AI
Computers can see and hear, but smelling the world has been out of reach. New research introduces New York Smells: a large, real-world dataset that pairs odors with images. * 7,000 smell-image pairs from 3,500 objects, indoors and outdoors — about 70x more objects than past datasets. * Benchmark tasks: match
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
AudioAI
Ever wished your mic could zoom in on the right voice, even in a noisy room? This research shows how. An on-device deep learning tracker works with a compact ring-shaped array of tiny MEMS microphones to steer a "listening beam" in real time—locking onto a moving target
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
Robotics
Robots that learn by watching you What if multi-fingered robots could pick up everyday skills just by watching people in the real world? A new framework, AINA, takes a big step toward that. Using lightweight Aria Gen 2 smart glasses, anyone can record natural demonstrations at home, in the office,
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
ReinforcementLearning
Reinforcement learning (RL) wins in games and simulators—but deploying it on real products is a different story. Gabriel Dulac‑Arnold, Daniel Mankowitz, and Todd Hester outline nine must-solve challenges before RL can safely power real-world systems. * Safety & constraints: avoid harmful actions while learning. * Sample efficiency: learn from limited,
AutonomousDriving
Teaching self-driving cars to spot the unknown Real roads are messy. To stay safe, autonomous vehicles must notice unexpected hazards—from a toppled ladder to a runaway stroller. That’s the challenge of out-of-distribution (OOD) segmentation. Seungheon Song and Jaekoo Lee propose a simple idea with big impact: use language
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 decide how to grasp anything—tools, toys, odd-shaped parts—in real time? Lightning Grasp delivers orders-of-magnitude speedups for dexterous robotic hands, while keeping results diverse and reliable. The trick is a simple data structure called a Contact Field: it precomputes where and how fingers can safely
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&