Technical Intelligence Report: 2025-12-04

🤖 ROCm Updates & Software

[2025-12-04] Training an X-ARM 5 robotic arm with AMD Schola and Unreal Engine

Source: AMD GPUOpen

Key takeaway relevant to AMD: > AMD Schola v2.0 provides a critical bridge for AMD developers to connect Python-based reinforcement learning (RL) frameworks with the high-fidelity simulation capabilities of Unreal Engine, facilitating the development of complex robotics AI.

Summary: > This technical guide demonstrates the application of AMD Schola v2.0 for training a simulated UFACTORY X-Arm 5 robotic arm. Using the Soft Actor-Critic (SAC) algorithm via Stable-Baselines3, AMD shows how reinforcement learning agents can be trained within Unreal Engine to perform spatial tasks, progressing from static environments to highly randomized, dynamic scenarios.

Details:

  • Software Framework: Utilizes AMD Schola v2.0, a library designed to integrate Unreal Engine (C++) with Python RL frameworks.
  • Hardware Simulation: Replicates the UFACTORY X-Arm 5, a 5 Degrees of Freedom (5 DOF) robotic arm, within the Unreal Engine environment.
  • Algorithm Specifics: Employs Soft Actor-Critic (SAC) from the Stable-Baselines3 library. SAC was chosen for its off-policy nature (allowing reuse of the replay buffer) and its entropy regularization, which facilitates exploration in complex action spaces.
  • Observation Space: A continuous space of floating-point numbers including:
    • Spatial coordinates (XYZ) of three target blocks (Red, Blue, Yellow).
    • Color identification data for each block.
    • Location coordinates of the robot’s end effector.
    • Specific target color indicators for the current episode.
  • Action Space: The agent outputs three floating-point values representing a force vector applied to the robot’s end effector to produce movement.
  • Training Progression:
    • Task 1: Reaching a fixed location.
    • Task 2: Reaching a target block (Red) where block colors are randomly permuted across three fixed locations.
    • Task 3: Fully randomized targets and locations, requiring the agent to adapt to dynamic reasoning rather than static spatial cues.
  • Developer Implications: For developers, this demonstrates Schola’s ability to handle high-dimensional continuous input/output, making it suitable for industrial digital twin applications and complex robotics research on AMD-powered workstations.