Posts by Mehdi Saeedi
Unlocking Sim2Real for a Robotic Arm with RL Accelerated by AMD Instinct GPUs
- 29 September 2026
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.
GEAK V3: Agent-Driven, Repository-Level GPU Kernel Optimization across HIP, Triton, and FlyDSL on AMD GPUs
- 20 July 2026
In the ever-evolving world of GPU computing, optimizing kernels for performance and efficiency is a critical challenge. Hand-tuning kernels demands deep technical expertise and manual iteration. In this blog, you will read about how GEAK v3, the latest iteration of the agent-driven framework, tackles this problem using enhanced features such as task planning, test-harness discovery, patch-based handling of multi-file kernels, dynamic memory system and expert knowledge database. Our results show improvements across three kernel languages (HIP, Triton, and FlyDSL) and both CDNA and RDNA GPUs.
Utilizing AMD Schola and UnrealRoboticsLab with AMD ROCm™ Software to Train a Robotic Arm
- 17 June 2026
A great reinforcement learning (RL) training environment excels along many axes. Unreal® Engine brings a powerful combination of capabilities, including physically based rendering, high-fidelity visual environments, and a mature toolset for building rich interactive scenes. These strengths make it an excellent fit for training tasks that involve complex lighting or rich vision-based observations.
Training a Robotic Arm Using MuJoCo and JAX on AMD Hardware with ROCm™
- 31 March 2026
Training a robotic arm to pick up an object and place it somewhere else may sound straightforward, but teaching a robot to do this reliably in the real world is one of the harder problems in robotics. Traditional approaches rely on hand-tuned motion planning and carefully scripted control logic, which is brittle and time-consuming to maintain as environments change.