Robotics RL/DL Engineer

4 days ago

Pune, Maharashtra, India Kalyani Group Full time ₹ 12,00,000 - ₹ 36,00,000 per year

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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