , author=. IEEE Robotics and Automation Letters , volume=. 2022 , publisher=

    2022

  19. [27]

    2022 , doi =

    Shah, Dhruv and Levine, Sergey , booktitle =. 2022 , doi =

    2022

  20. [28]

    arXiv preprint arXiv:2012.02924 , year=

    igibson 1.0: A simulation environment for interactive tasks in large realistic scenes , author=. arXiv preprint arXiv:2012.02924 , year=

    2012 arXiv

  21. [29]

    arXiv preprint arXiv:2109.08238 , year=

    Habitat-matterport 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai , author=. arXiv preprint arXiv:2109.08238 , year=

    arXiv

  22. [30]

    Recent Mathematical Methods in Dynamic Programming: Proceedings of the Conference held in Rome, Italy, March 26--28, 1984 , pages=

    Optimal control and viscosity solutions , author=. Recent Mathematical Methods in Dynamic Programming: Proceedings of the Conference held in Rome, Italy, March 26--28, 1984 , pages=. 2006 , organization=

    1984

  23. [31]

    arXiv preprint arXiv:2512.10942 , year=

    Vl-jepa: Joint embedding predictive architecture for vision-language , author=. arXiv preprint arXiv:2512.10942 , year=

  24. [32]

    arXiv preprint arXiv:2606.16076 , year=

    Phys-JEPA: Physics-Informed Latent World Models for Multivariate Time-Series Forecasting , author=. arXiv preprint arXiv:2606.16076 , year=

  25. [33]

    Proceedings of the IEEE , volume=

    Toward causal representation learning , author=. Proceedings of the IEEE , volume=. 2021 , publisher=

    2021

  26. [34]

    arXiv preprint arXiv:2504.16693 , year=

    Pin-wm: Learning physics-informed world models for non-prehensile manipulation , author=. arXiv preprint arXiv:2504.16693 , year=

    arXiv

  27. [35]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Deepsdf: Learning continuous signed distance functions for shape representation , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  28. [36]

    Advances in Neural Information Processing Systems , volume=

    Generative neural fields by mixtures of neural implicit functions , author=. Advances in Neural Information Processing Systems , volume=

  29. [37]

    IEEE Transactions on Robotics , volume=

    Robot motion planning in dynamic, uncertain environments , author=. IEEE Transactions on Robotics , volume=. 2011 , publisher=

    2011

  30. [38]

    2, 2022-06-27 , author=

    A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27 , author=. Open Review , volume=

    2022

  31. [39]

    Aerospace Science and Technology , pages=

    Multi-objective trajectory planning for flexible spacecraft via physics-informed neural network , author=. Aerospace Science and Technology , pages=. 2025 , publisher=

    2025

  32. [40]

    Advances in Neural Information Processing Systems , volume=

    Stable port-hamiltonian neural networks , author=. Advances in Neural Information Processing Systems , volume=

  33. [41]

    arXiv preprint arXiv:2605.18303 , year=

    PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics , author=. arXiv preprint arXiv:2605.18303 , year=

    arXiv

  34. [42]

    arXiv preprint arXiv:1909.12077 , year=

    Symplectic ode-net: Learning hamiltonian dynamics with control , author=. arXiv preprint arXiv:1909.12077 , year=

    1909 arXiv

  35. [43]

    7th Annual Learning for Dynamics & Control Conference, 04-06 June, 2025, Ann Arbor, Michigan, USA , pages=

    Action-Conditioned Hamiltonian Generative Networks (AC-HGN) for Supervised and Reinforcement Learning , author=. 7th Annual Learning for Dynamics & Control Conference, 04-06 June, 2025, Ann Arbor, Michigan, USA , pages=

    2025

  36. [44]

    The International Journal of Robotics Research , volume=

    Randomized kinodynamic motion planning with moving obstacles , author=. The International Journal of Robotics Research , volume=. 2002 , publisher=

    2002

  37. [45]

    IEEE/ASME Transactions on Mechatronics , year=

    Aligning cyber space with physical world: A comprehensive survey on embodied ai , author=. IEEE/ASME Transactions on Mechatronics , year=

  38. [46]

    , author=

    Kinodynamic Planning in the Configuration Space via Admissible Velocity Propagation. , author=. Robotics: Science and Systems , volume=

  39. [47]

    IEEE Transactions on Robotics , volume=

    Interactive perception: Leveraging action in perception and perception in action , author=. IEEE Transactions on Robotics , volume=. 2017 , publisher=

    2017

  40. [48]

    arXiv preprint arXiv:2602.11291 , year=

    H-wm: Robotic task and motion planning guided by hierarchical world model , author=. arXiv preprint arXiv:2602.11291 , year=

  41. [49]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    SIAM: Towards Generalizable Articulated Object Modeling via Single Robot-Object Interaction , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  42. [50]

    arXiv preprint arXiv:2603.19312 , year=

    Leworldmodel: Stable end-to-end joint-embedding predictive architecture from pixels , author=. arXiv preprint arXiv:2603.19312 , year=

    arXiv

  43. [51]

    arXiv preprint arXiv:1907.04490 , year=

    Deep lagrangian networks: Using physics as model prior for deep learning , author=. arXiv preprint arXiv:1907.04490 , year=

    1907 arXiv

  44. [52]

    arXiv preprint arXiv:2607.03339 , year=

    CSympNet-ID: conformal-symplectic map learning for linearly damped Hamiltonian systems , author=. arXiv preprint arXiv:2607.03339 , year=

  45. [53]

    arXiv preprint arXiv:2107.04034 , year=

    Rma: Rapid motor adaptation for legged robots , author=. arXiv preprint arXiv:2107.04034 , year=

    arXiv

  46. [54]

    arXiv preprint arXiv:2602.15922 , year=

    World action models are zero-shot policies , author=. arXiv preprint arXiv:2602.15922 , year=

    arXiv

  47. [55]

    1997 , publisher=

    Optimal control and viscosity solutions of Hamilton-Jacobi-Bellman equations , author=. 1997 , publisher=

    1997

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.