Pranav Unnikrishnan

Autonomous RC Car

Robotics SLAM • Planning GitHub

• Built an autonomous hallway-navigation system using LiDAR-based SLAM, RF2O odometry, and a ROS2–ArduPilot pipeline.
• Implemented a PD-based waypoint follower with dual-criteria waypoint advancement for reliable multi-lap navigation and accurate cornering.
• Developed a LiDAR-only wall-following controller using RANSAC line extraction + geometric distance/orientation estimation for map-free navigation.
• Created an A* path-planning pipeline: map preprocessing, padding, distance-field cost shaping, and waypoint discretization.
• Prototyped a NumPy-only camera navigation system and evaluated a custom ICP-EKF localization approach.

Social Navigation Project (SocNavGym)

Robotics Reinforcement Learning GitHub

• Extended SocNavGym with generalized environment handling (padding + attention-based encoders) and a proxemics-based reward function.
• Trained DQN agents across simple and complex social environments and compared reward models (Baseline, DSRNN, TGRF, Proxemics).
• Ran large-scale evaluation (100-episode rollouts per agent) + human A/B preference tests; proxemics-based policies performed best in complex scenes.

Algorithms for Exploration and Exploitation in Reinforcement Learning

Reinforcement Learning Research / Implementations GitHub

• Implemented and compared value-based (Q-learning, SARSA, DQN) and policy-based (REINFORCE, PPO, DDPG) algorithms.
• Designed and trained agents in classic control + gym environments (Cliff Walking, Taxi, LunarLander-v2, Pendulum).
• Applied TD learning, Bellman equations, policy gradients, and actor-critic methods to study exploration–exploitation trade-offs.
• Produced training curves and performance evaluations showing convergence and stability across algorithms.

Sawyer Robot Analysis

Robotics MuJoCo • RL GitHub

• Analyzed Sawyer task data with Python, OpenCV, and NumPy; visualized kinematics, trajectories, and control signals for benchmarking.
• Implemented Q-Learning, PPO, and DDPG in Sawyer (MuJoCo) environments for robotic grasping and trajectory-following tasks.
• Evaluated convergence and stability to compare training behavior across algorithms.

DRL-Based Map-less Crowd Navigation for Mobile Robots

Robotics TD3 • SAC Gazebo • TurtleBot3

• Designed and implemented a deep RL navigation system for autonomous mobile robots in crowded environments.
• Developed reward functions incorporating collision probability (CP) and trained models with TD3 and SAC using TurtleBot3 + 2D laser scans in Gazebo.
• Achieved strong gains: TD3 + CP reached 1387 successful episodes out of 1400 vs 255 without CP.

Real-Time AI-Enabled Retail Analytics & Data Pipeline

Data Engineering Streaming • CV GitHub

• Architected an edge-to-cloud pipeline (Jetson Nano → Kafka → PySpark → MongoDB Atlas) with real-time object detection for automated checkout and invoice generation.
• Built an ALS-based recommendation engine and an MQTT bridge for personalized suggestions and high-throughput async transfer.

Football Player Performance Scraper & Analysis

Data FastAPI • Pandas GitHub

• Scraped and engineered a 64-feature dataset of 12,900+ players across 5 seasons (2020–2025) using Python, FastAPI, Pandas, and NumPy.
• Applied EDA, trend modeling, and visualization to predict market value fluctuations and identify top performers for transfer analytics.

Hyperpartisan News Classification (BERT + Longformer)

NLP Transformers GitHub

• Compared chunk analysis, summarization, and stride chunking to detect extreme bias in 10,000 news articles using BERT and Longformer.
• Reached 95.5% accuracy using RoBERTa + chunk analysis, outperforming full-sequence Longformer.
• Analyzed misclassifications and label frequency distributions to improve generalization.

Efficient Fine-Tuning of LLMs + RLHF

GenAI QLoRA • PEFT GitHub

• Fine-tuned Llama 7B on Vicuna (20,000 samples) using QLoRA and PEFT for parameter-efficient adaptation.
• Explored optimization strategies including 8-bit matmul, NF4 quantization, and nested quantization.
• Integrated RLHF via TRL + PPO and deployed a text-generation policy model behind a functional UI.

NBA Player Scoring Analytics & Regression Modeling

Analytics R • Regression GitHub

• Cleaned and processed NBA scoring + salary datasets in R to prepare analysis-ready data.
• Performed EDA to identify trends, outliers, and differences between normal and high scorers.
• Built and compared linear regression, Poisson regression, and GAM models to study scoring patterns.