The ethics of human-robot interaction (HRI) have been discussed extensively based on three traditional frameworks: deontology, consequentialism, and virtue ethics. We conducted a mixed within/between experiment to investigate Sparrow's proposed ethical asymmetry hypothesis in human treatment of robots. The moral permissibility of action (MPA) was manipulated as a subject grouping variable, and virtue type (prudence, justice, courage, and temperance) was controlled as a within-subjects factor. We tested moral stimuli using an online questionnaire with Perceived Moral Permissibility of Action (PMPA) and Perceived Virtue Scores (PVS) as response measures. The PVS measure was based on an adaptation of the established Questionnaire on Cardinal Virtues (QCV), while the PMPA was based on Malle et al. [39] work. We found that the MPA significantly influenced the PMPA and perceived virtue scores. The best-fitting model to describe the relationship between PMPA and PVS was cubic, which is symmetrical in nature. Our study did not confirm Sparrow's asymmetry hypothesis. The adaptation of the QCV is expected to have utility for future studies, pending additional psychometric property assessments.
Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.
Mask2Real-WM is presented, a two-stage action-conditioned world model for dexterous manipulation that decouples pixel prediction into a dynamics model and a rendering model that shows that mask conditioning and simulation pretraining are both required for per-DoF action controllability across all 23 degrees of freedom.
Riccardo Feingold, Davide Liconti, Chenyu Yang et al.· 1 citation
This work proposes COMPACT-VA, a planning-aligned working memory framework built on conditional VQ-VAE, compressing extended context into bounded representations, and evaluates on high-signal dynamic scenarios where historical context is most critical for behavior correctness, and accordingly design behavioral metrics.
Zhixuan Liang, Yuxiao Chen, Yurong You et al.· arXiv.org· 1 citation
Shielding has emerged as a promising approach for ensuring safety of AI-controlled autonomous systems. The algorithmic goal is to compute a shield, which is a runtime safety enforcement tool that needs to monitor and intervene the AI controller's actions if safety could be compromised otherwise. Traditional shields are designed statically for a specific safety requirement. Therefore, if the safety requirement changes at runtime due to changing operating conditions, the shield needs to be recomputed from scratch, causing delays that could be fatal. We introduce dynamic shields for parametric safety specifications, which are succinctly represented sets of all possible safety specifications that may be encountered at runtime. Our dynamic shields are statically designed for a given safety parameter set, and are able to dynamically adapt as the true safety specification (permissible by the parameters) is revealed at runtime. The main algorithmic novelty lies in the dynamic adaptation procedure, which is a simple and fast algorithm that utilizes known features of standard safety shields, like maximal permissiveness. We report experimental results for a robot navigation problem in unknown territories, where the safety specification evolves as new obstacles are discovered at runtime. In our experiments, the dynamic shields took a few minutes for their offline design, and took between a fraction of a second and a few seconds for online adaptation at each step, whereas the brute-force online recomputation approach was up to 5 times slower.
Davide Corsi, Kaushik Mallik, Andoni Rodriguez et al.· 0 citations
Instant Episode Repetition (IER) is introduced, a simple and novel mechanism that improves sample efficiency by immediately repeating action sequences from successful episodes during environment interaction by directly influences the data collection process.
Hoda Yamani, Yuning Xing, Koen van Rijnsoever et al.· 0 citations
Existing articulatory corpora based on real-time MRI and electromagnetic articulography capture tongue motion but lack traceable labels for the muscle-driven process underlying each configuration. We introduce a simulator-grounded construction framework, instantiated as 3DTongueQA. Controlled 11-D muscle activations are mapped by the ArtiSynth Badin finite-element model to fixed-topology tongue meshes, converted into reusable biomechanical records, and rendered as deterministic QA on simulator-defined muscle state, geometry, and target-directed change. From 295,157 screened configurations, 295,115 valid meshes yield 891,156 QA records per language. English and Korean naturalizations modify only surface form and are verified against the source records. A swappable SpiralNet++--Qwen3-8B baseline reaches $62.9\pm9.2$ Muscle EM, dropping to 2.2 under mesh shuffling; anchor-held-out training retains 80.4--98.6\% of full-inventory scores, while task-specific readouts reach $88.7\pm0.7$ Muscle EM and $93.3\pm1.0$ Direction EM. The results show geometry-grounded supervision supporting both unified language QA and structured prediction. Code is available at https://github.com/esh0504/muscle-grounded-qa .
