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