Jess+: designing embodied AI for interactive music-making

Craig Vear; Johann Benerradi

REVIEW 5 major objections 6 minor 49 references

Jess+: designing embodied AI for interactive music-making

T0 review · 5 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A robotic arm guided by embodied AI served as a digital score that let a mixed-ability ensemble improvise together as co-creators.

desk verdict A genuinely novel integrated system and a careful design report, but the central causal claim outruns the evidence, and the deployment pipeline has a train/serve skew the authors don't address. read the letter →

arxiv 2412.06469 v1 pith:XDPVRHWM submitted 2024-12-09 cs.HC

Craig Vear , Johann Benerradi This is my paper · ORCID

classification cs.HC

keywords embodiedAIinclusivemusic-makingdigitalscorehuman-robotinteractionbrain-computermusicinterfacemusickingroboticarmco-creativeimprovisation

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 argues that an embodied-AI system called Jess+, in which a robotic arm is driven by neural networks trained on recordings of pianists improvising, can function as an intelligent digital score for a mixed ensemble of disabled and non-disabled musicians. The claim is that the design decisions—a modular AI Factory, a gesture manager, and an embedded belief system—led to deeply engaged experiences for the musicians and transformed their practice as an inclusive ensemble. The musicians reported being in-the-loop and perceiving the robot as a co-creative partner; the disabled musician described it as an extension of herself, while the others saw it as a creative accompanist. The authors are careful to say they cannot explain with certainty how the system produced these transformations, only that embedding music-specific embodied interaction data into each design layer contributed to them.

What carries the argument

The load-bearing mechanism is the closed-loop interaction design with a four-layer modular architecture. Layer 1 formats audio, EEG, EDA, and robot-arm positions; Layer 2, the AI Factory, runs seven hourglass-shaped convolutional neural networks that predict one feature stream from another, such as audio-to-flow, EEG-to-flow, and flow-to-core; Layer 3, the gesture manager, selects among nine streams (seven model outputs, live audio amplitude, and a random poetry stream) and maps their values to low, medium, or high responses; Layer 4, the belief system, converts those values into a predefined gestural language of shapes and graphic-score-inspired movements with randomly varied speed, size, and other performance parameters. The gesture manager also has a startled response that interrupts a gesture phrase when live sound exceeds a threshold, creating a two-way interaction where the robot both dances to the musicians and conducts them.

What would settle it

Play the same four improvisation pieces with the gesture manager fed genuine live sensor streams versus the same streams shuffled or replaced by random numbers while keeping the gesture library and thresholds identical; if the musicians cannot distinguish the two conditions, or their reports of co-creation do not change, the central claim of meaningful embodied-AI coupling is refuted.

Watch

Extended reading notes

Core claim

The central discovery is that a robot arm can serve as a co-creative digital score in live improvisation when its behaviour is coupled to a closed loop of sound, physiological signals, and a library of gestures. Jess+ senses the ensemble's audio plus EEG and electrodermal activity from the disabled musician, feeds these through seven small neural networks trained on an Embodied Musicking Dataset of eight pianists, and converts their outputs into thought trains that select movements of a pen- or feather-wielding robotic arm. The musicians reported back-and-forth interaction in real time, and a public sharing performance demonstrated the system working with a live audience. The authors assert that embedding music-specific embodied interaction data and behaviours into every design layer contributed to the musicians' transformational encounters, though the exact causal pathway remains unknown.

Load-bearing premise

The central assumption is that the seven neural networks, trained on eight pianists improvising to a jazz backing track, still produce meaningful real-time predictions when fed the live audio, EEG, EDA, and robot-arm positions of a different trio improvising freely; if those predictions are meaningless, the robot's behaviour reduces to loudness-triggered random gesture selection.

Editorial extensions

If this is right

If the embodied-AI claim holds, disabled musicians can participate in live improvisation as full co-creators rather than being limited by the interface barriers of traditional instruments.
A robotic arm using physiological and audio sensing can act as a non-judgemental 'third space' that reduces the psychological pressure of human-to-human improvisation, as the musicians reported.
The modular, subsumption-inspired architecture allows individual components—sensors, models, gesture library—to be replaced or updated without rebuilding the whole system, supporting iterative co-design with musicians.
The open-source release of Jess+ makes the system reproducible for other inclusive music-making projects.
The closed-loop design, including the startled response, offers a concrete model for how an embodied agent can alternate between following and leading in a collaborative improvisation.

Reading between the lines

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

Beyond the paper's claims, the system's design suggests a general template for embodied co-creative agents: a perception layer connected to a factory of simple predictors, a stochastic selector, and a curated expressive language, a template that could transfer to other art forms such as dance or theatre.
The paper leaves open whether the neural networks' predictions are the active ingredient. A direct test would be to run the same workshops with the seven model streams replaced by random values while keeping the gesture library and thresholds; if the musicians' experience is unchanged, the 'AI' contribution would be shown to be decorative.
The startled response and the 0.1/0.7 thresholds introduce a turn-taking and interruption mechanism that could be studied as a form of human-robot coordination, potentially informing non-musical assistive and collaborative robotics.
The Embodied Musicking Dataset, collected from only eight pianists improvising to a jazz backing track, is a narrow basis for a system used in free improvisation; collecting a more diverse dataset and validating model predictions on the deployment context would be a natural next step the paper does not take.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Craig, here's my read of Vear & Benerradi's Jess+ paper.

What's new and worth taking seriously: the integration itself. A robotic arm as a digital score, driven by seven parallel encoder-decoder models trained on an 'embodied musicking' dataset, plus EEG/EDA from the disabled musician, plus a belief-system gesture manager — that combination doesn't appear in the cited prior art. The paper gives enough architectural detail to reproduce the system, and the code is open source under BSD-2. That's real, formal, reproducible work, and I'd cite it if I were working on accessible music technology or HRI for co-creativity.

The qualitative material is also handled carefully. The authors quote the musicians, distinguish between the disabled and non-disabled musicians' relationships with the robot, and repeatedly hedge that they can't say with certainty how the design led to the reported transformations. That honesty is to their credit, and the testimonials are plausible as descriptions of rich experiences.

Where it gets soft — and this is where I think the stress-test note is right, not wrong: the deployment of the AI Factory is not faithful to the training setup. The models were trained on min-max scaling computed across the whole training set; at deployment they normalize each 5-second buffer independently. That's a textbook train/serve skew, and it means the audio envelope and EDA inputs at deployment are not the same quantities the models learned to predict from. On top of that, models 2 and 3 were trained on human core mid-shoulder coordinates, but at deployment they're fed robot-arm tip positions with no reported coordinate alignment or calibration. Those predictions aren't about the musicians' bodies. Since the gesture manager tosses a coin between nine streams anyway (audio at 36%, the rest equal), the robot's behavior is largely random or audio-threshold-driven. The claim that the embodied-AI approach 'did contribute' to the transformations therefore overreaches: the system as a whole contributed, but the specific coupling via the neural models isn't demonstrated.

Is this fatal? Not for the paper as a design report. But it is load-bearing for the conclusion. The fix is straightforward in principle: an offline validation of the seven models on held-out data, or a clear reframing that treats the AI Factory as an aesthetic component whose predictive validity wasn't assessed. The co-design circularity — musicians helped shape the belief system and gesture language, then their positive feedback is cited as evidence — is a minor additional concern, not the main problem.

Who's this for? Researchers in accessible music tech and human-robot co-creativity. It deserves a serious referee — the system is novel and the reporting is honest — but the referee should demand either the validation or a humbler conclusion.

Referee Report

5 major / 6 minor

Summary. This paper presents Jess+, an intelligent digital score system that uses a robotic arm as an embodied AI co-creative partner for a mixed-ability ensemble. The system combines audio, EEG, and EDA sensing with an 'AI Factory' of seven neural networks trained on an 'Embodied Musicking Dataset' of eight pianists improvising to a jazz backing track. The networks predict flow, body position, and audio features from one another; a gesture manager selects among the resulting 'thought-train' streams and a belief system maps them to robotic gestures. The authors report qualitative findings from four iterative workshops and a final sharing performance with three musicians (including one disabled musician), and they claim in the abstract and conclusion that the implemented design decisions and embodied-AI approach led to rich experiences that transformed the musicians' practice. The paper also states, in the Discussion, that the authors 'can not say with any certainty how they led to such transformations encounters for the musicians' and that a deep dive into dataset correlations reveals 'extremely loose causality.' The paper is positioned as a companion to a CHI user-experience paper and aims to provide the technical details necessary for reproducibility.

Significance. If the central causal claim were supported, Jess+ would be a notable contribution to inclusive music technology and human-robot co-creativity, integrating physiological sensing, multiple neural networks, and a robotic arm in a modular, open-source system, and reporting positive experiences from a real mixed-ability ensemble. The paper's strengths include its clear architectural description, the inclusion of an open-source implementation, and its honest acknowledgment of uncertainty in parts of the Discussion. However, the significance is currently undercut by the lack of validation of the deployed ML pipeline: the training and deployment normalization procedures differ, and the robot arm position is used as a proxy for human body position without calibration. Because the gesture manager mixes model outputs with random and audio-threshold streams, the robot's behavior may be effectively decoupled from the AI predictions, weakening the attribution of the musicians' experiences to the embodied-AI design. The qualitative testimonials support a modest claim about rich user experience, but they do not, as presented, support the causal claim made in the abstract and conclusion.

major comments (5)

[Deployment (near Figure 4)] The deployed models are not fed the same input distribution that the models were trained on. The Training section states that 'Data was normalised with min-max feature scaling by computing the minimums and maximums for each channel of each feature across the training set,' while the Deployment section states that features 'are normalised in real-time across the 5-sec buffer (as opposed to the whole training set).' This train/serve skew means that every live buffer is rescaled to [0,1] independently, destroying absolute amplitude information for the audio envelope and EDA, and can produce out-of-distribution inputs whenever the live buffer extrema differ from the training extrema. The model outputs used to drive the robot are therefore not the quantities the models were trained to estimate. The paper needs to provide evidence that the deployed pipeline produces meaningful predictions (for example, by evaluating deployment normalization on held-out data or comparing predictions against random baselines), or the causal claim that the AI Factory's output drove the musicians' experience must be withdrawn.
[Deployment, models 3 and 6 (Figure 4)] Models 2, 3, and 6 were trained on the x and y positions of the human core mid-shoulder point, but in deployment model 3 is fed 'the x and y positions of the robot arm tip' as a 'robotic representation of the core mid-shoulder position.' No coordinate alignment, calibration, or reference-frame mapping is reported; the robot tip is in a different physical space (pen on a table or drawing board, arm mounted on a pallet) from the musician's shoulders. Feeding this substitution into model 3, and using the predicted core as a target for model 6, yields predictions that are not about the musicians' bodies. This is a load-bearing gap for the 'self-awareness' stream (items c and f in Figure 4), and it must be either validated or removed from the causal account.
[Training and Figure 3] The paper validates the seven models only through training/validation loss curves (Figure 3 shows MSE loss for two models) and manual hyperparameter selection. There is no evaluation on held-out data in terms of prediction quality (e.g., correlation, R-squared, classification accuracy), no comparison to trivial baselines, and no per-model analysis. Since the gesture manager selects among the seven model streams plus audio and random streams (Gesture manager section, with the audio stream given 36% probability and the other streams equally probable), and applies 0.1/0.7 thresholds, it is plausible that the robot's behavior is dominated by the audio-threshold 'startle' and random choice rather than by the neural-network predictions. Without task-relevant validation of the deployed models, the paper's assertion that the AI Factory 'seems to be key' (Discussion) is unsupported.
[User centred design and Results] The causal attribution in the abstract and conclusion ('the implemented design decisions and embodied-AI approach led to rich experiences... transformed their practice') is not supported by the study design. The three musicians co-designed the belief system and gesture language during four iterative workshops, so their positive testimonials may reflect ownership, novelty, and the collaborative development process rather than the specific embodied-AI mechanisms. There is no control condition (e.g., a robot with scripted or random gestures) and no comparison to other digital-score systems. The authors themselves acknowledge in the Discussion that 'we can not say with any certainty how they led to such transformations encounters for the musicians' and that the dataset correlations show 'extremely loose causality.' The abstract and conclusion should be aligned with this more modest evidence; alternatively, the paper should present the qualitative findings as a design case study rather than as confirmation of the causal claim.
[Dataset and Deployment] The AI Factory models are trained on eight pianists improvising to a jazz backing track (Dataset), but are deployed with a trio of musicians (Ableton, violin, cello) engaged in free improvisation with no backing track (Results). The modal and stylistic shift is large, and the paper provides no analysis of whether the learned correlations between body movement, physiological response, and audio envelope transfer to this context. This generalizability gap compounds the train/serve skew identified above, and it should be addressed explicitly, for example by reporting how the predicted streams behave on live data or by adding a domain-adaptation discussion.

minor comments (6)

[Layer 3 - gesture manager] The phrase 'holds this stream for a few sections' should read 'holds this stream for a few seconds'; the intended time unit is clear from the later 'gesture phrases of 3 to 8 seconds.'
[Deployment, item (g)] In the Deployment list, 'predicted flow from d) if fed into model 7)' should be 'predicted flow from d) is fed into model 7)'; the same typo appears in the Features/Models list where model 7 is described.
[Results and Discussion] The musician's name is spelled 'Clare' in the Results section but 'Claire' in the Discussion when quoting her; please make the spelling consistent.
[Introduction and Conclusion] The sentence 'we can not say with any certainty how they led to such transformations encounters for the musicians' is grammatically incomplete; 'transformations encounters' appears to be a typo for 'transformational encounters.'
[Interaction loop and modular design] Several cross-references are empty: 'described in )' and 'discussed in )' in the 'Interaction loop' section, and the reference to Figure 2 appears as a placeholder. Please fill in the figure and section numbers before submission.
[Gesture manager] In 'Every 0.5 to 2 seconds (selected randomly) which represent a rhythmic loop,' the verb 'represent' should agree with the singular subject 'which'; use 'represents.'

Circularity Check

1 steps flagged · score 3.0 of 10

Mild participatory-design circularity: musicians' preferences were built into the system and then used as evidence that the design caused their rich experiences; the technical AI claims are under-validated but not circular.

other [User centred design section; Summary of the findings]
"Their feedback was critical and was taken into account through each testing session following a user-centred design for improving the system at each new iteration. ... Our findings showed that the design decisions that were implemented through the embodied AI approach led to rich experiences for the musicians which in turn transformed their practice and creative engagement as an inclusive ensemble."

The system was iteratively fitted to the musicians' expressed desires: feedback was 'taken into account through each testing session' and the belief system and aesthetic were built 'through their experiences and desires.' The same musicians' subsequent positive accounts then serve as the evidence that the 'design decisions ... led to rich experiences.' Because the evaluators helped design the evaluated system, the positive outcome is partly manufactured by the development loop rather than independently validated. The paper's own caveat ('we can not say with any certainty how they led to such transformations') concedes the causal link is unexplained. This is mild participatory/UX circularity, not equation-level circularity.

full rationale

The paper contains no first-principles mathematical derivation, so the classic circularity patterns (self-definitional equations, fitted parameters renamed as predictions, uniqueness theorems, ansatz-by-citation) do not directly apply. The AI Factory is a set of seven separately trained neural networks; calling the robot-arm tip position a 'robotic representation of the core mid-shoulder position' and normalizing live buffers differently from training are validity and domain-shift concerns, not circularity. The self-citations (Vear 2019, 2021, 2022) supply definitions and framing but are not load-bearing in forcing the empirical result. The only notable circularity is the participatory-design evaluation loop: the musicians' preferences were embedded in the system during iterative workshops, and their positive feedback is then cited as evidence of success. The paper explicitly disclaims causal certainty, which keeps the circularity mild. Accordingly, the score is 3 rather than higher.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central claim depends on the trained AI Factory transferring to the live trio, on the hand-set gesture-manager parameters, and on the validity of flow self-reports as ground truth. These are not independently established.

free parameters (6)

Model hyperparameters (learning rates, batch sizes) = Learning rates 5e-5 and 1e-5, batch sizes 32 and 16, epochs unspecified
Manually optimized per Figure 3; no exhaustive search or final values reported for all seven models.
Probability of listening to audio stream = 0.36
Hand-set in the gesture manager: audio stream selected with 36% chance, other 8 streams with equal probability.
Affect-level thresholds for response tiers = 0.1 and 0.7
Hand-set boundaries for low, medium, and high robot responses in the gesture manager.
Gesture phrase duration = 3 to 8 seconds
Randomly selected range for each gesture phrase, part of the belief system.
Rhythmic loop interval = 0.5 to 2 seconds
Randomly selected interval for gesture decisions, part of the belief system.
Real-time normalization window = 5-second buffer
Live features are min-max normalized across the 5-second buffer rather than the training set, changing the model input distribution from what was trained.

assumptions (5)

domain assumption The continuous self-reported 'flow' rating during video replay is a valid measure of embodied musical engagement.
Used as ground truth for training models 1, 3, 4, and 5; no validation of the rating scale's reliability or consistency.
ad hoc to paper Robot arm tip position can substitute for the human core mid-shoulder position as input to models 3 and 6.
Deployment replaces the dataset's core position with the robot arm's x/y position without any calibration or validation.
domain assumption Models trained on eight pianists improvising to a jazz backing track generalize to the Jess+ trio's live improvisational context.
The Embodied Musicking Dataset is from 2020 with different musicians, instruments, and musical style; no fine-tuning on the target ensemble.
domain assumption The definition of embodied AI from Vear (2022) and Small's musicking are accepted as the theoretical foundation.
The paper builds on these definitions without empirical comparison to alternative frameworks.
domain assumption The fixed gesture language (shapes, Cardew and Wolff inspired movements) is meaningful to musicians as a score.
The system assumes the gesture library communicates musical ideas to the musicians.

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Cite this review

Pith. "Pith review of Jess+: designing embodied AI for interactive music-making." pith.science (2026). https://pith.science/paper/XDPVRHWM

@misc{pith2026241206469,
  author       = {Pith},
  title        = {Pith review of: Jess+: designing embodied AI for interactive music-making},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XDPVRHWM}},
  note         = {Machine review of arXiv:2412.06469}
}

read the original abstract

In this paper, we discuss the conceptualisation and design of embodied AI within an inclusive music-making project. The central case study is Jess+ an intelligent digital score system for shared creativity with a mixed ensemble of non-disabled and disabled musicians. The overarching aim is that the digital score enables disabled musicians to thrive in a live music conversation with other musicians regardless of the potential barriers of disability and music-making. After defining what we mean by embodied AI and how this approach supports the aims of the Jess+ project, we outline the main design features of the system. This includes several novel approaches such as its modular design, an AI Factory based on an embodied musicking dataset, and an embedded belief system. Our findings showed that the implemented design decisions and embodied-AI approach led to rich experiences for the musicians which in turn transformed their practice as an inclusive ensemble.

Figures

Figures reproduced from arXiv: 2412.06469 by the authors.

**Figure 1.** Example of notation Basic description of the system Jess+ is an intelligent digital score system that uses AI and a robotic arm to amplify and communicate the creativity of an inclusive ensemble. The role of the arm is to present movements and gestures that inspire the musicians to make a sound and to co-create music through improvisation. The role of the AI is to sense the humans and to generate a response via the … view at source ↗

**Figure 2.** Interaction design (Vear et al. 2024) Modular design: how does it work? Jess+ was built on a modular system comprising high-level layers (also outlined in (Vear et al. 2024)): • Layer 1 - percept input and formatting. This module manages and formats all the real-time input data and sends it to the AI factory and gesture manager for processing. • Layer 2 - AI Factory. This module generates streams of data from the 7… view at source ↗

**Figure 3.** AI Factory validation loss (mean squared error) [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: AI Factory (Vear et al. 2024) • the audio envelope is extracted from the microphone input buffer using a Hilbert transform, downsampled to 10 Hz to match the rest of the data (1 channel); • the x and y positions of the robot arm tip (2 channels), robotic representatio…

**Figure 5.** Figure 5: Photos of the final performance lighted how the system operated with them inside musicmaking, and how it inspired and offered appropriate musical gestures for them to interpret through the flow of the music-making. However, each musician had built a different relati…

Discussion (0). Continue with ORCID to comment.

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

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all...
[49]

write newline

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

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

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