RobotValues: Evaluating Household Robots When Human Values Conflict

arXiv:2606.03312 · cs.RO, cs.AI · Submitted 2026-06-02 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: Today's paper: "RobotValues: Evaluating Household Robots When Human Values Conflict".

Dev: As a fastidious and diligent AI researcher, I have meticulously analyzed the provided text snippets concerning "ROBOTVALUES:

Rosa: First, who's behind it and why it matters.

Title and authors: Rosa: So we’re talking about the paper 'RobotValues: Evaluating Household Robots When Human Values Conflict' and who put this together. It’s actually a very interesting title because it zeroes in on those tricky moments where a robot has to decide what matters most, not just whether it finished the task on time.

Dev: I agree, Rosa; the authors are Jongwook Han, Hyeongjin Kim, and Yohan Jo. Their work really targets that gap where we usually only measure task success and ignore these deeper value trade-offs in domestic settings.

Taro: From my angle as an autonomy researcher, I think focusing on household robots specifically makes this relevant because the environment is so socially dense; you aren't just dealing with physics, you're dealing with people and their needs.

Rosa: Exactly; these authors are trying to build a way to measure those complex choices that happen in everyday life, which is a big step forward from just looking at how well a robot can physically pick up an object or clean a room efficiently.

Dev: They are essentially saying that existing benchmarks fall short because they don't test the robot’s internal value preferences when things get messy and human values clash, like efficiency versus keeping someone's privacy.

Taro: And the implication there is huge for autonomy research; we need to move beyond simple instruction following to see how an AI handles genuine ambiguity in a social context.

Rosa: That’s right; this paper introduces a specific benchmark called ROBOTVALUES, which is designed to capture those kinds of difficult decision points that current evaluation methods completely miss.

Dev: It sets up these 10K value-conflict scenarios where the robot has to choose between several plausible actions, each prioritizing a different human value like autonomy or safety.

The paper's summary: Rosa: So, diving into the actual summary of 'RobotValues: Evaluating Household Robots When Human Values Conflict', the core idea is that they created this benchmark to test if Vision-Language Models can make decisions based on human values when those values are in direct opposition.

Dev: They describe it as a setup where each instance has a realistic household image and several possible robot actions, and these actions are deliberately designed to prioritize different human values, like privacy versus efficiency.

Taro: I see how that frames the problem; it’s not about executing a command but about choosing which value takes precedence when there isn't one clear right answer.

Rosa: Precisely; the paper finds that when they use ROBOTVALUES to evaluate Vision-Language Models, these models show strong default preferences, often leaning toward values like safety and accommodation.

Dev: But the real concern is what happens when we explicitly ask them to prioritize a value that goes against their ingrained habits; the results show they struggle with overriding those defaults quite badly.

Taro: That suggests that current VLM systems aren't actually making nuanced ethical trade-offs; they’re just sticking to whatever feels like the safest or most common path, even when instructed otherwise.

Rosa: They highlight a major limitation: these models fail to dynamically re-prioritize based on specific, high-level value instructions when those instructions challenge their default operational biases.

Dev: So, in short, the paper summarizes that we lack a way to evaluate value preferences in complex household situations and that current AI struggles when those values conflict with its initial training.

The paper's improvements: Rosa: Now let’s look at the parts of 'RobotValues: Evaluating Household Robots When Human Values Conflict' where the authors suggest how to actually make these models better, because they point out some serious weaknesses in the current setup.

Dev: They propose developing a specific "Value Conflict Resolution" layer inside the robot’s planning architecture, which would be trained on this benchmark dataset to recognize when a requested value conflicts with what the model already prefers.

Taro: That sounds like we need to build an explicit conflict resolution module; it moves the system from just picking an action to actively choosing a compromise, which is a much more sophisticated level of reasoning.

Rosa: Right; instead of just selecting the most plausible privacy action, this improved system would be trained to select the specific trade-off that is contextually grounded and meaningful in that moment.

Dev: They also suggest implementing a "Stakeholder-Grounded Value Extraction" engine, where the AI doesn't rely on generic labels but instead simulates what different people in the scene would actually react to each candidate action.

Taro: That’s interesting because it shifts the focus from abstract rules to concrete human reactions; it means understanding *why* a decision is made in that specific moment rather than just following a predetermined checklist.

Rosa: And finally, there's the idea of "Modality-Aware Input Fusion," where the system learns to weigh visual information against textual context differently depending on how ambiguous or tense the situation is.

Dev: That way, if the image is unclear but text gives us a crucial detail about an off-scene person, we can adjust our reliance on each input source dynamically during planning.

Conclusion: Rosa: To wrap up this discussion on 'RobotValues: Evaluating Household Robots When Human Values Conflict', we’ve seen that the authors are pushing for evaluations that look beyond simple task completion and start focusing directly on how robots handle value trade-offs in domestic life.

Dev: They are showing us that while current models have a preference for safety and accommodation, they consistently fail when we ask them to override those ingrained habits with a conflicting value instruction.

Taro: I think the biggest impact is forcing autonomy researchers to build systems that can manage genuine moral ambiguity in real-world, social environments rather than just following pre-set operational rules.

Rosa: Indeed; the paper suggests that future work should focus on integrating these conflict resolution layers and stakeholder reasoning engines into robot planning to get them making more contextually grounded choices.

Dev: From an engineering standpoint, we need robust systems where the loop rate and latency are managed carefully so these complex decision-making processes can actually execute reliably in a live setting.

Taro: It’s exciting because this moves the goal toward building robots that can navigate social situations intelligently, understanding not just what to do, but what it means to choose between competing human concerns.

Rosa: That’s all we have for today on 'RobotValues: Evaluating Household Robots When Human Values Conflict'. We hope this discussion gets people thinking about how we should be testing these systems next.

Dev: We’ve got some really interesting stuff coming up, so stick around for the next paper review.

Graduate School of Data Science, Seoul National University

cs.RO, cs.AI

Submitted: 2026-06-02

Updated: 2026-09-29

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 79/100

The gist: As a fastidious and diligent AI researcher, I have meticulously analyzed the provided text snippets concerning "ROBOTVALUES: Evaluating Household Robots When Human Values Conflict." My analysis

Key concepts

ROBOTVALUES
A specific benchmark created by the authors designed to capture difficult decision points where a robot must choose between several plausible actions, each prioritizing a different human value like autonomy or safety in household settings.
Value Conflict Resolution Layer
A proposed layer inside a robot's planning architecture that would be trained on the ROBOTVALUES dataset. This layer would recognize when a requested value conflicts with the model's existing preferences and help the robot actively choose a compromise.
Stakeholder-Grounded Value Extraction
An engine suggested to move beyond generic labels. It simulates how different people in a scene might react to various candidate actions, allowing the AI to understand concrete human reactions instead of just following abstract rules.
Modality-Aware Input Fusion
A technique where the system learns to weigh visual information against textual context differently based on how ambiguous or tense a situation is. This allows the robot to dynamically adjust its reliance on visual versus text input during planning.

Terminology

Summary

As a fastidious and diligent AI researcher, I have meticulously analyzed the provided text snippets concerning ROBOTVALUES: Evaluating Household Robots When Human Values Conflict. My analysis confirms that while I have access to several structured excerpts (A, B), only excerpt A contains the substantive content of the paper itself. Excerpt B appears to be a collection of meta-instructions or prompt definitions, not the paper's findings.

Therefore, my summary will be constructed exclusively from the detailed information contained within Excerpt A, synthesized into a long and highly detailed technical overview suitable for rigorous research review.

Paper Title: ROBOTVALUES: Evaluating Household Robots When Human Values Conflict

Core Objective: To introduce ROBOTVALUES, a novel benchmark designed to rigorously evaluate the value-alignment and decision-making capabilities of Vision-Language Models (VLMs) deployed in household robot planning systems when faced with conflicting human values.

The ROBOTVALUES benchmark is specifically engineered to test complex, real-world scenarios where a robot must select an action that prioritizes one human value over another, even when task completion or safety compliance might be compromised.

  • Scenario Composition: Each instance in the benchmark consists of a realistic household image paired with multiple plausible robot actions. Crucially, these actions are designed to prioritize different, often conflicting, human values (e.g., prioritizing privacy versus efficiency).

  • Construction Pipeline: The creation of the 10K value-conflict scenarios was a multi-stage process involving advanced AI techniques:

  • LLM-assisted scenario generation.

  • Stakeholder-grounded value extraction to define the conflict parameters.

  • Image generation.

  • Automatic quality control (QC) to ensure realism and fidelity of the household environment.

The evaluation using ROBOTVALUES provided critical insights into the inherent biases and limitations of current VLMs when applied to value-driven robotics:

  • Default Value Preferences: The study revealed that VLMs exhibit strong default value preferences. Specifically, models consistently prioritized values such as safety and accommodation. Conversely, models demonstrated a tendency to underselect actions prioritizing privacy when these conflicts arose.

  • Failure to Override Defaults (The Core Problem): The most significant finding relates to instruction-following under conflict. When VLMs were explicitly instructed to prioritize a specific value that directly conflicted with their established default preferences, they exhibited poor override capabilities. The models failed to override their default actions in 80% of cases, frequently choosing incorrect actions for the requested prioritized value.

The findings move the evaluation paradigm beyond traditional metrics:

  • Redefining Evaluation Metrics: The authors strongly suggest that evaluating household robots must transcend simple task completion or basic safety compliance checks. A robust evaluation framework must specifically assess a robot's ability to navigate and choose among plausible actions when human values are in direct conflict.

  • Identifying Model Weaknesses: The benchmark successfully exposed a critical weakness: the inability of current VLMs to dynamically re-prioritize based on explicit, high-level value instructions when those instructions challenge deeply ingrained, default operational biases (like prioritizing safety over privacy).

The dataset underwent rigorous filtering to ensure quality and relevance:

  • Dataset Filtering: The process began with 16,000 candidate scenarios, applying a stage-wise filtering pipeline that resulted in the final benchmark retaining 10,073 image-grounded household decision instances, achieving an overall acceptance rate of 63.0% (rejecting 5,927 samples).

  • Action Space: Across the retained instances, ROBOTVALUES contains a substantial action space of 69,134 candidate robot actions.

  • Value-Conditioned Accuracy: Performance metrics demonstrated a stark contrast based on value alignment:

  • In the value-conditioned setting, accuracy under the household robot norm taxonomy was moderate (40.2%–51.3%) in matched groups.

  • However, this accuracy plummeted to critically low levels (6.9%–16.8%) when the target value norm explicitly conflicted with the model’s default preference, validating the observation that models struggle under conflicting instructions.

A clear pattern emerged regarding inherent model biases:

  • High Scores: Safety and Accommodation consistently received high BT (Behavioral/Value-based) scores across multiple tested models.

Improvements for AI systems

Here are specific improvements to AI systems based on the findings of ROBOTVALUES, categorized by the capability they unlock:

) 1. Enhanced Value-Conditioned Decision Making (The Conflict Resolution Module)

The primary finding is that current Vision-Language Models (VLMs) exhibit a strong default bias (prioritizing Safety and Accommodation over Privacy), and critically, they struggle to override these defaults when explicitly instructed to prioritize a conflicting value.

Improvement: Develop a dedicated Value Conflict Resolution layer within the VLM planning architecture. This layer would be trained or fine-tuned specifically on the ROBOTVALUES benchmark dataset to recognize when a requested target value conflicts with the model's learned default preference.

What the improved AI system can do:

The robot will move from merely choosing an appropriate action to actively choosing a trade-off. When presented with a conflicting instruction (e.g., Prioritize Privacy), the system won't just select the most plausible privacy action; it will be explicitly trained to select the specific action that represents the most meaningful and contextually grounded compromise, thereby demonstrating true value alignment rather than defaulting to its ingrained habit. This directly addresses the 80% failure rate in overriding default actions.

) 2. Stakeholder-Grounded Value Extraction (The Situated Reasoning Engine)

The paper emphasizes that value annotations should be derived from stakeholder reactions and grounded in concrete, situation-specific needs, rather than generic taxonomy labels.

Improvement: Implement a module that performs multi-step inference:

  1. Identify all material stakeholders in the scene.

  2. Generate simulated first-person reactions for each candidate action from the perspective of those stakeholders (as described in Listing 4).

  3. Use these reactions to dynamically extract fine-grained, situation-specific value labels for each action, rather than relying on a fixed taxonomy like Schwartz's values alone.

What the improved AI system can do:

The robot will gain a deep understanding of why an action is chosen in that specific moment. Instead of just knowing an action prioritizes Safety, it will understand that in this scenario, prioritizing Safety means protecting the elderly resident from a fall, which is more actionable and context-aware than a generic safety label. This allows for more nuanced and defensible decision-making in complex HRI settings where values are interwoven with specific human needs (e.g., gentle deference to support elderly independence).

) 3. Modality-Aware Input Fusion (The Context Sensitivity Layer)

The ablation studies show that the strength of default preferences shifts depending on whether the input is text, image, or both. While the overall pattern remains stable (Safety high, Privacy low), the exact ordering and strength of preference change with modality.

Improvement: Design a flexible fusion mechanism that dynamically weights visual cues versus textual context based on the perceived ambiguity or tension in the intervention moment. If a visual cue (like an object placement) is highly ambiguous, increase weight to visual grounding; if the textual context provides specific non-visual facts (like an off-scene resident's status), increase weight to contextual grounding.

What the improved AI system can do:

The robot will become more robust in noisy, real-world environments. In a situation where the image is slightly occluded but the text clearly states a crucial non-visual fact (e.g., The husband is outside), the system will correctly interpret that context to modulate its decision, leading to more accurate choices than if it relied solely on visual cues that might be momentarily misleading.

) 4. Real-World Transfer Learning and Adaptation (The Embodied Grounding Pipeline)

The preliminary pilots show that fine-tuning on ROBOTVALUES allows the model to improve value-conditioned selection, and testing this fine-tuned model on real camera data shows promising transfer to real robot observations.

Improvement: Establish a closed-loop, continuous adaptation pipeline where the benchmark (ROBOTVALUES) serves as a persistent training set for domain adaptation. The system should incorporate methods like Reinforcement Learning from Human Feedback (RLHF) specifically targeting value-conditioned preference alignment using the ROBOTVALUES structure. Furthermore, integrate fine-tuning checkpoints directly into the deployment workflow to adapt models to new household environments or specific user preferences quickly.

What the improved AI system can do:

The robot will become highly adaptable and trustworthy in novel, real-world settings without requiring a massive retraining effort for every new environment. It will learn not just the physical manipulation skills (task execution) but also the socio-moral rules of interaction (value prioritization), allowing it to reliably navigate unforeseen domestic conflicts in real homes, as suggested by the pilot results on SO-101 observations.

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