Bimanual Long-Horizon Lifecare Robotics with Temporal Context LLM Planner and Transformer Reinforcement Learning

This paper presents Temporal-Context Planner with Transformer Reinforcement Learning (TCP-TRL), a novel robot intelligence capable of learning and performing complex bimanual lifecare tasks without using long-horizon (LH) human demonstrations and manually designed reward functions. TCPTRL combines a Large Language Model (LLM) enhanced by a built-in Retrieval-Augmented Generation (RAG) as TCP and a Transformer-Based reinforcement learning (TRL) framework. TCP serves as a high-level planner, translating textual task descriptions into temporal action contexts. These contexts then guide TRL to generate precise joint actions needed for bimanual robot arms. TCP use a built-in RAG, which leverages a compact dataset of 10 primitive actions (PAs) and task-specific information to guide LLM for LH task planning. The high-level planner TCP generates structured LH plans comprising task-sequenced PAs and sparse reward functions, replacing human demonstrations and human-designed reward functions. Then, under RL, the generated LH plan and sparse reward functions guide the TRL network through behavior cloning and online fine-tuning training, adding robustness and temporal dependencies. Evaluated on four lifecare LH tasks, the TCPTRL achieved an 81.86% average success rate, matching the performance of a state-of-the-art TRL model trained with human demonstrations and manually designed reward functions. The TCP-TRL provides a scalable human intervention-free solution and overcomes challenges in bimanual LH manipulation tasks, such as LLM hallucinations, dependence on large-scale datasets, and temporal context preservation.Clinical Relevance- TCP-TRL is a novel robotic approach specifically designed for learning and performing long-horizon lifecare tasks with dual-arm robots in hospital and homecare environments.