The problem
RL policies are good at precise low-level control but struggle to generalise across task descriptions; LLMs reason well about tasks but cannot control joints. The framework splits the work between them.
Approach
- 01An LLM interprets the natural-language task and decomposes it into sub-goals.
- 02RL policies (PPO and SAC) execute each sub-goal as low-level motion.
- 03Evaluated in PyBullet with a simulated Franka Emika Panda on pick-and-place, object sorting and dynamic obstacle avoidance.
Evidence
Compared with an RL-only baseline in simulation, the study reports shorter task completion times and higher success and adaptability. Metric definitions and trial counts are not detailed, so these numbers are not reproduced here.
Scope and limitations
Simulation-based evaluation. Real-world robot validation and sim-to-real transfer remain future work.
Publication
2025
Conference
Hybrid Framework for Robotic Manipulation: Integrating Reinforcement Learning and Large Language Models
IEEE International Conference on Emerging Technologies and Innovation for Sustainability (EmergIN 2025) · IEEE EmergIN 2025 · pp. 886–890
LLM-based task planning on top of RL-based low-level control (PPO/SAC), evaluated in PyBullet with a simulated Franka Emika Panda.