phuongboi/pushing-and-grasping-with-reinforcement-learning

This project is a classic academic or personal implementation of reinforcement learning for robotic pick-and-place tasks. With only 30 stars and 3 forks over nearly three years, it lacks the community traction or technical uniqueness to be considered a viable commercial or infrastructure project. It likely serves as a student project or a recreation of the work seen in papers like 'Learning Synergies between Pushing and Grasping' (Zeng et al.). Defensibility is minimal as it uses standard DQN architectures and commodity simulation tools (CoppeliaSim). Frontier labs and well-funded startups (e.g., Covariant, Sanctuary AI, or Google DeepMind with RT-2) have moved far beyond discrete action-space DQN towards large-scale Vision-Language-Action (VLA) models that generalize across tasks. NVIDIA's Isaac Gym and Omniverse offer significantly more performant simulation environments with built-in RL libraries, making projects like this largely obsolete for professional or production-grade research.