We engineer reinforcement learning systems for autonomous robots — quadrupeds, manipulators, drones, and humanoids. From high-fidelity simulation to validated hardware deployment.
Our RL pipelines deploy across every major robot form factor — ground, air, and bipedal systems.
Dynamic locomotion, manipulation, and throwing policies. Phase-controlled gaits with sim-to-real transfer.
legged_locomotion // arm_manipulationAutonomous navigation, obstacle avoidance, and precision landing for multi-rotor platforms.
px4_autopilot // nav_avoidanceWhole-body control, bipedal locomotion, and dexterous manipulation through learned policies.
bipedal_control // dexterous_manipEnd-to-end systems from reward design to real-world deployment.
PPO policy for high-velocity throwing. Kinematic chain whip, phase clamping, curriculum 3m→10m, velocity auto-release, FK collision avoidance.
Voice-commanded robot control with vision-language models. Person tracking, ASR, GPS, tactical bridge on edge compute.
Real-time multi-object tracking. 4-state machine, sparse optical flow, fisheye correction.
Multi-machine local model serving. Optimized quantized models for robot command interpretation.
Group Relative Policy Optimization. Parameter-efficient fine-tuning in under 15 minutes.
PPO, SAC with GPU-parallel environments in high-fidelity simulation.
Domain randomization and system ID validated on hardware.
Detection, tracking, and pose estimation for edge deploy.
On-device LLM for robot command interpretation.
ROS 2, sensor fusion, behavior trees, distributed compute.
CUDA optimization, quantization, real-time tuning.
Desktop GPU, 16GB VRAM, 128GB system. Primary simulation training.
CUDA 12.8 // x86_64125GB unified memory. Large model inference and fine-tuning.
CUDA 13.0 // ARM6464GB unified, ROS 2. Primary robot deployment platform.
ARM64 // DockerDesktop AI supercomputer. 3.67TB storage.
NVIDIA DGXChallenging problems in robotics, reinforcement learning, and autonomous systems.