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First in Modular Robotics https://t.co/EsSbOYVGUV

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Live from the Inclusion Conference, @LarryHashpowerX shares an update with the Robora community. Bipedal robotics, global manufacturing, and the momentum we’re building were all in focus. We are already in active discussions with several major manufacturers, and the level of interest continues to grow. One of these discussions is moving toward a major partnership, which we look forward to announcing soon. Through these efforts, Robora is working to bring Web3 into the broader robotics space, creating new opportunities where decentralized infrastructure and physical AI converge.

Live from the Inclusion Conference, @LarryHashpowerX shares an update with the Robora community. Bipedal robotics, global manufacturing, and the momentum we’re building were all in focus. We are already in active discussions with several major manufacturers, and the level of interest continues to grow. One of these discussions is moving toward a major partnership, which we look forward to announcing soon. Through these efforts, Robora is working to bring Web3 into the broader robotics space, creating new opportunities where decentralized infrastructure and physical AI converge.

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Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.

Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.

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