Optimus Agent.
foundational autonomous reasoning kernel. optimus discovers, evaluates, and synthesizes reward functions for reinforcement-learning agents via massively parallel simulation and heuristic optimization.
NEURAL_KERNEL_ARCHITECTURE
Latent Space Manifold
Transformer Policy
Value Network
IO_Buffer_System
| MORPHOLOGY_ID | DOF_COUNT | SOLVER_STATUS | STEP_LATENCY | CONV_CONFIDENCE |
|---|---|---|---|---|
01Unitree Go2 | 12_DOF | STABLE | 0.8ms | 98.2% |
02Agility Digit | 20_DOF | OPTIMIZING | 1.2ms | 84.5% |
03Shadow Hand | 24_DOF | STABLE | 1.5ms | 92.1% |
04Frank Panda | 7_DOF | STABLE | 0.4ms | 99.8% |
05Anymal C | 18_DOF | TESTING | 0.9ms | 72.4% |
Reward Landscape Discovery
optimus performs millions of stochastic mutations on the abstract syntax tree (ast) of its reward functions to avoid local minima and discover dense gradients.
Neural Vision Analysis
integrated vision and state transformers analyze agent behavior and environment contact in real-time to identify failure modes and wasted effort before they occur.
Hyper-Parallel GPU Scaling
seamlessly distribute thousands of concurrent training environments across global h100 clusters with zero-latency weight synchronization.
Agent Autonomy Module
optimus is a self-evolving reasoning kernel designed to identify the shortest path from intent to a working policy. by bridging the gap between semantic task definitions and low-level control, it eliminates the need for manual reward engineering and solves long-horizon tasks that remain intractable for static reinforcement-learning pipelines.
Autonomous Reward Reflection
optimus abandons static search algorithms. instead, it utilizes a foundation reasoning model to write, evaluate, and rewrite dense reward functions in real-time. by analyzing telemetry from thousands of parallel rollouts, the agent interprets failures in natural language, identifies reward hacking, and autonomously patches its own python logic until the policy converges on stable, deployable behavior.
v_x without constraining torso orientation."Joint Velocity
Torque Output
Contact Pen
Base Pitch
automated reward synthesis naturally gravitates toward exploiting the simulator (e.g., vibrating to fake forward momentum). optimus enforces strict regularization constraints that penalize degenerate behavior, ensuring policies transfer reliably from sim to deployment.
Initialize Optimus.
allocate high-performance compute nodes, synthesize your reward landscapes, and begin autonomous training of your next-generation rl policies today.