3 accepted papers
Large Language Models (LLMs) have showcased remarkable reasoning capabilities in various domains, yet face challenges in complex embodied tasks due to the need for a coherent long-term policy and context-sensitive environmental understanding. Previous work performed LLM refinement relying on outcome…
Recent planning methods based on Large Language Models typically employ the In-Context Learning paradigm. Complex long-horizon planning tasks require more context(including instructions and demonstrations) to guarantee that the generated plan can be executed correctly. However, in such conditions, L…
Multi-task learning has expanded the boundaries of robotic manipulation, enabling the execution of increasingly complex tasks. However, policies learned through reinforcement learning exhibit limited generalization and narrow distributions, which restrict their effectiveness in multi-task training.…