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Improving User Experience in Preference-Based Optimization of Reward Functions for Assistive Robots

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read CMA-ES-IG combines an evolution strategy with an information-gain objective to generate ranking queries that improve in reward while staying easy to distinguish, and users rate it the most adaptive and preferred way to teach a robot.

desk verdict A sensible hybrid algorithm with a promising user study, but the missing bridge between sampled features and retrieved real trajectories leaves the main claim about user experience unproven. read the letter →

arxiv 2411.11182 v1 pith:P6UFL323 submitted 2024-11-17 cs.RO cs.AIcs.HCcs.LG

Nathaniel Dennler , Zhonghao Shi , Stefanos Nikolaidis , Maja Matarić This is my paper · ORCID
classification cs.ROcs.AIcs.HCcs.LG
keywords preference-basedrewardlearninghuman-robotinteractionCMA-ESinformationgainactiveuserexperienceassistiverobotstrajectoryranking
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

Preference-based robot teaching asks users to rank candidate behaviors, but the two standard query-generation strategies pull in opposite directions: information-gain selection gives options that are easy to rank yet do not visibly get better, while CMA-ES gives options that improve but become too similar to distinguish. This paper tries to establish that a hybrid, CMA-ES-IG, gets both: it samples candidates from CMA-ES's adaptive distribution and then picks the subset with the highest expected information gain. The simulations show the hybrid keeps preference-learning alignment while markedly increasing the average reward of the presented trajectories. The user study, across a physical JACO arm and a social Blossom robot, reports that participants rated CMA-ES-IG highest on ease of use and on perceived behavioral adaptation, significantly above the pure CMA-ES baseline for both and above the pure information-gain baseline for adaptation, and ranked it as their preferred method. If true, this would mean that how much the robot appears to adapt during teaching is a designable property, not a side effect, of the query-selection algorithm.

What carries the argument

Algorithm 1: sample D trajectory-feature vectors from a CMA-ES multivariate normal distribution parameterized by mean µ and covariance C; sample a set of belief trajectories from the current posterior over reward weights; select the |Q| queries that maximize expected information gain, approximated by finding |Q| medoids among the samples; collect the user's ranking; update the posterior over weights with the Bradley-Terry model; update µ and C with the standard CMA-ES rule. The combination is the carrier: CMA-ES moves the sampling distribution toward high-reward regions so presented trajectories improve, while the information-gain selection keeps the presented options far apart in feature space so users can still rank them.

What would settle it

Collect a trajectory dataset with deliberately sparse coverage of the feature space, run the same user study, and measure whether CMA-ES-IG still beats the baselines on perceived behavioral adaptation and ease of use; if its advantage disappears or reverses, the reported effect comes from the retrieval rule or dataset, not from the sampling-and-selection algorithm. Alternatively, in simulation, compute the information gain of the actual nearest-neighbor queries and compare it with the information gain of the raw CMA-ES samples: if they diverge substantially, the algorithm is not delivering the queries it claims to.

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Extended reading notes

Core claim

The paper argues that the two dominant approaches to preference-based robot teaching each sacrifice something users care about: pure information-gain (IG) query selection produces options that are easy to distinguish but do not visibly improve, while CMA-ES produces visibly improving options that become too similar to rank. CMA-ES-IG combines them by sampling trajectory features from CMA-ES's adaptive distribution and then selecting the subset with maximum expected information gain. In simulation across 8-, 16-, and 32-dimensional feature spaces, it matches or exceeds the baselines on learned-preference alignment while producing queries with substantially higher average reward. In a within-subjects study with 14 participants teaching a physical JACO arm and a social Blossom robot, CMA-ES-IG received the highest ease-of-use ratings and the highest perceived behavioral adaptation, significantly above both baselines on adaptation, and was ranked the preferred algorithm.

Load-bearing premise

The claim depends on the assumption that the real trajectory closest to a CMA-ES-sampled feature point keeps the sampled query's information gain and quality, so that what users actually rank matches what the algorithm believes it is presenting.

Editorial extensions

If this is right

  • If correct, assistive robots can be taught through rankings that users perceive as responsive, which may increase adoption and sustained engagement with personalization.
  • The approach suggests that 'query quality' (reward of presented options) and 'query informativeness' are not mutually exclusive; future active-learning objectives can include perceptual experience as an explicit optimization criterion.
  • The simulation results imply the benefit grows in higher-dimensional feature spaces, where CMA-ES-IG improves alignment while IG degrades.
  • The same combination strategy could be applied to other black-box optimization settings where humans evaluate candidates, such as haptic texture design or exoskeleton assistance, where candidate quality matters during training.
  • Since all methods took under one second per query, CMA-ES-IG is deployable in real-time interaction loops.

Reading between the lines

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

  • The user-study advantage may depend on dataset coverage: because participants rank the nearest real trajectories to the sampled feature vectors, a sparse trajectory dataset could sever the link between the CMA-ES sample and what the user actually sees; an ablation with denser versus sparser datasets would test whether the perceived adaptation comes from the algorithm or from the retrieval rule.
  • If the perceived-adaptation effect is real, it suggests that presenting visible learning progress—not just arriving at a good final policy—drives user trust; measuring trust and willingness to keep teaching over longer interactions would likely show stronger retention for CMA-ES-IG than for IG.
  • The medoid approximation trades exact information gain for tractability; a version that directly optimizes the continuous query set rather than selecting from a finite sample might further increase both alignment and quality.
  • The framework could extend beyond rankings to pairwise choices or 'approximately equal' responses, using the same query-selection principle.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes CMA-ES-IG, an algorithm that combines CMA-ES adaptive sampling with an information-gain query selection criterion for preference-based reward learning from trajectory rankings. The central claim is that CMA-ES-IG preserves the preference-learning alignment of information-gain querying while increasing the average reward of the trajectories presented to users, and that users perceive the robot as adapting without sacrificing the distinguishability of the ranking options. The authors support this with a simulated-preference study across feature spaces of dimension 8, 16, and 32, and with a within-subjects user study (n=14) in which participants ranked JACO arm handover trajectories and Blossom robot gestures under IG, CMA-ES, and CMA-ES-IG. The user study reports that CMA-ES-IG received the highest ease-of-use rating, significantly higher than CMA-ES but not significantly higher than IG, and the highest perceived behavioral adaptation, significantly higher than both baselines, and was ranked as the preferred algorithm.

Significance. If the central claim holds, the contribution is valuable to human-robot interaction and personalization: it reframes query generation as a user-experience design problem, and it is one of the few papers to combine an explicit Bayesian reward model with black-box trajectory optimization and to evaluate the combination on both physical and social robot tasks. The user study uses validated Likert scales with high internal consistency (Cronbach's alpha .89 and .97), appropriate non-parametric repeated-measures tests, and a counterbalanced within-subjects design. The authors also provide code and a hyperparameter sensitivity analysis, which strengthens reproducibility. The main risk is whether the user-study results can be attributed to the CMA-ES-IG mechanism, because the presented trajectories are retrieved from a fixed dataset rather than being the sampled feature-space queries that the simulation and algorithm description analyze.

major comments (4)
  1. [§4.1, Algorithm 1] The user study replaces the feature-space query produced by Algorithm 1 with the nearest real trajectory from a pre-collected dataset, as stated in §4.1: 'We then identified the closest trajectories in the dataset to the sampled trajectory features to present to the participant.' However, the simulation in §3.1 and the theoretical motivation in §3 evaluate information gain and quality on continuous sampled feature points, with no finite-dataset retrieval step. The paper does not report nearest-neighbor distances, the coverage of the learned autoencoder feature space, or whether the retrieved trajectories preserve the information gain, average reward, and pairwise distinctness of the intended queries. Without this, the significant user-experience results in §4.3 could be driven by the dataset construction or the retrieval rule rather than by CMA-ES-IG's sampling and IG mechanism. Please add a validation of this bridge, for example by reporting nearest-neighbor distance distributions, recomputing information gain and quality on the actually presented queries, or running the simulation with the retrieval step included.
  2. [§4.3 and Abstract] The abstract claims that users find CMA-ES-IG 'more intuitive and easier to use than previous approaches,' but the ease-of-use analysis in §4.3 only shows a significant improvement over CMA-ES (W=5.5, p=.016); the comparison with IG (M=5.50 vs 5.13) is described as 'empirically easier' without a reported significance test. Since IG is one of the previous approaches, the stronger claim in the abstract and conclusion is not supported by the reported statistics. Please report the IG comparison p-value or qualify the claim.
  3. [§4.3, Figs. 6–8] With n=14 and multiple pairwise repeated-measures tests across ease of use, behavioral adaptation, and overall ranking, the p-values are reported without correction for multiple comparisons. For example, the behavioral-adaptation comparison against CMA-ES (p=.033) would not survive a Holm-Bonferroni correction over six tests. This does not invalidate the results, but the paper should report adjusted p-values or explicitly frame the comparisons as exploratory, especially given the small sample size.
  4. [§3.1, Table 1 and Fig. 3] The quality metric used in the simulation is the average reward of the presented trajectories, which is precisely the quantity that CMA-ES and CMA-ES-IG are designed to increase. Consequently, the quality results do not independently establish that users perceive higher appeal, and the Fig. 3 caption's claim that 'CMA-ES-IG performing significantly better' is not supported by the reported statistics, since no tests, effect sizes, or confidence intervals are given for the simulated AUC comparisons. Please provide inferential statistics for the simulated comparisons or soften the significance claim.
minor comments (6)
  1. [§3, Algorithm 1] The symbol D is used both for the trajectory dataset and for the number of samples drawn from the CMA-ES distribution; please rename one of them for clarity.
  2. [§2] The text cites 'Habiban et al.' but the reference list gives the correct spelling 'Habibian'; please correct the in-text citation.
  3. [§4.4] The conclusion contains a typo: 'prefered' should be 'preferred.'
  4. [§4.2] Each algorithm was paired with a specific handover object or gesture emotion within a domain; although the algorithm order was counterbalanced, please clarify how the assignment of algorithms to tasks was randomized and whether any task-specific effects were examined.
  5. [§4.1] The sample count D for the CMA-ES sampling and the posterior belief, as well as several autoencoder and CMA-ES hyperparameters beyond the initial step size, are not reported; please list them in the appendix or supplement.
  6. [§3, Equation (3)] The information-gain objective is approximated by medoid selection, but the exact procedure used for the IG baseline is not specified; please state how the IG baseline generates its candidate set and whether it samples uniformly from the feature space.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; CMA-ES-IG is a stated composition of external baselines and the headline user-experience claims rest on an independent user study.

full rationale

This paper does not exhibit definitional circularity. Algorithm 1 is an explicit composition of two externally published components: information-gain query selection (Eq. 3, from Bıyık et al.) and CMA-ES distribution updates (Hansen et al.), with no parameter fitted to the outcome metrics. The simulation's quality metric (average true reward of presented trajectories) is indeed the quantity that the CMA-ES component is designed to increase, so the favorable quality comparison in Table 1 is partly a sanity check of the design intent rather than an independent confirmation; however, it is not forced by construction because the algorithm optimizes an estimated reward under a Bradley-Terry belief, while quality is evaluated under the simulated true weight vector, and the added information-gain constraint could in principle reduce quality. The user-experience results (EOU, behavioral adaptation, preference ranking) come from a within-subjects user study with 14 participants and standard nonparametric tests, independent of the algorithm's equations. The main threat to the paper's bridge is the nearest-neighbor retrieval step in Section 4.1, which replaces sampled feature points with closest dataset trajectories; that is a fidelity/validity gap, not a circularity, because it does not make the measured outcome equal to the algorithm's input by definition. No load-bearing self-citation or imported uniqueness theorem is used.

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

The paper's central claim is empirical, so its ledger is dominated by behavioral modeling assumptions and unreported implementation parameters rather than mathematical axioms. Key inputs are the linear reward assumption, the Bradley-Terry choice model, learned autoencoder features, and the nearest-trajectory retrieval step. The most consequential free parameters are the CMA-ES step size, the Bradley-Terry rationality beta, the sample counts, and the feature dimensions. No new physical or conceptual entities are introduced.

free parameters (4)
  • CMA-ES initial step size sigma = 0.5
    Default step size; authors state it was not tuned, but it affects sampling spread and query quality in both simulation and user study.
  • Bradley-Terry rationality parameter beta = not reported
    Equation 1 includes beta; the simulation uses this model for user rankings, but the value is not stated, affecting simulated user behavior and posterior updates.
  • Number of samples D for CMA-ES and posterior belief = not reported
    Algorithm 1 samples D features from the belief and D from CMA-ES, but the paper does not give these values or the medoid selection details.
  • Autoencoder feature dimensions = 6 (Blossom), 4 (JACO)
    Hyperparameters tuned to reconstruction loss; the low-dimensional feature space defines both information gain and CMA-ES quality, so the results depend on this learned representation.
assumptions (5)
  • domain assumption User preference is linear in trajectory features: R(ξ) = ω^T Φ(ξ)
    Section 3 preliminaries; standard IRL assumption, but user preferences may be nonlinear.
  • domain assumption User rankings follow the Bradley-Terry model with conditional independence
    Section 3 Equations 1 and 2; used to update the posterior and to simulate users. If humans violate this choice model, both simulation and inference are biased.
  • ad hoc to paper The pre-collected trajectory datasets are diverse enough that nearest-neighbor retrieval preserves the desirable properties of sampled feature queries
    Section 4.1; the user study presents nearest dataset trajectories to sampled features, but incomplete coverage would break the link between the algorithm objective and what users actually see.
  • domain assumption Autoencoder features capture the dimensions users care about
    Section 4.1; features are learned by reconstruction loss, not by preference data, so they may omit preference-relevant variation.
  • domain assumption User preferences are stationary during the five-minute interaction
    The posterior update assumes a fixed ground-truth ω across rankings; users may change preferences or fatigue during the session.

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Pith. "Pith review of Improving User Experience in Preference-Based Optimization of Reward Functions for Assistive Robots." pith.science (2026). https://pith.science/paper/P6UFL323

@misc{pith2026241111182,
  author       = {Pith},
  title        = {Pith review of: Improving User Experience in Preference-Based Optimization of Reward Functions for Assistive Robots},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P6UFL323}},
  note         = {Machine review of arXiv:2411.11182}
}
read the original abstract

Assistive robots interact with humans and must adapt to different users' preferences to be effective. An easy and effective technique to learn non-expert users' preferences is through rankings of robot behaviors, for example, robot movement trajectories or gestures. Existing techniques focus on generating trajectories for users to rank that maximize the outcome of the preference learning process. However, the generated trajectories do not appear to reflect the user's preference over repeated interactions. In this work, we design an algorithm to generate trajectories for users to rank that we call Covariance Matrix Adaptation Evolution Strategies with Information Gain (CMA-ES-IG). CMA-ES-IG prioritizes the user's experience of the preference learning process. We show that users find our algorithm more intuitive and easier to use than previous approaches across both physical and social robot tasks. This project's code is hosted at github.com/interaction-lab/CMA-ES-IG

Figures

Figures reproduced from arXiv: 2411.11182 by the authors.

Figure 1
Figure 1. The two domains that users taught robots their preferences for the robot’s behaviors. In the physical domain, users ranked a JACO arm’s movement trajectories to hand them a marker, a cup, and a spoon. In the social domain, users ranked a Blossom robot’s gestures to portray happiness, sadness, and anger. reward function [10], or (2) using rankings to implicitly infer a user’s reward function through black-box optimiz… view at source ↗
Figure 2
Figure 2. Example queries generated from an early step of each algorithm. The large circle represents the space of all trajectories with lighter areas representing higher reward, light blue arrows representing the user’s true preference, dark blue arrows representing the current estimate of the user’s preference, orange circles representing sampled trajectories to present to the user, and green dotted regions representing the… view at source ↗
Figure 3
Figure 3. Comparison of simulation results for learning user preferences. Shaded regions indicate standard error. We found that all methods were able to learn user preferences across varying dimensions. The quality of the trajectories in the query increases only for CMA-ES and CMA-ES-IG, with CMA-ES-IG performing significantly better. We compared the three algorithms: Information Gain (IG), CMA-ES, and CMA-ES-IG. For the alig… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: The framework for learning user preferences. We learned nonlinear features for sets of robot trajectories. The query sampler produced sets of trajectories for the user to rank and those rankings were used to update the estimate of the user’s preferences. From the gener…
Figure 5
Figure 5. Figure 5: User study setup. Users interacted with the robots through the ranking interface to specify their preferences for how the Blossom robot used gestures to signal different affective states and how the JACO robot arm handed them different items. Participants were randomly…
Figure 6
Figure 6. Figure 6: Ease of Use results. CMA-ES-IG was rated significantly easier to use than CMA-ES, and empirically easier than IG. Ease of Use. We evaluated the aver￾age scores for EOU from a four-item Likert scale. We identified high in￾ternal consistency of the scale, with a Cronbach…
Figure 7
Figure 7. Figure 7: Behavioral Adaptation results. CMA-ES-IG was rated as changing the robot’s behavior significantly more over time than both CMA-ES and IG. Perceived Behavioral Adapta￾tion. We evaluated the average scores for BA from a four-item Likert scale, and identified high interna…
Figure 8
Figure 8. Figure 8: Algorithmic ranking results. CMA￾ES-IG was consistently ranked as the most preferred algorithm for teaching robots preferences in our user study. Overall Ranking. Finally, we eval￾uated the ranking of each algorithm based on how the participants ranked the algorithms a…
Figure 9
Figure 9. Figure 9: Initial step-size sensitivity. We show the AUC of the labeled metric (corre￾sponding to the values in [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]

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Forward citations

Cited by 1 Pith paper

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.