
Robotics Simulation Engineer
2 weeks ago
Key Responsibilities:
Reinforcement Learning for Manipulation
Design and implement deep reinforcement learning (RL) policies for robotic manipulation in dynamic settings
Develop self-learning and policy optimization techniques to improve decision-making
Train inverse kinematics (IK) models for real-time, adaptive motion control
Deep Learning for Motor Control & Dexterity
Build neural network-based control models for grip compliance, force adaptation, and fluid motion
Use transformer models for intelligent motion sequencing
Develop Sim2Real pipelines to transfer trained models to physical robots
Motion Planning & Collision Avoidance
Implement and refine trajectory planning using RRT, PRM, Hybrid-A*, TEB*
Integrate motion control policies with ROS2 MoveIt and Orocos
Enable grasping strategies that adapt to force and handle unstructured environments
Sensor Fusion & Environment Mapping
Build systems combining data from LiDAR, depth cameras, IMU, and force sensors
Use Neural SLAM techniques for accurate mapping and object manipulation
Explore Vision-Language Models (VLMs) to support semantic understanding in robotic tasks
Testing, Simulation & Deployment
Benchmark model performance against real-world scenarios
Troubleshoot and refine control pipelines for reliability
Develop frameworks for Sim2Real validation and deployment
Documentation & Research
Maintain clear and detailed documentation of models, training processes, and system design
Stay updated on research in AI, robotics manipulation, and autonomous control systems
Must-Have Skills:
Strong foundation in Reinforcement Learning, Deep Learning, and trajectory optimization
Experience with ROS2 MoveIt, Orocos, NVIDIA Isaac Sim, Groot, and Omniverse
Hands-on work in Sim2Real transfer and AI-based robotic control
Familiarity with motion planning algorithms, sensor fusion, and SLAM frameworks
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