Nikhil Chinnalapatti Gopinath
Building the future of autonomous systems, one robot at a time
Manager of Automation Engineering at Walmart, specializing in computer vision, robotics, and AI-driven automation. Carnegie Mellon graduate with expertise in perception systems, robotic manipulation, and cutting-edge machine learning.
Experience
Leading innovation in robotics and automation across industry and research
- Spearheaded technical development of an industry-leading vision/perception system for case segmentation using GroundingDino & SAM2. Established the entire pipeline for post-processing, collision detection, and robot path planning. Achieved 98% accuracy and reduced inference latency from 19s → 4s
- Developed custom datasets, enabling model fine-tuning for accuracy gains and real-time performance, and a ROS2-based integration platform connecting vision, AMR control, PLC, and Fanuc robotic systems
- Led technical development of the industry-first robotic automated trailer swing door opening solution, projected to deliver $15.1M in savings, through robotic programming, state of the art vision system, and a custom End-of-Arm Tool (EOAT)
- Designed a custom AMR platform with synchronous control of dual industrial arm for latch and handle manipulation based on direction from vision system
- Supported the creation of an internally designed robotic automated trailer unload system, with potential savings of $1.9B, by evolving the system from prototype to proof-of-concept (PoC). Achieved 85% case entitlement at 850 cases/hour peak throughput
- Developed computational design optimization of a Quadruped Robot with a Spine using Pybullet simulation on ROS
- Devised an end-to-end hierarchical Model Predictive controller for generating joint commands of the quadruped
- Worked on grant with Google DeepMind
Projects
Pushing the boundaries of computer vision, robotics, and machine learning
Visual-Word Sense Disambiguation
Created state-of-the-art Visual Word Sense Disambiguation using MetaClip model evaluated against Weighted Average Loss. Achieved Hit Rate of 0.7235 and Mean Reciprocal Rank (MRR) of 0.8280.
Automatic Speech Recognition using Synthetic Speech
Generated 300 hours of Synthetic data using Variational Inference with adversarial learning Text-to-Speech models. Achieved a Levenshtein Distance of 23.38 and Loss of 0.14.
Deep Learning Portfolio
Comprehensive deep learning projects: Phoneme recognition (89.2%), Face Classification (89.9%), Face Verification (64.3%), ASR using LSTMs/RNNs/CTCs (LD: 3.98), and Attention-based TTS (LD: 9.99).
Construction Site Hazard Detection
Programmed a worker detection model using YoloV7 and Transfer Learning, with computation of 3D global coordinates using transformation matrices. Achieved mAP0.5 of 92% with 90% precision and 93% recall.
Skills
A comprehensive toolkit for robotics, AI, and automation development
Programming Languages
AI & Machine Learning
Robotics & Simulation
Engineering & Design
Cloud & DevOps
Specializations
Patents & Publications
Contributing to the advancement of robotics and automation technology
Education
Beyond Technical Work
Get in Touch
Open to opportunities in robotics, AI, and automation. Let's build the future together.