Training a policy in simulation and transferring those weights to a real robot (sim2real) is a highly desirable goal in physical AI. Simulation scales data collection far beyond what a single robot can provide, but mismatches in physics and in the software stack make transfer difficult.
In this blog post, we train a pick-and-lift policy for a UFactory XArm-6 with reinforcement learning on AMD Instinct GPUs, then deploy the same network on the real arm with a classical perception front-end in place of privileged simulator state.
If you’re building physical AI / robotics workloads on AMD hardware, this is a deployable-today walkthrough for closing the sim-to-real gap.
Read the full article here:
Unlocking Sim2Real for a Robotic Arm with RL Accelerated by AMD Instinct GPUs