EmbodiedAI
LLMs are getting better at reasoning—but can they act in the world? VirtualEnv is a new open-source simulation platform (built on Unreal Engine 5) that lets researchers put AI agents to the test in rich, interactive 3D worlds. * Fine-grained benchmarking of embodied skills: navigation, object manipulation, multi-agent teamwork. * Game-inspired
AI
How do we know if game-playing AIs can plan, act, and remember like good teammates? Meet MineNPC-Task—a new open benchmark for memory-aware AI agents inside Minecraft’s open world. Instead of toy prompts, tasks come from real co-play sessions with expert players, then get turned into templates with clear
EmbodiedAI
What’s new As AI video models are used as “world models” for robots, we need to know if their imagined futures match reality. WoW-World-Eval (Wow, wo, val) is a public benchmark that stress-tests these models before we trust them on real machines. * Five skills: perception, planning, prediction, generalization, execution.
AI
Can video AIs guide real robots? Meet the WoW-World-Eval Embodied Turing Test Robots need 'world models' that predict what happens next. A new benchmark, WoW-World-Eval (Wow, wo, val), tests video foundation models on 609 robot manipulation samples across five skills: perception, planning, prediction, generalization, and execution. * 22-metric score
Robotics
What’s new Meet SENTINEL, an end-to-end model that lets humanoid robots turn plain-language instructions into whole-body motion - no hand-crafted pipelines or teleop in the loop. How it works * Trains on a large dataset built by tracking human motions in simulation and pairing them with text descriptions. * Directly maps
AI
Teach Robots to Learn Like People Building versatile embodied AI (agents that see, read, and act) is hard because real-world data is scarce and training is expensive. We introduce Deliberate Practice Policy Optimization (DPPO), a metacognitive "metaloop" that alternates between: * Supervised fine-tuning to expand general competence * Reinforcement learning
AI
Building capable embodied AI is hard: real-world data is scarce and training is expensive. This paper introduces Deliberate Practice Policy Optimization (DPPO), a coach-like training loop that helps vision-language agents learn more from less. How it works: the system alternates between learning from examples (to expand skills) and trial-and-error reinforcement
Robotics
Robots are great at repeating tasks—but not at knowing when to trust their own choices. This study brings metacognition (self-monitoring) to robots by giving them a sense of confidence in each decision. * Inspired by neuroscience, the architecture treats confidence as a second-order judgment about actions. * In tests on autonomous
Robotics
What's new PhysWorld lets robots learn skills from AI-generated videos—without collecting any real robot data. How it works * Given a task and one photo of the scene, a video model "imagines" a demonstration. * PhysWorld reconstructs a physics-based world from that video. * An object-centric learner turns
Robotics
What if robots could learn new tasks just by watching AI-generated videos - and still obey physics? PhysWorld is a new framework that turns text-and-image prompts into physically executable robot skills. * It first generates a task-conditioned video from a single scene image and a command. * Then it reconstructs the scene&
Robotics
Robots generate huge logs. This open‑source ROSBag MCP Server lets you ask an AI to analyze ROS/ROS 2 bag files in plain English—no custom scripts. It plugs into Model Context Protocol (MCP), so assistants can call purpose-built tools for robot data. Great for debugging, teaching, and research