Pith. sign in

REVIEW 5 major objections 3 minor 112 references

FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization

T0 review · 5 major / 3 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read A feeding robot that care recipients can personalize in plain language, through mid-meal requests, custom gestures, and buttons, completed all six in-home test meals across three dining contexts.

desk verdict A credible systems contribution with real in-home deployment, but the personalization evidence is thinner than the abstract suggests; worth refereeing with expected revisions. read the letter →

arxiv 2506.14968 v2 pith:T54ZBK77 submitted 2025-06-17 cs.RO cs.AI

Rajat Kumar Jenamani , Tom Silver , Ben Dodson , Shiqin Tong , Anthony Song , Yuting Yang , Ziang Liu , Benjamin Howe
show 2 more authors
Aimee Whitneck Tapomayukh Bhattacharjee
This is my paper · ORCID
classification cs.ROcs.AI
keywords mealtimeassistanceassistiveroboticsin-the-wildpersonalizationbehaviortreeslargelanguagemodelshuman-robotinteractionin-homeevaluationcommunity-basedparticipatoryresearch
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

The paper sets out to establish that in-the-wild personalization of mealtime assistance is practical: care recipients themselves, not engineers, can adapt a feeding robot to their individual needs and preferences during real meals. FEAST combines modular hardware that swaps between feeding, drinking, and mouth-wiping tools with a web interface, head gestures, and physical buttons, and lets users change how the robot behaves through natural language. The adaptation is carried by parameterized behavior trees, where an LLM turns a request into structured updates that are statically checked for safety and summarized back to the user for transparency. Evidence includes a formative study with 21 care recipients, a five-day in-home evaluation in which two care recipients finished six meals across personal, TV, and social contexts with low reported workload, and an evaluation by an occupational therapist unfamiliar with the system. A sympathetic reading is that FEAST shows end users can tailor a multi-task care robot to their own bodies, habits, and settings.

What carries the argument

The load-bearing mechanism is the parameterized behavior tree: each skill (pick up tool, acquire bite, transfer, retract) is a behavior tree whose nodes and parameters carry human-readable names and bounded domains, such as $Speed \in \{low, medium, high\}$ or $TimeToWaitBeforeAutocontinue \in [5, 100]$. This representation makes LLM-driven personalization safe and inspectable: a natural-language request is translated by an LLM into structured tree updates, which are statically validated for valid names and in-domain values, with new nodes restricted to three empirically tested types: 'pause,' 'wait for gesture,' and 'retract.' A second LLM pass summarizes each change in plain language, and transparency queries are answered from the same tree encodings plus logs of node execution, perception, and safety checks. Around this, an automated-planning (PDDL) planner sequences skills to reach user goals, and custom gesture detectors are synthesized by LLM code generation, validated against a few user-recorded positive and negative examples.

What would settle it

Run the paper's 46 formative request types through the LLM personalization pipeline and count the updates that pass static validation yet change the wrong nodes or parameters; if that misapplication rate is high enough that users cannot reliably detect and correct errors within a meal, the in-the-wild claim fails. The documented 'use the button only when sipping' incident, where the LLM changed every tool, is the observed instance this test generalizes.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that in-the-wild personalization can be achieved by making every behavior change a small, checkable edit to a structured representation. The robot feeds, serves drinks, and wipes mouths with a single arm that swaps tools, and users steer it with requests such as 'feed me as fast as you can,' 'dip the strawberry deeper into the whipped cream,' 'do not show continue pages,' and 'move to retract position after every bite.' The paper's central empirical claims are that both care recipients completed three meals each in diverse in-the-wild contexts with few experimenter interventions, reported low cognitive workload on NASA-TLX, rated FEAST at or above 4 out of 5 on the Technology Acceptance Model, and said FEAST gave them more control and a stronger sense of independence than their human caregivers. It also claims that when the LLM misapplied a request, changing button-use for transfer completion across all tools instead of only sips, the transparency features let the user detect and iteratively correct the error.

Load-bearing premise

The load-bearing premise is that the LLM will usually turn a user's natural-language request into the behavior-tree change the user intended; the study itself shows this failing when a request to use the button 'only when taking a sip' was applied to all tools, so the in-the-wild claim depends on such errors being rare enough, and visible enough through transparency summaries, that users catch and correct them.

Editorial extensions

If this is right

  • If the central claim holds, care recipients can reconfigure a feeding robot's speed, transfer style, confirmation behavior, and interaction modality from meal to meal, without an engineer present.
  • Personalization would extend beyond feeding to drinking, mouth-wiping, retracting between bites, and custom gesture control, capabilities the paper says prior systems with fixed customizations lack.
  • The static safety net (whitelisted node types plus bounded parameter domains) means safe adaptation does not require flawless language understanding, only errors rare enough and transparent enough for users to catch.
  • The reported workload figures (mean NASA-TLX around 7–22 versus a literature baseline of 37) and Technology Acceptance Model scores at or above 4 out of 5 are the paper's evidence that in-the-wild adaptation does not impose an unusable cognitive burden.
  • A corollary the paper draws is that transparency must be reachable throughout the meal: in the study, users asked the experimenters questions that the transparency page could have answered had it been accessible from every screen.

Reading between the lines

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

  • Editorial inference: the safety design, restricting what an LLM may change at the representation level rather than trusting its output, transfers to any assistive or domestic robot whose policy is edited by non-experts; the open question is whether a three-node whitelist stays expressive enough as user requests diversify.
  • Editorial inference: the paper reports LLM misapplications qualitatively but not their rate; a precision measurement over the 46 request types (updates that pass static checks yet change the wrong nodes or parameters) would set a concrete reliability target for the pipeline.
  • Editorial inference: because the two in-home participants were co-developers of the system, the occupational-therapist evaluation is the strongest current evidence for unfamiliar-user usability; a longitudinal study with naive care recipients would test whether the low workload scores survive outside co-design.
  • Editorial inference: the gesture-synthesis ablation (F1 score of about 0.9 with personalized parameters versus 0.63 without) suggests a broader design lesson: for users with limited mobility, interaction modalities are better synthesized from a few personal examples than chosen from a fixed library.
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 / 3 minor

Summary. FEAST is a mealtime-assistance robot system that combines modular hardware (feeding, drinking, mouth-wiping tools), a web-based interface, head-gesture and button inputs, and parameterized behavior trees that can be adapted through natural language via a large language model. The authors conduct a formative study with 21 care recipients to identify personalization needs, then evaluate FEAST in a five-day in-home study with two care recipients (who are also community researchers and co-authors), spanning six meals in personal, TV-watching, and social contexts. They also report an evaluation with an occupational therapist unfamiliar with the system. The paper claims that FEAST can be personalized in the wild to meet the unique needs of individual care recipients, with low workload, high user acceptance, and broad coverage of the adaptability requests identified in the formative study.

Significance. If the claims hold, FEAST is a meaningful step towards deployable, personalized mealtime assistance: it is one of the first systems to integrate feeding, drinking, and mouth wiping with open-ended, natural-language personalization, and it reports real in-home meals across varied contexts. The paper is unusually transparent in its appendices, which document per-meal experimenter interventions, personalization requests, LLM responses, and OT scenarios; this level of detail is a genuine strength for reproducibility and for understanding failure modes. The community-based participatory research methodology is appropriate for assistive robotics, and the authors are candid about the insider status of the two care recipients. However, the evidence base for the central 'personalized in-the-wild' claim is narrow: only two co-designer participants, an acknowledged limitation in Section VIII, and the abstract's wording overstates what the data can support. The 'outperforms a state-of-the-art baseline' claim is also based on a coverage count rather than a direct comparative evaluation.

major comments (5)
  1. [Section V-A, Figure 5, and abstract] The abstract claims that FEAST 'outperforms a state-of-the-art baseline limited to fixed customizations,' but the only support is the coverage count in Section V-A (36 of 46 request types for FEAST vs. 17 for Nanavati et al. and 15 for FLAIR). This is a self-scored capability comparison based on the authors' categorization in Table III, not a head-to-head user evaluation. The OT study in Section VII compares personalized FEAST against the default no-personalization FEAST, not against another system. The abstract and Section V-A should be reworded to say 'covers more of the personalization request types identified in our formative study' rather than 'outperforms,' unless a direct comparative study is added.
  2. [Section IV-B (Safety Checks) and Section V-C] The statement that restricting node additions to 'pause,' 'wait for gesture,' and 'retract,' and confining parameter changes to predefined domains, 'guarantees safety' is not established. The static checks verify syntactic conformance to a whitelist and parameter bounds, but they do not prove that every accepted combination of nodes and parameters is physically safe across all user states, food configurations, and environmental contexts. The safety hardware and monitoring features in Section V-C are reasonable mitigations, but 'guarantee' is too strong. I recommend replacing 'guarantee' with 'mitigate' and adding a hazard analysis or a systematic failure-mode evaluation to support the safety claim.
  3. [Section VI-C, Table II, and Appendix E] The claim that users can personalize FEAST 'without engineer involvement' is not fully supported by the reported study. There were 18 experimenter interventions and 20 explanations across six meals, and some interventions directly modified personalization state: Meal ID 6 reports 'Bug in behavior tree param... manually updated to 100' and Meal ID 4 reports 'Had to code back continuous transfer in (experimenter fault).' The presence of experimenters who can edit behavior trees is reasonable for a prototype study, but the paper should explicitly report which personalization operations were completed end-to-end by the participant alone and which required experimenter action, and it should temper the 'without engineer involvement' phrasing accordingly.
  4. [Section VI-D, Lesson 2, and Appendix E] No aggregate success rate is reported for the central natural-language-to-behavior-tree personalization mechanism. The appendix shows multiple requests that were partially applied, misapplied to the wrong set of tools, or rejected as invalid (e.g., the 'Use button when completing a transfer when taking a sip' request initially changed all tools in Meal ID 1, and several requests in Meal IDs 3 and 6 required iterative rephrasing). Since reliable LLM translation is load-bearing for the personalization claim, the paper should quantify, from the existing logs, the first-attempt success rate, the fraction of requests requiring user correction, and the final success rate after transparency-based iteration.
  5. [Section VI-A and Section VIII] The only in-the-wild personalization evidence comes from CR1 and CR2, who are community researchers and co-authors deeply involved in the system's design since 2022; the occupational therapist is not a care recipient and was evaluated in a controlled scenario, not in the home. The paper acknowledges this in Section VIII, but the abstract's unhedged claim that FEAST 'can be personalized in-the-wild to meet the unique needs of individual care recipients' goes beyond the evidence. I recommend softening the central claim to describe a feasibility demonstration with two co-designer care recipients, and to state more prominently that transfer to non-expert end users remains untested.
minor comments (3)
  1. [Section V-C and Appendix C vs. Table I] The safety standard number is inconsistent: Table I references ISO 13482, while Section V-C and Appendix C repeatedly use 'ISO 13842.' The correct designation for 'Robots and robotic devices — Safety requirements for personal care robots' is ISO 13482; please correct all occurrences.
  2. [Section VI-B and Section VI-C] The NASA-TLX description in Section VI-B says participants completed a 7-point Likert scale, while Section VI-C reports workload on a 0-100 scale. Please clarify the mapping or make the scales consistent.
  3. [Section VI-C, Figure 7] The statement that drink acquisition averaged 100.0 ± 0.0% across Meal IDs 2-5 is slightly confusing because Meal ID 6 also had successful drink acquisition (2/2 in Table II). Consider reporting across Meal IDs 2-6 to avoid an apparent omission.

Circularity Check

1 steps flagged · score 4.0 of 10

Gesture-synthesis ablation evaluates on its own training examples, but the central in-the-wild personalization claim rests on separate empirical studies.

  1. fitted input called prediction [Appendix B (Gesture Synthesis Using Large Language Models), ablation paragraph; referenced by Section V-A.]
    "We then optimize these hyperparameters for each proposed program using a grid search with 10 values per dimension. The objective for optimization is classification accuracy with respect to the user-provided positive and negative examples. ... The personalized gesture detector achieves an F1-score of 0.9 ± 0.02 on the examples, compared to 0.63 ± 0.09 for no personalized parameters."

    The LLM-generated detector and its hyperparameters are selected and optimized to maximize classification accuracy on the user-provided positive and negative examples, and the reported F1-scores are computed on those same examples. No held-out split or cross-validation is described. The claimed advantage of personalized over non-personalized gesture detection therefore reflects fit to the training data rather than an independent measure of generalization; the result is forced by the optimization objective by construction.

full rationale

One concrete circular reduction exists: in Appendix B, the gesture detector is chosen and hyperparameter-tuned to maximize accuracy on the user-provided examples, then evaluated on those same examples. This makes the reported F1 improvement a training-set comparison, not a predictive result. This circularity is peripheral to the paper's central claim: the six-meal in-home study with the two community researchers and the separate Occupational Therapist evaluation provide independent, empirical support for the in-the-wild personalization claim, though the in-home evidence is limited by the participants' co-designer status. The paper itself flags this limitation in Section VIII ('Since they were familiar with FEAST's development, they may have had a lower cognitive workload than other users'), which I weigh as an external-validity concern rather than a derivation-level circularity. No equation-level or self-citation chain forces the main results, and the ISO/IEEE and behavior-tree safety arguments are compliance mappings and system designs, not tautological predictions.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This is an engineering systems paper with no fitted mathematical parameters or newly postulated physical entities. The system's adjustable parameters (e.g., speed, transfer distance, timer values) are user-facing settings, not free parameters fitted to data. The main assumptions concern LLM reliability, safety of the restricted update space, and generalizability of the participant results.

assumptions (3)
  • domain assumption GPT-4o will translate natural-language personalization requests into valid, safe behavior-tree updates within at most three re-prompts.
    The adaptability claim depends on LLM reliability; the paper documents at least one instance where the LLM applied a button-setting to all tools instead of only sips (Section VI-D, Lesson 2), so this assumption is not always met.
  • ad hoc to paper Restricting node additions to 'pause', 'wait for gesture', and 'retract', and confining parameter changes to predefined domains, is sufficient to guarantee safety of personalized behavior.
    This is a design choice introduced by the authors; no formal safety proof is given, and the system experienced a robot fall during an aborted meal (Appendix E-G).
  • domain assumption The formative study with 21 care recipients and the in-home evaluation with two community researchers generalize to the broader care-recipient population.
    The paper acknowledges this limitation in Section VIII, noting that the two CRs were familiar with the system and may have lower cognitive workload than other users.

how reviews work

0 comments
Cite this review

Pith. "Pith review of FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization." pith.science (2026). https://pith.science/paper/T54ZBK77

@misc{pith2026250614968,
  author       = {Pith},
  title        = {Pith review of: FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T54ZBK77}},
  note         = {Machine review of arXiv:2506.14968}
}
read the original abstract

Physical caregiving robots hold promise for improving the quality of life of millions worldwide who require assistance with feeding. However, in-home meal assistance remains challenging due to the diversity of activities (e.g., eating, drinking, mouth wiping), contexts (e.g., socializing, watching TV), food items, and user preferences that arise during deployment. In this work, we propose FEAST, a flexible mealtime-assistance system that can be personalized in-the-wild to meet the unique needs of individual care recipients. Developed in collaboration with two community researchers and informed by a formative study with a diverse group of care recipients, our system is guided by three key tenets for in-the-wild personalization: adaptability, transparency, and safety. FEAST embodies these principles through: (i) modular hardware that enables switching between assisted feeding, drinking, and mouth-wiping, (ii) diverse interaction methods, including a web interface, head gestures, and physical buttons, to accommodate diverse functional abilities and preferences, and (iii) parameterized behavior trees that can be safely and transparently adapted using a large language model. We evaluate our system based on the personalization requirements identified in our formative study, demonstrating that FEAST offers a wide range of transparent and safe adaptations and outperforms a state-of-the-art baseline limited to fixed customizations. To demonstrate real-world applicability, we conduct an in-home user study with two care recipients (who are community researchers), feeding them three meals each across three diverse scenarios. We further assess FEAST's ecological validity by evaluating with an Occupational Therapist previously unfamiliar with the system. In all cases, users successfully personalize FEAST to meet their individual needs and preferences. Website: https://emprise.cs.cornell.edu/feast

Figures

Figures reproduced from arXiv: 2506.14968 by the authors.

Figure 1
Figure 1. Informed by a formative study with 19 care recipients and 2 community researchers, we propose FEAST: a flexible mealtime-assistance [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. We use speculative videos recorded with community researchers (left) to conduct a formative study with 19 care recipients and the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. We extract and categorize personalization requests from our formative study with 21 care recipients. See Appendix A for details. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: FEAST features diverse mealtime-assistance skills such as feeding, drinking and mouth wiping, custom tools, and a flexible web [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: FEAST proposes a personalization framework for mealtime assistance built on three key tenets: (i) adaptability towards tackling the [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Meals fed to care recipients. The images, captured by the robot’s in-hand camera, highlight the variability in lighting conditions. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Left: Per-meal NASA-TLX survey results, with CR1 and CR2 meals shown in chronological order. Center: TAM results, including [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

112 extracted references · 59 canonical work pages

  1. [1]

    Design prin- ciples for robot-assisted feeding in social contexts,

    A. Nanavati, P. Alves-Oliveira, T. Schrenk, E. K. Gor- don, M. Cakmak, and S. S. Srinivasa, “Design prin- ciples for robot-assisted feeding in social contexts,” in Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, 2023, pp. 24– 33

    2023

  2. [2]

    Man- ual feeding device experiences of people with a neu- rodisability,

    A. Mandy, T. Sims, G. Stew, and D. Onions, “Man- ual feeding device experiences of people with a neu- rodisability,” The American Journal of Occupational Therapy, vol. 72, no. 3, pp. 7 203 345 010p1– 7 203 345 010p5, 2018

    2018

  3. [3]

    How people with stroke and healthy older people experience the eating process,

    C. Jacobsson, K. Axelsson, P. O. ¨Osterlind, and A. Norberg, “How people with stroke and healthy older people experience the eating process,” Journal of Clinical Nursing, vol. 9, no. 2, pp. 255–264, 2000. [Online]. Available: https://onlinelibrary.wiley.com/doi/ abs/10.1046/j.1365-2702.2000.00355.x

    work page arXiv

  4. [4]

    An altered eating experience: Attitudes toward feeding assistance among younger and older adults,

    S. E. Shune, “An altered eating experience: Attitudes toward feeding assistance among younger and older adults,” Rehabilitation nursing : the official journal of the Association of Rehabilitation Nurses, 2020

    2020

  5. [5]

    Americans with disabilities: 2014,

    D. M. Taylor, “Americans with disabilities: 2014,” US Census Bureau, pp. 1–32, 2018

    2014

  6. [6]

    Caregiver time use in als,

    A. Chi `o, A. Gauthier, A. Vignola, A. Calvo, P. Ghiglione, E. Cavallo, A. Terreni, and R. Mutani, “Caregiver time use in als,” Neurology, vol. 67, no. 5, pp. 902–904, 2006

    2006

  7. [7]

    Care- giver and clinician shortages in an aging nation,

    K. C. Fleming, J. M. Evans, and D. S. Chutka, “Care- giver and clinician shortages in an aging nation,” in Mayo Clinic Proceedings, vol. 78, no. 8. Elsevier, 2003, pp. 1026–1040

    2003

  8. [8]

    Exploring workload among informal caregivers of persons with dementia,

    R. Zenker, C. Pardell, A. Gilmore-Bykovskyi, A. J. Kind, and N. Werner, “Exploring workload among informal caregivers of persons with dementia,” in proceedings of the human factors and ergonomics society annual meeting, vol. 61, no. 1. SAGE Publica- tions Sage CA: Los Angeles, CA, 2017, pp. 1297–1297

    2017

Show all 112 references
  1. [9]

    The role of assistive robotics in the lives of persons with disability,

    S. W. Brose, D. J. Weber, B. A. Salatin, G. G. Grindle, H. Wang, J. J. Vazquez, and R. A. Cooper, “The role of assistive robotics in the lives of persons with disability,” American Journal of Physical Medicine & Rehabilitation, vol. 89, no. 6, pp. 509–521, 2010

    2010

  2. [10]

    Is more autonomy always better? exploring preferences of users with mobility impairments in robot-assisted feeding,

    T. Bhattacharjee, E. K. Gordon, R. Scalise, M. E. Cabr- era, A. Caspi, M. Cakmak, and S. S. Srinivasa, “Is more autonomy always better? exploring preferences of users with mobility impairments in robot-assisted feeding,” in 2020 15th ACM/IEEE International Conference on Human-...

    2020

  3. [11]

    Feel the bite: Robot- assisted inside-mouth bite transfer using robust mouth perception and physical interaction-aware control,

    R. K. Jenamani, D. Stabile, Z. Liu, A. Anwar, K. Dim- itropoulou, and T. Bhattacharjee, “Feel the bite: Robot- assisted inside-mouth bite transfer using robust mouth perception and physical interaction-aware control,” in Proceedings of the 2024 ACM/IEEE International Conferenc...

    2024

  4. [12]

    Robot-assisted feeding: Generalizing skewering strate- gies across food items on a plate,

    R. Feng, Y . Kim, G. Lee, E. K. Gordon, M. Schmit- tle, S. Kumar, T. Bhattacharjee, and S. S. Srinivasa, “Robot-assisted feeding: Generalizing skewering strate- gies across food items on a plate,” in The International Symposium of Robotics Research. Springer, 2019, pp. 427–442

    2019

  5. [13]

    Adaptive robot-assisted feeding: An online learning framework for acquiring previously unseen food items,

    E. K. Gordon, X. Meng, T. Bhattacharjee, M. Barnes, and S. S. Srinivasa, “Adaptive robot-assisted feeding: An online learning framework for acquiring previously unseen food items,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020, ...

    2020

  6. [14]

    Leveraging post hoc context for faster learning in bandit settings with applications in robot-assisted feeding,

    E. K. Gordon, S. Roychowdhury, T. Bhattacharjee, K. Jamieson, and S. S. Srinivasa, “Leveraging post hoc context for faster learning in bandit settings with applications in robot-assisted feeding,” in 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2...

    2021

  7. [15]

    Towards general single-utensil food acquisition with human-informed actions,

    E. K. Gordon, A. Nanavati, R. Challa, B. H. Zhu, T. A. K. Faulkner, and S. Srinivasa, “Towards general single-utensil food acquisition with human-informed actions,” in Conference on Robot Learning. PMLR, 2023, pp. 2414–2428

    2023

  8. [16]

    Learning bimanual scooping policies for food acquisition,

    J. Grannen, Y . Wu, S. Belkhale, and D. Sadigh, “Learning bimanual scooping policies for food acquisition,” in 6th Annual Conference on Robot Learning, 2022. [Online]. Available: https://openreview.net/forum?id=qDtbMK67PJG

    2022

  9. [17]

    Scone: A food scooping robot learning framework with active perception,

    Y .-L. Tai, Y . C. Chiu, Y .-W. Chao, and Y .-T. Chen, “Scone: A food scooping robot learning framework with active perception,” in Conference on Robot Learning. PMLR, 2023, pp. 849–865

    2023

  10. [18]

    Imrl: Integrating visual, physical, temporal, and ge- ometric representations for enhanced food acquisition,

    R. Liu, Z. Mahammad, A. Bhaskar, and P. Tokekar, “Imrl: Integrating visual, physical, temporal, and ge- ometric representations for enhanced food acquisition,” arXiv preprint arXiv:2409.12092, 2024

    2024 arXiv

  11. [19]

    Learning se- quential acquisition policies for robot-assisted feeding,

    P. Sundaresan, J. Wu, and D. Sadigh, “Learning se- quential acquisition policies for robot-assisted feeding,” in Conference on Robot Learning. PMLR, 2023, pp. 1282–1299

    2023

  12. [20]

    Flair: Feeding via long- horizon acquisition of realistic dishes,

    R. K. Jenamani, P. Sundaresan, M. Sakr, T. Bhat- tacharjee, and D. Sadigh, “Flair: Feeding via long- horizon acquisition of realistic dishes,” arXiv preprint arXiv:2407.07561, 2024

    2024 arXiv

  13. [21]

    Repeat: A real2sim2real approach for pre-acquisition of soft food items in robot-assisted feeding,

    N. Ha, R. Ye, Z. Liu, S. Sinha, and T. Bhattacharjee, “Repeat: A real2sim2real approach for pre-acquisition of soft food items in robot-assisted feeding,” IEEE International Conference on Intelligent Robots and Systems (IROS), 2024

    2024

  14. [22]

    Lava: Long-horizon visual action based food acquisition,

    A. Bhaskar, R. Liu, V . D. Sharma, G. Shi, and P. Tokekar, “Lava: Long-horizon visual action based food acquisition,” arXiv preprint arXiv:2403.12876, 2024

    2024 arXiv

  15. [23]

    Transfer depends on acquisition: Analyz- ing manipulation strategies for robotic feeding,

    D. Gallenberger, T. Bhattacharjee, Y . Kim, and S. S. Srinivasa, “Transfer depends on acquisition: Analyz- ing manipulation strategies for robotic feeding,” in 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2019, pp. 267– 276

    2019

  16. [24]

    Balancing efficiency and comfort in robot-assisted bite transfer,

    S. Belkhale, E. K. Gordon, Y . Chen, S. Srinivasa, T. Bhattacharjee, and D. Sadigh, “Balancing efficiency and comfort in robot-assisted bite transfer,” in 2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022, pp. 4757–4763

    2022

  17. [25]

    Active robot-assisted feeding with a general- purpose mobile manipulator: Design, evaluation, and lessons learned,

    D. Park, Y . Hoshi, H. P. Mahajan, H. K. Kim, Z. Erickson, W. A. Rogers, and C. C. Kemp, “Active robot-assisted feeding with a general- purpose mobile manipulator: Design, evaluation, and lessons learned,” Robotics and Autonomous Systems, vol. 124, p. 103344, 2020. [Online]. A...

    2020

  18. [26]

    In-mouth robotic bite trans- fer with visual and haptic sensing,

    L. Shaikewitz, Y . Wu, S. Belkhale, J. Grannen, P. Sun- daresan, and D. Sadigh, “In-mouth robotic bite trans- fer with visual and haptic sensing,” arXiv preprint arXiv:2211.12705, 2022

    2022 arXiv

  19. [27]

    Sparcs: Structuring physically assistive robotics for caregiving with stakeholders-in-the-loop,

    R. Madan, R. K. Jenamani, V . T. Nguyen, A. Moustafa, X. Hu, K. Dimitropoulou, and T. Bhattacharjee, “Sparcs: Structuring physically assistive robotics for caregiving with stakeholders-in-the-loop,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (I...

    2022

  20. [28]

    Hacker, Community-based participatory research

    K. Hacker, Community-based participatory research. Sage publications, 2013

    2013

  21. [29]

    Teleconference ac- cessibility and guidelines for deaf and hard of hear- ing users,

    R. S. Kushalnagar and C. V ogler, “Teleconference ac- cessibility and guidelines for deaf and hard of hear- ing users,” in Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility, 2020, pp. 1–6

    2020

  22. [30]

    From research participant to co-researcher: Chloe’s story on co- designing inclusive technologies with people with intellectual disability,

    C. Haidenhofer, L. Sitbon, C. P. Beaumont, M. Hoogstrate, and J. L. Korte, “From research participant to co-researcher: Chloe’s story on co- designing inclusive technologies with people with intellectual disability,” in Proceedings of the 26th International ACM SIGACCESS Confe...

    2024

  23. [31]

    Depending on in- dependence an autoethnographic account of daily use of assistive technologies,

    F. Fussenegger and K. Spiel, “Depending on in- dependence an autoethnographic account of daily use of assistive technologies,” in Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility, 2022, pp. 1–6

    2022

  24. [32]

    Robots for humanity: User-centered design for assistive mobile manipula- tion,

    T. L. Chen, M. Ciocarlie, S. Cousins, P. Grice, K. Hawkins, K. Hsiao, C. C. Kemp, C.-H. King, D. A. Lazewatsky, A. Leeper et al., “Robots for humanity: User-centered design for assistive mobile manipula- tion,” in 2012 IEEE/RSJ International Conference on Intelligent Robots an...

    2012

  25. [33]

    Robots for joy, robots for sorrow: community based robot design for dementia caregivers,

    S. Moharana, A. E. Panduro, H. R. Lee, and L. D. Riek, “Robots for joy, robots for sorrow: community based robot design for dementia caregivers,” in 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2019, pp. 458–467

    2019

  26. [34]

    Indepen- dence in the home: A wearable interface for a person with quadriplegia to teleoperate a mobile manipulator,

    A. Padmanabha, J. Gupta, C. Chen, J. Yang, V . Nguyen, D. J. Weber, C. Majidi, and Z. Erickson, “Indepen- dence in the home: A wearable interface for a person with quadriplegia to teleoperate a mobile manipulator,” in Proceedings of the 2024 ACM/IEEE International Conference o...

    2024

  27. [35]

    Robots for humanity: In-home deploy- ment of stretch re2,

    V . Ranganeni, V . Nguyen, H. Evans, J. Evans, J. Mehu, S. Olatunji, W. Rogers, A. Edsinger, C. Kemp, and M. Cakmak, “Robots for humanity: In-home deploy- ment of stretch re2,” in Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 2024, pp. 1299–1301

    2024

  28. [36]

    Lessons learned from designing and evaluating a robot-assisted feeding system for out- of-lab use,

    A. Nanavati, E. K. Gordon, T. A. Kessler Faulkner, Y . R. Song, J. Ko, T. Schrenk, V . Nguyen, B. H. Zhu, H. Bolotski, A. Kashyap, S. Kutty, R. Karim, L. Rainbolt, R. Scalise, H. Song, R. Qu, M. Cakmak, and S. S. Srinivasa, “Lessons learned from designing and evaluating a robo...

    2025

  29. [37]

    Development of nasa-tlx (task load index): Results of empirical and theoretical research,

    S. Hart, “Development of nasa-tlx (task load index): Results of empirical and theoretical research,” Human mental workload/Elsevier, 1988

    1988

  30. [38]

    User acceptance of computer technology: A comparison of two theoretical models,

    F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “User acceptance of computer technology: A comparison of two theoretical models,” Management science, vol. 35, no. 8, pp. 982–1003, 1989

    1989

  31. [39]

    I. O. for Standardization, Robots and Robotic Devices: Safety Requirements for Personal Care Robots, 2014

    2014

  32. [40]

    Ieee standard for trans- parency of autonomous systems,

    I. T. S. C. of the IEEE Vehicular Technology So- ciety, the Standing Committee for Standards of the IEEE Robotics, and A. Society, “Ieee standard for trans- parency of autonomous systems,” IEEE Std 7001-2021, 2022

    2021

  33. [41]

    Meet Obi,

    “Meet Obi,” https://meetobi.com/, [Online; accessed 1- Jan-2025]

    2025

  34. [42]

    Neater eater robot,

    “Neater eater robot,” 2024, (Accessed: 1st January, 2025). [Online]. Available: https://www.neater.co.uk/ neater-eater-robotic

    2024

  35. [43]

    Veterans administration pros- thetics center research report,

    A. Staros and E. Peizer, “Veterans administration pros- thetics center research report,” Bulletin of Prosthetics Research, 1977

    1977

  36. [44]

    Meal assistance robots: A review on current status, challenges and future di- rections,

    I. Naotunna, C. J. Perera, C. Sandaruwan, R. Gopura, and T. D. Lalitharatne, “Meal assistance robots: A review on current status, challenges and future di- rections,” in IEEE/SICE International Symposium on System Integration, 2015, pp. 211–216

    2015

  37. [45]

    Learning visuo-haptic skewering strategies for robot-assisted feeding,

    P. Sundaresan, S. Belkhale, and D. Sadigh, “Learning visuo-haptic skewering strategies for robot-assisted feeding,” in 6th Annual Conference on Robot Learning, 2022. [Online]. Available: https://openreview.net/forum?id=lLq09gV oaTE

    2022

  38. [46]

    Leveraging multimodal haptic sensory data for ro- bust cutting,

    K. Zhang, M. Sharma, M. Veloso, and O. Kroemer, “Leveraging multimodal haptic sensory data for ro- bust cutting,” in 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), 2019

    2019

  39. [47]

    Vision augmented robot feeding,

    A. Candeias, T. Rhodes, M. Marques, M. Veloso et al., “Vision augmented robot feeding,” in Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0–0

    2018

  40. [48]

    Autonomous multi-sensory robotic as- sistant for a drinking task,

    F. F. Goldau, T. K. Shastha, M. Kyrarini, and A. Gr ¨aser, “Autonomous multi-sensory robotic as- sistant for a drinking task,” in 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). IEEE, 2019, pp. 210–216

    2019

  41. [49]

    An au- tonomous robotic assistant for drinking,

    S. Schr ¨oer, I. Killmann, B. Frank, M. V ¨olker, L. Fiederer, T. Ball, and W. Burgard, “An au- tonomous robotic assistant for drinking,” in 2015 IEEE international conference on robotics and automation (ICRA). IEEE, 2015, pp. 6482–6487

    2015

  42. [50]

    Wiping the mouth using the robot arm with information of rgb-d sensor,

    T. Ikeda, T. Makino, S. Furuno, and F. Nagata, “Wiping the mouth using the robot arm with information of rgb-d sensor,” Artificial Life and Robotics, vol. 24, pp. 360– 367, 2019

    2019

  43. [51]

    Informing assistive robots with mod- els of contact forces from able-bodied face wiping and shaving,

    K. P. Hawkins, C.-H. King, T. L. Chen, and C. C. Kemp, “Informing assistive robots with mod- els of contact forces from able-bodied face wiping and shaving,” in 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication. IEEE, 2012, pp...

    2012

  44. [52]

    A community-centered design framework for robot-assisted feeding sys- tems,

    T. Bhattacharjee, M. E. Cabrera, A. Caspi, M. Cak- mak, and S. S. Srinivasa, “A community-centered design framework for robot-assisted feeding sys- tems,” in Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility, 2019, pp. 482–494

    2019

  45. [53]

    Are prefer- ences useful for better assistance? a physically assistive robotics user study,

    G. Canal, C. Torras, and G. Aleny `a, “Are prefer- ences useful for better assistance? a physically assistive robotics user study,” THRI, 2021

    2021

  46. [54]

    Physically assistive robots: A systematic review of mobile and manipulator robots that physically assist people with disabilities,

    A. Nanavati, V . Ranganeni, and M. Cakmak, “Physically assistive robots: A systematic review of mobile and manipulator robots that physically assist people with disabilities,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 7, 2023

    2023

  47. [55]

    Desire- driven reasoning considering personalized care prefer- ences,

    G. Yang, S. Wang, J. Yang, and P. Shi, “Desire- driven reasoning considering personalized care prefer- ences,” Transactions on Systems, Man, and Cybernetics: Systems, 2021

    2021

  48. [56]

    Learning representations that enable generalization in assistive tasks,

    J. Z.-Y . He, Z. Erickson, D. S. Brown, A. Raghunathan, and A. Dragan, “Learning representations that enable generalization in assistive tasks,” in Conference on Robot Learning. PMLR, 2023, pp. 2105–2114

    2023

  49. [57]

    Grace: Generaliz- ing robot-assisted caregiving with user functionality em- beddings,

    Y . Gao, H. J. Chang, and Y . Demiris, “Grace: Generaliz- ing robot-assisted caregiving with user functionality em- beddings,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015, pp. 1840–1845

    2015

  50. [58]

    Tidybot: Personalized robot assistance with large lan- guage models,

    J. Wu, R. Antonova, A. Kan, M. Lepert, A. Zeng, S. Song, J. Bohg, S. Rusinkiewicz, and T. Funkhouser, “Tidybot: Personalized robot assistance with large lan- guage models,” arXiv preprint arXiv:2305.05658, 2023

    2023 arXiv

  51. [59]

    Grace: Generalizing robot-assisted caregiving with user functionality embeddings,

    Z. Liu, Y . Ju, Y . Da, T. Silver, P. N. Thakkar, J. Li, J. Guo, K. Dimitropoulou, and T. Bhattachar- jee, “Grace: Generalizing robot-assisted caregiving with user functionality embeddings,” arXiv preprint arXiv:2501.17855, 2025

    2025 arXiv

  52. [60]

    Personalizing gesture recognition using hierar- chical bayesian neural networks,

    A. Joshi, S. Ghosh, M. Betke, S. Sclaroff, and H. Pfis- ter, “Personalizing gesture recognition using hierar- chical bayesian neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 6513–6522

    2017

  53. [61]

    Personalization of human- robot gestural communication through voice interaction grounding,

    H. Brock and R. Gomez, “Personalization of human- robot gestural communication through voice interaction grounding,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2021, pp. 846–853

    2021

  54. [62]

    Designing a first person shooter game for quadriplegics,

    A. Taheri and Z. Weissman, “Designing a first person shooter game for quadriplegics,” in Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, 2021, pp. 1–6

    2021

  55. [63]

    Head motion and head gesture-based robot control: A usabil- ity study,

    A. Jackowski, M. Gebhard, and R. Thietje, “Head motion and head gesture-based robot control: A usabil- ity study,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 1, pp. 161–170, 2017

    2017

  56. [64]

    Adapting robot task planning to user preferences: an assistive shoe dressing example,

    G. Canal, G. Aleny `a, and C. Torras, “Adapting robot task planning to user preferences: an assistive shoe dressing example,” Autonomous Robots, 2019

    2019

  57. [65]

    Gpt-4 technical report,

    J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Alt- man, S. Anadkat et al., “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774, 2023

    2023 arXiv

  58. [66]

    Palm: Scaling language modeling with pathways,

    A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann et al., “Palm: Scaling language modeling with pathways,” Journal of Machine Learning Research, vol. 24, no. 240, pp. 1–113, 2023

    2023

  59. [67]

    Can large language models reason and plan?

    S. Kambhampati, “Can large language models reason and plan?” Annals of the New York Academy of Sciences, 2024

    2024

  60. [68]

    V oicepilot: Harnessing llms as speech interfaces for physically as- sistive robots,

    A. Padmanabha, J. Yuan, J. Gupta, Z. Karachiwalla, C. Majidi, H. Admoni, and Z. Erickson, “V oicepilot: Harnessing llms as speech interfaces for physically as- sistive robots,” arXiv preprint arXiv:2404.04066, 2024

    2024 arXiv

  61. [69]

    Autotamp: Autoregressive task and motion planning with llms as translators and checkers,

    Y . Chen, J. Arkin, Y . Zhang, N. Roy, and C. Fan, “Autotamp: Autoregressive task and motion planning with llms as translators and checkers,” arXiv preprint arXiv:2306.06531, 2023

    2023 arXiv

  62. [70]

    Inner monologue: Embodied reasoning through planning with language models,

    W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Flo- rence, A. Zeng, J. Tompson, I. Mordatch, Y . Cheb- otar et al., “Inner monologue: Embodied reasoning through planning with language models,” arXiv preprint arXiv:2207.05608, 2022

    2022 arXiv

  63. [71]

    Taps: Task-agnostic policy sequencing,

    C. Agia, T. Migimatsu, J. Wu, and J. Bohg, “Taps: Task-agnostic policy sequencing,”arXiv preprint arXiv:2210.12250, 2022

    2022 arXiv

  64. [72]

    Text2motion: From natural language instructions to feasible plans,

    K. Lin, C. Agia, T. Migimatsu, M. Pavone, and J. Bohg, “Text2motion: From natural language instructions to feasible plans,” Autonomous Robots, vol. 47, no. 8, pp. 1345–1365, 2023

    2023

  65. [73]

    Progprompt: Generating situated robot task plans using large language models,

    I. Singh, V . Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg, “Progprompt: Generating situated robot task plans using large language models,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 11 523–11 530

    2023

  66. [74]

    Rt-1: Robotics transformer for real-world control at scale,

    A. Brohan, N. Brown, J. Carbajal, Y . Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu et al., “Rt-1: Robotics transformer for real-world control at scale,” arXiv preprint arXiv:2212.06817, 2022

    2022 arXiv

  67. [75]

    Rt-2: Vision-language-action models transfer web knowledge to robotic control,

    A. Brohan, N. Brown, J. Carbajal, Y . Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn et al., “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” arXiv preprint arXiv:2307.15818, 2023

    2023 arXiv

  68. [76]

    Open x-embodiment: Robotic learning datasets and rt-x models,

    A. Padalkar, A. Pooley, A. Jain, A. Bewley, A. Herzog, A. Irpan, A. Khazatsky, A. Rai, A. Singh, A. Brohan et al., “Open x-embodiment: Robotic learning datasets and rt-x models,” arXiv preprint arXiv:2310.08864, 2023

    2023 arXiv

  69. [77]

    Openvla: An open-source vision-language- action model,

    M. J. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Bal- akrishna, S. Nair, R. Rafailov, E. Foster, G. Lam, P. San- keti et al., “Openvla: An open-source vision-language- action model,” arXiv preprint arXiv:2406.09246, 2024

    2024 arXiv

  70. [78]

    Language to rewards for robotic skill synthesis,

    W. Yu, N. Gileadi, C. Fu, S. Kirmani, K.-H. Lee, M. G. Arenas, H.-T. L. Chiang, T. Erez, L. Hasenclever, J. Humplik et al., “Language to rewards for robotic skill synthesis,” arXiv preprint arXiv:2306.08647, 2023

    2023 arXiv

  71. [79]

    Eureka: Human-level reward design via coding large language models,

    Y . J. Ma, W. Liang, G. Wang, D.-A. Huang, O. Bastani, D. Jayaraman, Y . Zhu, L. Fan, and A. Anandkumar, “Eureka: Human-level reward design via coding large language models,” arXiv preprint arXiv:2310.12931, 2023

    2023 arXiv

  72. [80]

    Code as policies: Language model programs for embodied control,

    J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for embodied control,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 9493–9500

    2023

  73. [81]

    How to prompt your robot: A promptbook for manipulation skills with code as policies,

    M. G. Arenas, T. Xiao, S. Singh, V . Jain, A. Ren, Q. Vuong, J. Varley, A. Herzog, I. Leal, S. Kirmani et al., “How to prompt your robot: A promptbook for manipulation skills with code as policies,” in 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE,...

    2024

  74. [82]

    V ocal sandbox: Continual learning and adaptation for situated human-robot collaboration,

    J. Grannen, S. Karamcheti, S. Mirchandani, P. Liang, and D. Sadigh, “V ocal sandbox: Continual learning and adaptation for situated human-robot collaboration,” arXiv preprint arXiv:2411.02599, 2024

    2024 arXiv

  75. [83]

    Rt-h: Action hierarchies using language,

    S. Belkhale, T. Ding, T. Xiao, P. Sermanet, Q. Vuong, J. Tompson, Y . Chebotar, D. Dwibedi, and D. Sadigh, “Rt-h: Action hierarchies using language,” arXiv preprint arXiv:2403.01823, 2024

    2024 arXiv

  76. [84]

    Yell at your robot: Improving on-the-fly from language corrections,

    L. X. Shi, Z. Hu, T. Z. Zhao, A. Sharma, K. Pertsch, J. Luo, S. Levine, and C. Finn, “Yell at your robot: Improving on-the-fly from language corrections,” arXiv preprint arXiv:2403.12910, 2024

    2024 arXiv

  77. [85]

    Kinova gen3 7dof robotic arm,

    “Kinova gen3 7dof robotic arm,” 2025, (Accessed: 1st January, 2025). [Online]. Available: https://www. kinovarobotics.com/uploads/User-Guide-Gen3-R07.pdf

    2025

  78. [86]

    Robotic 2f-85 gripper,

    “Robotic 2f-85 gripper,” 2025, (Accessed: 1st January, 2025). [Online]. Available: https://robotiq. com/products/2f85-140-adaptive-robot-gripper

    2025

  79. [87]

    Rovi wheelchair,

    “Rovi wheelchair,” 2025, (Accessed: 1st January, 2025). [Online]. Available: https://www.rovimobility.com/

    2025

  80. [88]

    Vention stand,

    “Vention stand,” 2025, (Accessed: 1st January, 2025). [Online]. Available: https://vention.io/designs/ heavy-duty-robot-pedestal-mobile-130889

    2025

  81. [89]

    Ati f/t sensor,

    “Ati f/t sensor,” 2025, (Accessed: 1st January, 2025). [Online]. Available: https://www.ati-ia.com/products/ft/ sensors.aspx

    2025

  82. [90]

    Anti slip adjustable mug holder,

    “Anti slip adjustable mug holder,” 2025, (Accessed: 1st January, 2025). [Online]. Available: https://www.amazon.com/ YOUCOX-Anti-Slip-Adjustable-Tumbler-SUNWILL/ dp/B0936XNR2C

    2025

  83. [91]

    Automatic gener- ation and detection of highly reliable fiducial markers under occlusion,

    S. Garrido-Jurado, R. Mu ˜noz-Salinas, F. J. Madrid- Cuevas, and M. J. Mar ´ın-Jim´enez, “Automatic gener- ation and detection of highly reliable fiducial markers under occlusion,” Pattern Recognition, vol. 47, no. 6, pp. 2280–2292, 2014

    2014

  84. [92]

    Intel realsense d435i,

    “Intel realsense d435i,” 2025, (Accessed: 1st January, 2025). [Online]. Available: https://www.intelrealsense. com/depth-camera-d435i/

    2025

  85. [93]

    Intel nuc 12 (nuc12wshi7),

    “Intel nuc 12 (nuc12wshi7),” 2025, (Ac- cessed: 1st January, 2025). [Online]. Available: IntelNUC12ProMiniPCKitNUC12WSHi7

    2025

  86. [94]

    Legion pro 7i gen 9 intel (16

    “Legion pro 7i gen 9 intel (16”) with rtxtm 4090 (83de000aus),” 2025, (Accessed: 1st January, 2025). [Online]. Available: https://www.lenovo. com/us/en/p/laptops/legion-laptops/legion-pro-series/ lenovo-legion-pro-7i-gen-9-16-inch-intel/83de000aus

    2025

  87. [95]

    Netgear nighthawk wifi router (rax43),

    “Netgear nighthawk wifi router (rax43),” 2025, (Accessed: 1st January, 2025). [Online]. Available: https://www.amazon.com/ NETGEAR-Nighthawk-5-Stream-Router-RAX43/dp/ B08JY6RMG9/

    2025

  88. [96]

    Glassouse press switch,

    “Glassouse press switch,” 2025, (Accessed: 1st January, 2025). [Online]. Available: https://glassouse.com/ product/press-switch-100gr-glassouse-assistive-device/

    2025

  89. [97]

    Colledanchise and P

    M. Colledanchise and P. ¨Ogren, Behavior trees in robotics and AI: An introduction. CRC Press, 2018

    2018

  90. [98]

    Gpt-4o system card,

    A. Hurst, A. Lerer, A. P. Goucher, A. Perelman, A. Ramesh, A. Clark, A. Ostrow, A. Welihinda, A. Hayes, A. Radford et al., “Gpt-4o system card,” arXiv preprint arXiv:2410.21276, 2024

    2024 arXiv

  91. [99]

    The 1998 ai planning systems competition,

    D. M. McDermott, “The 1998 ai planning systems competition,” AI magazine, vol. 21, no. 2, pp. 35–35, 2000

    1998

  92. [100]

    The fast downward planning system,

    M. Helmert, “The fast downward planning system,” Journal of Artificial Intelligence Research, vol. 26, pp. 191–246, 2006

    2006

  93. [101]

    Tracker pro 2,

    “Tracker pro 2,” 2025, (Accessed: 1st January, 2025). [Online]. Available: https://www.schoolhealth. com/tracker-pro-2

    2025

  94. [102]

    You, “Vue.js,” 2025, (Accessed: 1st January, 2025)

    E. You, “Vue.js,” 2025, (Accessed: 1st January, 2025). [Online]. Available: https://vuejs.org

    2025

  95. [103]

    Ros noetic,

    “Ros noetic,” May Accessed 2022. [Online]. Available: https://wiki.ros.org/noetic

    2022

  96. [104]

    The strands project: Long-term au- tonomy in everyday environments,

    N. Hawes, C. Burbridge, F. Jovan, L. Kunze, B. Lac- erda, L. Mudrova, J. Young, J. Wyatt, D. Hebesberger, T. Kortner et al., “The strands project: Long-term au- tonomy in everyday environments,” IEEE Robotics & Automation Magazine, vol. 24, no. 3, pp. 146–156, 2017

    2017

  97. [105]

    Tritonbot: First lessons learned from deployment of a long-term autonomy tour guide robot,

    S. Wang and H. I. Christensen, “Tritonbot: First lessons learned from deployment of a long-term autonomy tour guide robot,” in 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, 2018, pp. 158–165

    2018

  98. [106]

    Ieee p7001: A pro- posed standard on transparency,

    A. F. Winfield, S. Booth, L. A. Dennis, T. Egawa, H. Hastie, N. Jacobs, R. I. Muttram, J. I. Olszewska, F. Rajabiyazdi, A. Theodorou et al., “Ieee p7001: A pro- posed standard on transparency,” Frontiers in Robotics and AI, vol. 8, p. 665729, 2021

    2021

  99. [107]

    N. B. Reese and W. D. Bandy, Joint range of motion and muscle length testing-E-book. Elsevier Health Sciences, 2016

    2016

  100. [108]

    Robot collisions: A survey on detection, isolation, and identi- fication,

    S. Haddadin, A. De Luca, and A. Albu-Sch ¨affer, “Robot collisions: A survey on detection, isolation, and identi- fication,” IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1292–1312, 2017

    2017

  101. [109]

    Inverse kinematic solu- tions with singularity robustness for robot manipulator control,

    Y . Nakamura and H. Hanafusa, “Inverse kinematic solu- tions with singularity robustness for robot manipulator control,” Journal of dynamic systems, measurement, and control, vol. 108, no. 3, pp. 163–171, 1986

    1986

  102. [110]

    Reference values and subscale patterns for the task load index (tlx): a meta-analytic review,

    M. Hertzum, “Reference values and subscale patterns for the task load index (tlx): a meta-analytic review,” Ergonomics, vol. 64, no. 7, pp. 869–878, 2021. Appendix FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization http://emprise.cs.cornell.edu/fe...

    2021

  103. [111]

    When using a utensil, the system now automatically senses when the task is done

  104. [112]

    close mouth

    When taking a sip or wiping the face, the user needs to press a button to indicate the task is complete. Adaptability Request: Initiate transfers with open mouth gesture Adaptability Response: The robot-assisted feeding system has been updated to start transferring utensils, d...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.