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.
arXiv AI
Aug 28
STEP: State-Aware Task Estimation and Planning with Multi-Modal LLMs for Human-Robot Collaboration
The paper introduces STEP, a State‑Aware Task Estimator and Planner that uses multi‑modal large language models to explicitly estimate system states and predict state transitions during task planning. By forecasting future states alongside actions, STEP reduces hallucinated actions and improves task‑convergent planning. In a simulated robot assembly task, STEP outperforms the state‑of‑the‑art by 32.8% in action executability and 14.8% in final‑state error.
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
Aug 18
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 AI
Aug 25
Physical Agentic AI: An Architecture for Orchestrating a Robot Crew with LLMs
Physical Agentic AI proposes an architecture that links semantic planning with physical execution for robot crews. Each robot exposes a typed skill library, while a foundation model planner decomposes tasks into phases and assigns robot‑skill pairs. A Robot Orchestrator validates and authorizes one skill at a time, ensuring actions are grounded in robot capabilities, system state, and workflow constraints before actuation.
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 AI
Sep 2
EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents
EmbodiedSkills is a unified framework that treats each skill decision as an execution proposal, checking prerequisites and verifying outcomes during long‑horizon vision‑language‑action tasks. It connects high‑level skill selection, bounded low‑level VLA execution, and post‑action verification through a fixed executable‑skill interface, enabling easy replacement of low‑level policies and recording of structured trajectories for supervision and adaptation. Instantiated with Qwen3‑VL and OpenPI/pi0.5 on RoboTwin 2.0 and LIBERO, the framework achieves high success rates (86.20% and 97.40% respectively) and demonstrates effective memory‑dependent task performance.
arXiv AI
2d ago
DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication
arXiv:2610.02161v1 Announce Type: cross Abstract: Vision-language models (VLMs) and vision-language-action models (VLAs) have recently driven rapid progress in general-purpose robots, yet most progre...
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
Sep 15
Bridging Thought and Action: Taming Long-Horizon Instability in Open-Source LLM Agents with a MetaTool-Enhanced ROS Framework
The paper introduces a ROS-Agent architecture that enhances task reliability and execution efficiency for open‑source LLM‑powered robotic agents. It adds a MetaTool that forces the LLM to produce a structured pseudo‑code plan before any action, storing this plan in a scratchpad to separate planning from execution. Experiments on a custom mobile robot show up to ~24% improvement in complex task completion and contextual consistency compared to the baseline.