Robotics Rl/Dl Engineer

4 weeks ago

Pimpri, India Kalyani Group Full time

Reinforcement Learning & Deep Learning for Robotic Arms 🔹 Location: Bharat Forge, Mundhwa, Pune 🔹 Job Type: Full-time 🔹 Experience Level: 3-6 Years 🔹 Industry: AI-Driven Robotics, Neural Network-Based Manipulation, Autonomous Dexterity Systems Job Overview We are looking for a highly technical Robotics Simulation Engineer specializing in reinforcement learning (RL) and deep learning for robotic arms. The ideal candidate should have strong expertise in learning-based motion planning, grasp adaptation, real-time trajectory optimization, and AI-powered dexterous manipulation. This role requires proficiency in ROS2 MoveIt, Orocos, NVIDIA Isaac Sim, Omniverse, Groot, RL-based control models, policy optimization, and Sim2Real AI adaptation strategies to push the boundaries of robotic autonomy and self-learning dexterity. Key Responsibilities 🔹 Reinforcement Learning for Robotic Manipulation: - Develop Deep Reinforcement Learning (DRL)-based policies for robotic arm dexterity, training models on high-dimensional action spaces for grasping and force adaptation. - Implement self-learning policy gradient optimization for improving robotic decision-making in dynamic task execution. - Train RL-based inverse kinematics (IK) models, enabling real-time trajectory correction and adaptive grasping strategies. 🔹 Deep Learning-Based Motor Control & Dexterity Optimization: - Work on neural network-driven control models, allowing robotic arms to self-learn adaptive grip forces, compliance, and motion refinement. - Apply transformer-based movement prediction algorithms, ensuring fluid and intelligent robotic motion sequencing. - Implement Sim2Real adaptation pipelines, transferring trained RL deep learning policies from simulation environments to real-world robotic execution. 🔹 Advanced Motion Planning & Collision Detection: - Optimize collision-aware trajectory generation algorithms using Hybrid-A, RRT, PRM, TEB*, ensuring real-time corrective motion strategies. - Develop ROS2 MoveIt and Orocos-integrated policy models, refining trajectory optimization for tool handling and dexterous motion control. - Train robotic arms on force-modulated grasp strategies, ensuring precision pick-and-place execution in dynamic environments. 🔹 Multi-Sensor Fusion & Neural SLAM in Robotics Simulation: - Model sensor fusion techniques, integrating LiDAR, depth cameras, IMU, force-torque sensors for neural SLAM- driven manipulation. - Utilize vision-language models (VLM) for task-driven robotic autonomy, enhancing semantic reasoning for self-correcting grasping strategies. 🔹 AI Simulation, Testing, Benchmarking & Deployment: - Conduct benchmark testing, validating RL-trained models against real-world execution consistency. - Debug AI-driven robotic control inconsistencies, ensuring seamless synchronization across real-time hardware execution pipelines. - Develop Sim2Real AI validation workflows, refining robotic arms' adaptability across varied manipulation tasks. 🔹 Research & Technical Documentation: - Maintain technical documentation, detailing RL-based motion planning architectures, deep learning policy training methodologies, and neural SLAM integration techniques. - Stay updated on cutting-edge advancements in AI-driven manipulation, RL-powered autonomous dexterity, and self-learning humanoid robotics.

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