5 accepted papers
机器人 快速掌握作技能是一项重大挑战， 受制于物理设备的寿命和安全 要求。目前，强化学习技术在 解决涉及丰富接触的动态、无结构问题 场景。然而，这些算法的收敛率通常为 由于机器人状态-动作映射的维度较高，速度较慢 空间以及广泛的初始政策搜索空间。与此同时， 大型语言模型（LLM）的进步赋予了这些模型 具有一定的逻辑推理能力，使他们能够接受 机器人初期阶段的主动目标导向行动 任务。这些模型可以隐式生成状态的特征和 揭示轨迹生成中的潜在模式。然而，复杂地说 涉及丰富接触场景的作性任务，LLM依然会失败 短暂。因此，整合了 的强大交互功能 &
In robotic skill acquisition, rapid policy learning remains challenging due to high-dimensional state-action spaces and inefficient exploration in the early stage of training cite{p1}. Although the pre-trained OpenVLA model exhibits cross-task generalization and can generate goal-directed actions fo…
In the domain of robot learning, equipping robots with the capability to swiftly acquire operational skills poses a significant challenge. Currently, reinforcement learning techniques are adept at addressing dynamic, unstructured problems involving rich contact scenarios. However, the convergence ra
Deepreinforcement learning algorithms offer a promising method for industrial robots to tackle unstructured and complex scenarios that are difficult to model. However, due to constraints related to equipment lifespan and safety requirements, acquiring a number of samples directly from the physical e
Complex operational scenarios increasingly demand that industrial robots sequentially resolve multiple interrelated problems to accomplish complex operational tasks, necessitating robots to have the capacity for not only learning through interaction with the environment but also for continual learni