Assistant Professor at School of Engineering & Technology, Baba Ghulam Shah Badshah University - Rajouri (J&K) India.
What if your robot didn’t pick one move at a time—but imagined an entire motion, then refined it from noise into a perfect plan? Diffusion Policies in Robotics Diffusion policies treat robot actions like a generative process: start with noise, then denoise into a safe, efficient sequence. In manipulation benchmarks, this approach handles multi-modal choices and high-dimensional control, often outperforming prior IL/RL baselines. The idea is scaling fast: Octo pretrains a transformer diffusion policy on ~800k trajectories from Open X-Embodiment, then quickly adapts to new robots, sensors, and action spaces—“generalist first, specialize later.” And beyond hands, diffusion is powering trajectory planning for mobile manipulation—sampling whole feasible paths and enforcing physics constraints during denoising. Why it matters: diffusion lets robots propose many futures, pick the best one in milliseconds on the edge, and recover when reality changes—turning brittle scripts into creative autonomy. A solid, 2025 survey charts the progress across grasping, planning, and data augmentation. Speaker Dr. Qamar Ul Islam D.Engg. B.Tech. M.Tech. Ph.D. FHEA #DiffusionPolicy #RobotLearning #EmbodiedAI #Octo #TrajectoryPlanning #EdgeAI #InfiniteMind #ai #robotics #viral #shorts #youtubeshorts
