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REVIEW 3 major objections 4 minor 34 references

Uncertainty-Resilient Active Intention Recognition for Robotic Assistants

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read The paper demonstrates an integrated framework in which a mobile robot infers a worker's assembly goal from noisy color-part observations and proactively delivers missing parts, without explicit commands, using online POMDP planning.

desk verdict A credible systems-integration paper with a strong POMDP noise-resilience experiment, but the physical-robot claim outruns the quantified evidence and the evaluation is partly self-referential. read the letter →

arxiv 2508.19150 v1 pith:NXK5W3PE submitted 2025-08-26 cs.RO cs.AI

Juan Carlos Saborío , Marc Vinci , Oscar Lima , Sebastian Stock , Lennart Niecksch , Martin Günther , Alexander Sung , Joachim Hertzberg
show 1 more author
Martin Atzmüller
This is my paper · ORCID
classification cs.ROcs.AI
keywords intentionrecognitionactivegoalPOMDPhuman-robotcollaborationplanningunderuncertaintyassemblyassistancemobilemanipulationsensornoise
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

Proactive robotic assistance usually stops at recognizing explicit prompts or assumes the robot sees the world clearly. This paper argues the missing piece is a single framework that keeps planning and acting while uncertain about a person's goal, and it builds one around an active goal recognition POMDP. In the test scenario, a human assembles one of two color-coded insect hotels; a mobile robot watches parts appear, disappear, and run low, then decides when to look again and which missing parts to bring. The framework was evaluated with simulated sensor noise, with accuracy down to 0.5, and on a physical robot; in the paper's archetypal simulation, all 20 runs completed with the robot bringing common parts first and type-specific parts only after gaining confidence. If the claim holds, it shows that assistance can be based on predicting a person's upcoming needs rather than reacting to commands or ongoing motions.

What carries the argument

The load-bearing object is the active goal recognition POMDP (AGR-POMDP), a partially observable Markov decision process whose hidden state contains the human's goal, assembly progress, and part availability, and whose observations are noisy labels produced by color-based part detection. The POMDP is solved online by the RAGE planner, which extends Monte-Carlo tree search with relevance estimation and subgoal generation; POMCP serves as the comparison baseline. The same object carries both sides of the argument: it is how the robot absorbs sensor data into a belief, reasons about delayed rewards, chooses information-gathering actions, and decides when a manipulation action is worth its cost.

What would settle it

Run the same physical or simulated scenario with human workers who are not following the model's policy—for example, arbitrary part orders, mid-task goal switches, or long pauses—and record whether the robot's delivered parts still match the parts actually missing. If the robot's deliveries match the true missing parts in fewer than 7 of 10 such deviation trials, the central claim would be falsified.

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Extended reading notes

Core claim

The central claim is that online POMDP planning can drive a physical robot's proactive assistance in a shared assembly task despite perception noise and delayed outcomes. The paper's architecture connects real-time cameras to an object detector that supplies symbolic observations, an active goal recognition POMDP (AGR-POMDP) that estimates part availability, assembly status, and the intended hotel type, and a hierarchy of planners that executes the selected high-level task on the robot. In the evaluative scenario, the robot had no initial knowledge of the inventory, assembly status, or goal; from color-coded part detections it learned to bring common missing parts first and type-specific par

Load-bearing premise

The robot assumes the human worker follows a known random policy for choosing assembly steps; the paper's tests evaluate success against that same model, so real human deviation remains untested.

Editorial extensions

If this is right

  • Robotic assistants can operate in semi-structured assembly without explicit commands, inferring needs from part usage.
  • Online planning, not offline precomputation, is sufficient for a physical robot to interleave perception, reasoning, and execution.
  • The approach tolerates high perception noise; in simulation, both planners still complete assemblies at the lowest tested sensor accuracy (0.5).
  • The reward structure leads to an emergent risk-averse strategy: fetch common parts early, delay type-specific parts until the intended hotel is confident.
  • Perception and grasping failures are handled by the same POMDP mechanism rather than hard-coded fallbacks.

Reading between the lines

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

  • Because the paper's own tests use the same MDP worker model both as the planner's assumption and as the simulated ground truth, the strongest extension would be an experiment with human participants who are free to deviate; the framework's success on real people is not yet demonstrated.
  • The robot only observes worker actions after they happen (a part appears or disappears), so a richer perception layer—hand position, body pose, gaze—could let the same POMDP predict needs earlier and cut the long waiting times reported.
  • The same core should transfer to any cooperative task that can be abstracted into part states and goal types, such as restocking or sequential manual assembly, since the POMDP consumes symbolic labels rather than raw images.
  • The consistent advantage of the relevance-based planner suggests that the scaling bottleneck for active intention recognition is algorithmic—sampling relevance and subgoal generation—rather than raw simulation budget.
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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

3 major / 4 minor

Summary. The paper proposes an integrated architecture for proactive robot assistance in a collaborative assembly task. The system combines a perception pipeline (color-based part detection, 6DoF box pose estimation) with an active goal recognition POMDP (AGR-POMDP) solved online using the RAGE planner, and lower-level task/motion planning and execution on a physical Mobipick robot. The robot infers the worker's intended hotel type and part status from noisy observations, and decides when to perceive, navigate, search, and deliver parts without explicit commands. Evaluation consists of (i) 100-run simulated POMDP experiments with varying sensor accuracy comparing RAGE and POMCP, (ii) a Gazebo assistance scenario replicated 20 times, and (iii) a qualitative physical robot demonstration. The central claim is that the framework is resilient to uncertainty and sensor noise and can assist a human worker effectively without explicit instructions.

Significance. If the claims hold, the paper contributes a useful integration of online POMDP planning with a real robot control stack for proactive intention recognition, going beyond reactive gesture/activity recognition. The Fig. 4 results provide a concrete, quantitative resilience check with standard errors and a POMCP baseline, and the release of the synthetic dataset and demo code is a reproducibility strength. However, the significance is tempered by the evaluation gaps described in the major comments: the simulated worker is also the assumed generative model, the Gazebo scenario uses ground-truth perception, and the physical-robot claim is not quantified.

major comments (3)
  1. [Sec. IV-C and V-A] The worker's task model is defined in Sec. IV-C as an MDP policy known to the robot, and the AGR-POMDP observations in Sec. V-A are generated from that same policy. The simulated experiments therefore evaluate the planner on the exact distribution it assumes; they cannot detect misspecification of the worker model. The Gazebo assistance scenario in Sec. V-B replicates the same domain, so it inherits the same limitation. This is load-bearing for the claim of uncertainty-resilient intention recognition for human workers. I recommend adding robustness experiments with perturbed worker policies (different part-order biases, unpredicted pauses, or mistakes not representable in the MDP) or, ideally, real human interaction data, to show that the belief update does not rely on a self-fulfilling model.
  2. [Sec. V-B and Abstract] The Gazebo assistance evaluation uses ground-truth perception with artificial sensor noise (Sec. IV-B and V), so the integrated pipeline from camera images through YOLOv8/DOPE to POMDP observations is not exercised end-to-end. The abstract's claim that the framework was 'successfully tested on a physical robot' is supported only by a qualitative narrative and a video link; no per-run metrics on inference accuracy, delivery correctness, success rate, or human variability are reported. The 20-run statistic in Sec. V-B appears to refer to Gazebo, not to the physical robot. Please either provide quantitative physical-robot results or temper the claim accordingly.
  3. [Sec. V-A] The reward structure in Sec. V-A is hand-authored and contains several free parameters (perception cost -0.5, restocking rewards -10/2/-2, etc.). The risk-averse behavior described in Sec. V-B—preferring common parts and waiting on type-specific parts—appears to be a direct consequence of these reward choices, yet no sensitivity analysis is reported. Since the central message is about the framework's resilience rather than a particular reward grid, the generality of the reported returns would be strengthened by a sensitivity study over the reward magnitudes and thresholds.
minor comments (4)
  1. [Sec. V-B] Please clarify explicitly whether the 20 successful runs of the Fig. 5 scenario were performed in Gazebo, on the physical robot, or both. The text moves from 'We replicated several exemplary scenarios in Gazebo' to 'The scenario in Figure 5 was executed 20 times' without a clear subject.
  2. [Fig. 4] The text states the curves show standard errors, but the figure appears to show only point estimates. Consider adding error bars or shaded confidence bands, and define the range of mean returns reported in the caption.
  3. [Introduction] Typo: 'transfering' should be 'transferring' in the first paragraph. Also, the phrase 'our projects' in Sec. IV-B ('out of the scope of our projects') is informal; clarify whether this is a limitation of the framework or only of the demonstration.
  4. [Fig. 5] The color-coded actions are described only in the caption. A legend or textual indication of which colors correspond to which parts would improve readability, especially in a printed black-and-white version.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the POMDP posterior is a Bayesian computation from an explicit generative model, not a fitted output; the main limitations are external validity, not circularity.

full rationale

The paper does not fit any parameter to data and then re-predict the same data as a 'result.' The AGR-POMDP belief over hotel type and part availability is computed from the explicitly stated generative worker model (Sec. IV-C) plus online observations; the worker model is an assumption, not a fitted constant, and the posterior is a function of observations, so the inference is not equivalent to its inputs by construction. The simulation experiments use the same worker MDP that the robot believes, which limits the external validity of the simulated returns if real human behavior deviates from the MDP, but that is a modeling/evaluation-scope concern rather than a circular derivation. The physical-robot demonstration, while qualitative, is independent evidence that the integrated system can function with a real human. The self-citations to the authors' earlier POMDP formulation [6] and RAGE planner [19,20] are load-bearing for the framework, but they are not invoked as uniqueness theorems, and the paper includes a POMCP baseline, so the planner comparison is independently checkable. No step in the paper's claimed derivation chain reduces to a fit or to a self-citation chain.

Assumptions & free parameters 7 free parameters · 7 assumptions · 0 invented entities

The central claim rests on hand-chosen rewards, an assumed worker MDP, and the authors' own AGR-POMDP/RAGE formulations. No new physical entities are introduced. The rewards are not fitted to external data but they are free parameters that determine the evaluated behavior.

free parameters (7)
  • Perception action reward = -0.5
    Hand-set in Sec V-A to balance information gathering cost.
  • Restocking rewards (lack of info / ideal / otherwise) = -10 / +2 / -2
    Hand-crafted reward schedule in Sec V-A; drives risk-averse part delivery.
  • Worker model rewards (missing part / assembly / completed) = -2 / +2 / +5
    Defines the simulated worker's MDP in Sec V-A; not derived from data.
  • Discount factor and horizon = gamma=0.99, max steps=100
    Experiment settings in Sec V-A; not justified.
  • Sensor accuracy levels = 0.5, 0.65, 0.75, 0.85
    Chosen grid in Sec V-A to vary observation noise; not tied to the real camera's accuracy.
  • Worker assembly pacing in Gazebo = 30-second intervals
    Sec V-B; selected to fit the demo timeline, not a measured worker behavior.
  • Worker MDP transition probabilities = not specified
    Sec IV-C says actions occur 'with probability' but values are not given; they parameterize the belief model.
assumptions (7)
  • domain assumption Human worker behavior can be modeled as an MDP policy with known probabilities and rewards
    Sec IV-C states the worker's task model is an MDP policy; the robot's belief model is this same MDP, making simulation self-consistent.
  • domain assumption The AGR-POMDP formulation in [6] correctly represents robot-human collaboration
    Sec IV-C cites [6] for the model; the paper does not restate the formulation.
  • domain assumption RAGE planner provides efficient online POMDP planning in this domain
    Sec IV-C/V rely on RAGE from [19,20]; performance is shown empirically but not proven for this domain.
  • domain assumption YOLOv8 trained on synthetic NVISII data transfers to real camera images
    Sec IV-B states the training setup; no quantitative detection accuracy on real images is reported.
  • domain assumption Color-coded part detection suffices to infer assembly progress
    Sec IV-B: 'For simplicity we use color-coded parts and rely on color recognition.'
  • standard math Discounted return with gamma=0.99 is an appropriate performance measure
    Sec V-A defines the evaluation metric.
  • standard math POMDP optimality and standard probability calculus
    Underlying formal framework, used throughout Sec III-IV.

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Cite this review

Pith. "Pith review of Uncertainty-Resilient Active Intention Recognition for Robotic Assistants." pith.science (2026). https://pith.science/paper/NXK5W3PE

@misc{pith2026250819150,
  author       = {Pith},
  title        = {Pith review of: Uncertainty-Resilient Active Intention Recognition for Robotic Assistants},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NXK5W3PE}},
  note         = {Machine review of arXiv:2508.19150}
}
read the original abstract

Purposeful behavior in robotic assistants requires the integration of multiple components and technological advances. Often, the problem is reduced to recognizing explicit prompts, which limits autonomy, or is oversimplified through assumptions such as near-perfect information. We argue that a critical gap remains unaddressed -- specifically, the challenge of reasoning about the uncertain outcomes and perception errors inherent to human intention recognition. In response, we present a framework designed to be resilient to uncertainty and sensor noise, integrating real-time sensor data with a combination of planners. Centered around an intention-recognition POMDP, our approach addresses cooperative planning and acting under uncertainty. Our integrated framework has been successfully tested on a physical robot with promising results.

Figures

Figures reproduced from arXiv: 2508.19150 by the authors.

Figure 1
Figure 1. A ten-part insect hotel: individual parts (left) and assembled (right). [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. AGR Architecture: Dashed lines group the different major modules and arrows indicate the necessary communication and representation steps [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Robot-assisted assembly environment, with the physical Mobipick [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Active Goal Recognition under different levels of sensor accuracy, [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Timeline of an exemplary scenario, where a human worker assembles a type-A insect hotel while the robot perceives the environment and brings [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

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Works this paper leans on

34 extracted references · 32 canonical work pages

  1. [1]

    Intention recognition with recurrent neural networks for dynamic human-robot collabora- tion,

    M. Mavsar, M. Deni ˇsa, B. Nemec, and A. Ude, “Intention recognition with recurrent neural networks for dynamic human-robot collabora- tion,” in 2021 20th Intl. Conf. on Advanced Robotics (ICAR) . IEEE, 2021, pp. 208–215. 0 50 100 150 200 250 308 Time (seconds) worker robot worker: assemble assemble assemble assemble assemble assemble wait for parts robot...

    work page 2021

  2. [2]

    Dynamic human-aware task planner for human-robot collaboration in industrial scenario,

    A. Gottardi, M. Terreran, C. Frommel, M. Schoenheits, N. Castaman, S. Ghidoni, and E. Menegatti, “Dynamic human-aware task planner for human-robot collaboration in industrial scenario,” in 2023 European Conference on Mobile Robots (ECMR) , 2023, pp. 1–8

    work page 2023

  3. [3]

    Goal recognition over pomdps: Inferring the intention of a pomdp agent,

    M. Ram ´ırez and H. Geffner, “Goal recognition over pomdps: Inferring the intention of a pomdp agent,” in Proc. IJCAI, 2011, pp. 2009–2014

    work page 2011

  4. [4]

    Active Goal Recognition

    C. Amato and A. Baisero, “Active goal recognition,” arXiv preprint arXiv:1909.11173, 2019

    work page Pith review arXiv 1909

  5. [5]

    Probabilistic decision model for adaptive task planning in human-robot collaborative assem- bly based on designer and operator intents,

    M. Cramer, K. Kellens, and E. Demeester, “Probabilistic decision model for adaptive task planning in human-robot collaborative assem- bly based on designer and operator intents,” IEEE Robot. Autom. Lett., vol. 6, no. 4, pp. 7325–7332, 2021

    work page 2021

  6. [6]

    Towards Intention Recognition for Robotic Assistants Through Online POMDP Planning

    J. C. Sabor ´ıo and J. Hertzberg, “Towards Intention Recognition for Robotic Assistants Through Online POMDP Planning,” in Plan, Activ- ity, and Intent Recognition (PAIR) at ICAPS 2023. arXiv:2411.17326, 2023

    work page Pith review arXiv 2023

  7. [7]

    Integration of planning with recog- nition for responsive interaction using classical planners,

    R. Freedman and S. Zilberstein, “Integration of planning with recog- nition for responsive interaction using classical planners,” in Proc. of the AAAI Conf. on Artificial Intelligence , vol. 31, 2017

    work page 2017

  8. [8]

    A general skeleton- based action and gesture recognition framework for human–robot collaboration,

    M. Terreran, L. Barcellona, and S. Ghidoni, “A general skeleton- based action and gesture recognition framework for human–robot collaboration,” Robot. Auton. Syst. , vol. 170, p. 104523, 2023

    work page 2023

Show all 34 references
  1. [9]

    Robot teaching system based on hand-robot contact state detection and motion intention recognition,

    Y . Pan, C. Chen, Z. Zhao, T. Hu, and J. Zhang, “Robot teaching system based on hand-robot contact state detection and motion intention recognition,” Robot. Comput.-Integr. Manuf., vol. 81, 2023

    2023

  2. [10]

    Virtual reality robot-assisted welding based on human intention recognition,

    Q. Wang, W. Jiao, R. Yu, M. T. Johnson, and Y . Zhang, “Virtual reality robot-assisted welding based on human intention recognition,” IEEE Trans. Autom. Sci. Eng. , vol. 17, no. 2, pp. 799–808, 2020

    2020

  3. [11]

    YOLO by Ultralytics,

    G. Jocher, A. Chaurasia, and J. Qiu, “YOLO by Ultralytics,” 2023. [Online]. Available: https://github.com/ultralytics/ultralytics

    2023

  4. [12]

    Probabilistic plan recognition using off- the-shelf classical planners,

    M. Ram ´ırez and H. Geffner, “Probabilistic plan recognition using off- the-shelf classical planners,” in Proc. Conf. Assoc. for the Advance- ment of Artificial Intelligence (AAAI 2010) , 2010, pp. 1121–1126

    2010

  5. [13]

    Lexicalized reasoning,

    C. Geib, “Lexicalized reasoning,” in Proc. of the 3 rd Annual Conf. on Advances in Cognitive Systems ACS , 2015

    2015

  6. [14]

    Sukthankar, C

    G. Sukthankar, C. Geib, H. Bui, D. Pynadath, and R. P. Goldman, Plan, activity, and intent recognition: Theory and practice . Newnes, 2014

    2014

  7. [15]

    A probabilistic plan recognition algorithm based on plan tree grammars,

    C. W. Geib and R. P. Goldman, “A probabilistic plan recognition algorithm based on plan tree grammars,” Artif. Intell., vol. 173, no. 11, pp. 1101–1132, 2009

    2009

  8. [16]

    From activity recognition to intention recognition for assisted living within smart homes,

    J. Rafferty, C. D. Nugent, J. Liu, and L. Chen, “From activity recognition to intention recognition for assisted living within smart homes,” IEEE Trans. Hum.-Mach. Syst. , vol. 47, no. 3, pp. 368–379, 2017

    2017

  9. [17]

    Plan recognition as planning revisited

    S. Sohrabi, A. V . Riabov, and O. Udrea, “Plan recognition as planning revisited.” in Proc. IJCAI, 2016, pp. 3258–3264

    2016

  10. [18]

    Active goal recognition using intention aware motion planning,

    J. Massardi and ´E. Beaudry, “Active goal recognition using intention aware motion planning,” in Plan, Activity and Intent Recognition (PAIR) 2021, 2021

    2021

  11. [19]

    Planning Under Uncertainty Through Goal-Driven Action Selection,

    J. C. Sabor ´ıo and J. Hertzberg, “Planning Under Uncertainty Through Goal-Driven Action Selection,” in Agents and Artificial Intelligence , J. van den Herik and A. P. Rocha, Eds. Cham: Springer International Publishing, 2019, pp. 182–201

    2019

  12. [20]

    Efficient planning under uncertainty with incremental re- finement,

    ——, “Efficient planning under uncertainty with incremental re- finement,” in Proc. of the 35 th Conf. on Uncertainty in Artificial Intelligence, Tel Aviv, Israel, ser. UAI’19, 2019, p. 112

    2019

  13. [21]

    Monte-Carlo Planning in Large POMDPs,

    D. Silver and J. Veness, “Monte-Carlo Planning in Large POMDPs,” in Adv. in Neural Information Processing Systems 23, 2010, pp. 2164– 2172

    2010

  14. [22]

    Multimodal sensor- based whole-body control for human-robot collaboration in industrial settings,

    J. de Gea Fern ´andez, D. Mronga, M. G¨unther, T. Knobloch, M. Wirkus, M. Schr ¨oer, M. Trampler, S. Stiene, E. Kirchner, V . Bargsten, T. B¨anziger, J. Teiwes, T. Kr¨uger, and F. Kirchner, “Multimodal sensor- based whole-body control for human-robot collaboration in industria...

    2017

  15. [23]

    CoSTAR: Instructing collaborative robots with behavior trees and vision,

    C. Paxton, A. Hundt, F. Jonathan, K. Guerin, and G. D. Hager, “CoSTAR: Instructing collaborative robots with behavior trees and vision,” in IEEE Intl. Conf. on Robotics and Automation (ICRA), 2017, pp. 564–571

    2017

  16. [24]

    Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges,

    R. Alami, A. Albu-Schaeffer, A. Bicchi, R. Bischoff, R. Chatila, A. De Luca, A. De Santis, G. Giralt, J. Guiochet, G. Hirzinger, F. Ingrand, V . Lippiello, R. Mattone, D. Powell, S. Sen, B. Siciliano, G. Tonietti, and L. Villani, “Safe and dependable physical human-robot inter...

    2006

  17. [25]

    Incremental task and motion planning: A constraint-based approach,

    N. T. Dantam, Z. K. Kingston, S. Chaudhuri, and L. E. Kavraki, “Incremental task and motion planning: A constraint-based approach,” in Robotics: Science and Systems , 2016

    2016

  18. [26]

    Robotic control via embodied chain-of-thought reasoning,

    M. Zawalski, W. Chen, K. Pertsch, O. Mees, C. Finn, and S. Levine, “Robotic control via embodied chain-of-thought reasoning,” in 8th Annual Conference on Robot Learning , 2024

    2024

  19. [27]

    Intention recognition in manufacturing applications,

    C. Schlenoff, Z. Kootbally, A. Pietromartire, M. Franaszek, and S. Foufou, “Intention recognition in manufacturing applications,” Robot. Comput.-Integr. Manuf., vol. 33, pp. 29–41, 2015

    2015

  20. [28]

    Integrating intention-based systems in human- robot interaction: a scoping review of sensors, algorithms, and trust,

    Y . Zhang and T. Doyle, “Integrating intention-based systems in human- robot interaction: a scoping review of sensors, algorithms, and trust,” Front. Robot. AI, vol. 10, p. 1233328, 2023

    2023

  21. [29]

    NViSII: A scriptable tool for photorealistic image generation,

    N. Morrical, J. Tremblay, Y . Lin, S. Tyree, S. Birchfield, V . Pascucci, and I. Wald, “NViSII: A scriptable tool for photorealistic image generation,” 2021

    2021

  22. [30]

    Deep object pose estimation for semantic robotic grasping of household objects,

    J. Tremblay, T. To, B. Sundaralingam, Y . Xiang, D. Fox, and S. Birch- field, “Deep object pose estimation for semantic robotic grasping of household objects,” in Conf. on Robot Learning (CoRL) , vol. abs/1809.10790, 2018, pp. 306–316

    2018 arXiv

  23. [31]

    Mesh-based object tracking for dynamic semantic 3D scene graphs via ray tracing,

    L. Niecksch, A. Mock, F. Igelbrink, T. Wiemann, and J. Hertzberg, “Mesh-based object tracking for dynamic semantic 3D scene graphs via ray tracing,” 2024. [Online]. Available: https://arxiv.org/abs/2408. 04979

    2024

  24. [32]

    Unified planning: Modeling, manipulating and solving ai planning problems in python,

    A. Micheli, A. Bit-Monnot, G. R ¨oger, E. Scala, A. Valentini, L. Framba, A. Rovetta, A. Trapasso, L. Bonassi, A. E. Gerevini, et al., “Unified planning: Modeling, manipulating and solving ai planning problems in python,” SoftwareX, vol. 29, p. 102012, 2025

    2025

  25. [33]

    A closed-loop framework-independent bridge from aiplan4eu’s unified planning platform to embedded sys- tems,

    S. H. S. S. Sadanandam, S. Stock, A. Sung, F. Ingrand, O. Lima, M. Vinci, and J. Hertzberg, “A closed-loop framework-independent bridge from aiplan4eu’s unified planning platform to embedded sys- tems,” in ICAPS Workshop on Planning and Robotics (PlanRob 2023), Prague, Czech R...

    2023

  26. [34]

    A physics-based simulated robotics testbed for planning and acting research,

    O. Lima, M. G ¨unther, A. Sung, S. Stock, M. Vinci, A. Smith, J. C. Krause, and J. Hertzberg, “A physics-based simulated robotics testbed for planning and acting research,” in ICAPS Workshop on Planning and Robotics (PlanRob 2023) , Prague, Czech Republic, 2023

    2023

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Reviewed August 5, 2026 · model on record in the stance chip above.