arXiv AI
Jun 19
Co-policy: Responsive Human-Robot Co-Creation for Musical Performances
arXiv:2606. 19914v1 Announce Type: cross Abstract: Art has long stood as a pivotal expression of human creativity.
Robotics and embodied AI
Manipulation, locomotion, sim-to-real transfer and autonomous driving: learning systems that have to survive physics.
Hugging Face Trending Papers
Jun 18
HumanScale: Egocentric Human Video Can Outperform Real-Robot Data for Embodied Pretraining
Embodied foundation models are expected to benefit from data scaling like large language models, but face a much tighter data bottleneck. Teleoperated real-robot trajectories remain the dominant pretraining source due to their precise action supervision and embodiment alignment, yet their scalability is limited by high collection cost, acquisition difficulty, and low behavioral and environmental diversity.
Hugging Face Trending Papers
Jun 18
See-and-Reach: Precise Vision-Language Navigation for UAVs within the Field of View
UAV Vision-Language Navigation (UAV-VLN) is typically formulated as a holistic search-and-reach problem, where long-range target discovery and final target approach are optimized and evaluated jointly. This formulation makes it difficult to assess a critical capability of aerial embodied agents, namely whether a UAV can accurately ground a visible target and translate vision-language evidence into precise 3D motion once the target enters its field of view.
arXiv AI
Jun 18
Leveraging Energy Features for Surface Classification with Deep Learning: A Comparative Analysis Across Three Independent Datasets
arXiv:2606. 18698v1 Announce Type: cross Abstract: The energy-based method remains a comparatively underexamined approach for surface classification in mobile robotics, despite promising results in constrained environments.
arXiv AI
Jun 18
A Variational Framework for LLM Generator-Regulator Games
arXiv:2606. 18424v1 Announce Type: cross Abstract: This paper develops a variational framework for regulated language generation.
arXiv AI
Jun 18
R2D-RL: A RoboCup 2D Soccer Environment for Multi-Agent Reinforcement Learning
arXiv:2606. 18786v1 Announce Type: new Abstract: Robot soccer is a challenging testbed for multi-agent reinforcement learning because it combines partial observability, cooperative and adversarial interaction, sparse rewards, and long-horizon tactical behavior.
arXiv AI
Jun 18
Guava: An Effective and Universal Harness for Embodied Manipulation
arXiv:2606. 18363v1 Announce Type: cross Abstract: Language models trained on large-scale vision-language data have demonstrated strong potential for embodied agents.
arXiv AI
Jun 18
Generating Natural and Expressive Robot Gestures through Iterative Reinforcement Learning with Human Feedback using LLMs
arXiv:2606. 18747v1 Announce Type: cross Abstract: Expressive gestures are essential for natural and effective communication, complementing speech when verbal cues alone are insufficient (e.
arXiv Machine Learning
Jun 18
Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale
arXiv:2605. 07022v3 Announce Type: replace Abstract: Manually curated biomedical repositories -- spanning bioactivity, genomics, and chemistry -- are expensive to maintain, lag behind primary literature, and discard experimental context, obscuring nuances needed to assess data correctness and coverage.
arXiv AI
Jun 18
NeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localization
arXiv:2606. 18664v1 Announce Type: cross Abstract: Reliable sound source localization is fundamental to robot audition, enabling autonomous robots to perceive spatial cues and operate effectively in dynamic environments.
arXiv Machine Learning
Jun 18
Hierarchical Planning with Latent World Models
arXiv:2604. 03208v2 Announce Type: replace Abstract: World models are a promising path to zero-shot embodied control through planning.
arXiv Machine Learning
Jun 18
Zero-Shot Cross-City Generalization in End-to-End Autonomous Driving: Self-Supervised versus Supervised Representations
arXiv:2603. 11417v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving models are typically trained on multi-city datasets using supervised ImageNet-pretrained backbones, yet their ability to generalize to unseen cities remains largely unexamined.
arXiv Machine Learning
Jun 18
Stealthy World Model Manipulation via Data Poisoning
arXiv:2606. 18697v1 Announce Type: new Abstract: Model-based learning agents use learned world models to predict future states, plan actions, and adapt to new environments.
arXiv AI
Jun 18
AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework
arXiv:2606. 18532v1 Announce Type: cross Abstract: AI systems are increasingly evaluated in bounded environments that combine isolation, simulation, instrumentation, supervision, and evidence capture.
arXiv AI
Jun 18
Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation
arXiv:2606. 18594v1 Announce Type: cross Abstract: In real-world reinforcement learning (RL), the choice of action space can play a key role in shaping motion smoothness, safety, and overall task performance.
arXiv AI
Jun 18
WorldLines: Benchmarking and Modeling Long-Horizon Stateful Embodied Agents
arXiv:2606. 18847v1 Announce Type: new Abstract: To assist humans over extended periods in real homes, embodied agents must remember user routines, world states, and past interactions.
arXiv AI
Jun 18
R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations
arXiv:2510. 18085v2 Announce Type: replace-cross Abstract: Imitation Learning (IL) is a natural way for humans to teach robots, particularly when high-quality demonstrations are easy to obtain.
arXiv Machine Learning
Jun 18
Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models
arXiv:2606. 19297v1 Announce Type: new Abstract: Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual knowledge they retain after adaptation.
arXiv AI
Jun 18
A Knowledge Theory of Capital:The Value of Natural and Artificial Intelligence
arXiv:2606. 18288v1 Announce Type: cross Abstract: This volume develops a knowledge theory of capital for economies in which productive capacity increasingly resides in software, data, models, routines, expertise, platforms, organizations, commons, and public epistemic infrastructure.
arXiv AI
Jun 18
Semantic Router: On the Feasibility of Hijacking MLLMs via a Single Adversarial Perturbation
arXiv:2511. 20002v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) are increasingly deployed in stateless systems, such as autonomous driving and robotics.