arXiv AI
Jul 14
A Glimpse into Long-term Physical Coexistence with Intelligent Robots
arXiv:2607. 11377v1 Announce Type: cross Abstract: Long-term physical coexistence with intelligent robots requires more than capable robot policies.
MistyPilot: Enabling Social-Robot Control through Multi-Agent LLM Skill Orchestration
arXiv:2608. 15549v1 Announce Type: cross Abstract: Programming small social robots from natural-language instructions requires more than invoking isolated APIs.
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arXiv AI
Jun 17
MagicSim: A Unified Infrastructure for Executable Embodied Interaction
arXiv:2606. 17511v1 Announce Type: cross Abstract: Robot learning and embodied agents now require simulation to serve as a shared execution substrate linking control, skills, and planning, not only as a renderer, controller testbed, or fixed task environment.
Hugging Face Trending Papers
Jul 13
A Glimpse into Long-term Physical Coexistence with Intelligent Robots
Long-term physical coexistence with intelligent robots requires more than capable robot policies. A persistent robotic assistant must support diverse user-facing interfaces, maintain long-horizon memory of people and preferences, coordinate across robot embodiments, and translate human intent into safe physical execution.
arXiv Machine Learning
1d ago
SkillComposer: Learning Reusable Skills for Natural-Language Robot Programming
arXiv:2608. 14944v1 Announce Type: cross Abstract: Natural-language interfaces can lower the barrier to programming robots, but existing systems struggle when users request complex tasks.
arXiv Machine Learning
Jul 16
HRIBench: Benchmarking Interaction-Centric Human-Robot Collaboration
arXiv:2607. 13056v1 Announce Type: cross Abstract: Current vision-language-action (VLA) benchmarks primarily evaluate isolated manipulation skills while leaving human-robot interaction structure largely unmodeled.
arXiv AI
Jun 30
StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley
arXiv:2507. 07445v3 Announce Type: replace Abstract: Autonomous agents navigating human society must master both production activities and social interactions, yet existing benchmarks rarely evaluate these skills simultaneously.
arXiv Machine Learning
Aug 10
Robot guide with multi-agent control and automatic scenario generation with LLM
arXiv:2509. 10317v2 Announce Type: replace-cross Abstract: The article describes the development of a hybrid social robot control architecture to overcome the limitations of traditional approaches, where behavior scripts manually synchronize the robot's actions and text, and existing methods focus primarily on short dialogue responses.
arXiv AI
1d ago
When State Becomes an Attack Surface: State-Semantic Injection in LLM-Driven Embodied Agents
arXiv:2608. 16806v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated capabilities in in-context learning, task decomposition, step-by-step reasoning, and code generation, driving their gradual evolution from text generation models into the core of agents capable of perceiving environments, invoking tools, and executing tasks.
arXiv AI
Jun 9
Efficient Skill Grounding via Code Refactoring with Small Language Models
arXiv:2606. 07999v1 Announce Type: new Abstract: Effective skill grounding is essential for deploying reusable skills in embodied agents, as even minor embodiment or environmental differences can render an entire skill incompatible.
arXiv Machine Learning
Jul 1
Prompting Robot Teams with Natural Language
arXiv:2509. 24575v2 Announce Type: replace-cross Abstract: This paper presents a framework to prompt multi-robot teams with high-level tasks using natural language expressions.
arXiv AI
Jul 14
Think When It Matters: Conditional VLM Reasoning for Social Navigation with RL Policies
arXiv:2607. 10991v1 Announce Type: cross Abstract: As mobile robots become more integrated into everyday human environments, social robot navigation is becoming essential for ensuring human comfort, safety, and trust.
arXiv AI
Jun 30
Demonstration-Free Robotic Control via LLM Agents
arXiv:2601. 20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift.