Skid-steer mobile robots present a unique challenge for reinforcement learning (RL) due to their nonholonomic constraints, dynamic wheel coupling, and high susceptibility to slip. Standard RL methods often struggle to achieve stable torque control in such settings, particularly under long-horizon tasks with sparse or delayed rewards. This thesis introduces a knowledge-assisted RL framework that systematically integrates expert demonstrations and curriculum learning to improve sample efficiency, training stability, and final policy performance.

The foundation of the method is a torque-based Deep Deterministic Policy Gradient (DDPG) agent, augmented through two key innovations: (1) KAMMA (Knowledge-Assisted Mixed Mode Actioning), a probabilistic switching mechanism that alternates between expert and learned actions to avoid interference artifacts and accelerate early-stage convergence; and (2) Curriculum-Driven System Identification, where the task complexity is gradually increased via staged velocity profiles to reveal underlying terrain-robot dynamics in a structured manner.

Experiments conducted in Isaac Sim demonstrate that this integrated KAMMA + Curriculum approach outperforms both baseline KA-DDPG and imitation-only variants across key metrics, including trajectory tracking error, policy smoothness, and convergence speed. The results confirm that combining staged learning with adaptive knowledge infusion enables robust torque-level control and offers a scalable template for learning-driven system identification in robotics.