New AI model gives humanoid robots 90 percent success in complex missions
SMRTR summary
A robot walks into an office building, rides the elevator, climbs the stairs, unpacks a snack delivery, and puts everything away, all without a single human command after the initial instruction.
That's the promise behind Reflect v1.0, a new robotics intelligence platform from Flexion Robotics. The system enables humanoid robots to complete long, multi-step tasks autonomously in everyday human environments.
What makes it notable is the leap in reliability. Using only traditional supervised learning, robots completed a 16-step test mission just 38 percent of the time. After reinforcement learning was layered in, that number jumped to 90 percent.
The platform accepts plain natural-language instructions, meaning users can simply change the prompt to assign a different task, or even update the mission mid-execution.
Flexion is candid about the limits, acknowledging the system isn't yet capable of universal autonomy. But the trajectory is clear: robots that think on their feet are getting closer to the workplace floor.
SMRTR provides this summary for quick context. The original article belongs to Interesting Engineering.
Read the original article