#IsaacLab

🤖 Full-body teleoperation is wild! Just got Meta Quest 3's body tracking working with IsaacSim to control a GR1T2 humanoid in real-time.

Head, hands, body - all synced up for natural robot control.

Technical breakdown & code dropping soon 👀

#robotics #vr #embodiedai #isaaclab

Master Isaac Lab Ant Jumper: 10 Steps to Train High-Flying Robots   Source Video Link: Reinforcement learning (RL) holds immense promise for revolutionizing how robots learn and interact with our world. Imagine robots that can instinctively grab objects, navigate complex terrains, or even perform intricate assembly tasks, all by learning through trial and error. While the theoretical elegance of RL is captivating, bringing these learned behaviors to physical robots presents unique challenges. Training a real robot can be costly, time-consuming, and, crucially, unsafe. A robot performing random, exploratory actions in a physical environment could lead to damage or injury. This is precisely where the power of simulation steps in.

Teach robots to jump! Learn the Isaac Lab Ant Jumper tutorial using NVIDIA Isaac Sim and reinforcement learning. #IsaacLab #Robotics #ReinforcementLearning

IsaacLab Framework Boosts Scalable Heterogeneous Adversarial MARL

IsaacLab adds scalable adversarial MARL for heterogeneous robots, with pursuit‑evasion benchmarks and a competitive HAPPO version. Code is open on GitHub. getnews.me/isaaclab-framework-boost... #isaaclab #adversarialmarl