Configurable Bin-Packing RL Environment in IsaacLab

This project develops a configurable bin-packing simulation environment in IsaacLab. The system uses a virtual dropper to release rigid cubes into scaled KLT bins, while IsaacLab simulates falling, collision, and settling. The environment supports parallel simulation, configurable cube count and size, vector/image/multimodal observations, reward variants, PPO training integration, camera output, and timing benchmarks. The main focus is to validate a reusable IsaacLab simulation and benchmarking pipeline for future reinforcement-learning and robotic manipulation extensions.