通过引导式自注意力行为克隆策略学习软体任务中的可泛化操作技能
Learning the Generalizable Manipulation Skills on Soft-body Tasks via Guided Self-attention Behavior Cloning Policy
AI总结：
针对具身智能中软体操作技能泛化难的问题，提出含引导自注意力模块、两阶段微调的GP2E行为克隆策略，在ManiSkill2挑战赛软体赛道夺冠，提升了模型泛化能力。
摘要：
具身智能（Embodied AI）是人工智能研究中的一种范式，其中人工智能体置身于物理或虚拟环境中并与之交互。尽管具身智能领域近期取得了进展，但要学习能够应对软体物体（如黏土、水和土壤）的大形变和拓扑变化的可泛化操作技能，仍然极具挑战性。在本研究中，我们提出了一种有效的策略，即GP2E行为克隆策略，它能够引导智能体从软体任务（包括倾倒、填充、悬挂、挖掘、捏制和书写）中学习可泛化的操作技能。具体而言，我们基于三点洞见构建该策略：（1）从点云数据中提取复杂的语义特征，并将其无缝集成到机器人末端执行器坐标系中；（2）通过引入我们的引导式自注意力模块，捕捉长时序任务中的长距离交互关系；（3）通过引入我们的两阶段微调策略，缓解过拟合问题，并推动模型收敛到更高的精度水平。通过大量实验，我们证明了该方法的有效性——在CVPR 2023第四届具身智能研讨会上举办的ManiSkill2挑战赛软体赛道中获得了第一名。我们的研究结果凸显了该方法在提升具身智能模型泛化能力方面的潜力，并为其在现实场景中的实际应用铺平了道路。
英文摘要：
Embodied AI represents a paradigm in AI research where artificial agents are situated within and interact with physical or virtual environments. Despite the recent progress in Embodied AI, it is still very challenging to learn the generalizable manipulation skills that can handle large deformation and topological changes on soft-body objects, such as clay, water, and soil. In this work, we proposed an effective policy, namely GP2E behavior cloning policy, which can guide the agent to learn the generalizable manipulation skills from soft-body tasks, including pouring, filling, hanging, excavating, pinching, and writing. Concretely, we build our policy from three insights:(1) Extracting intricate semantic features from point cloud data and seamlessly integrating them into the robot's end-effector frame; (2) Capturing long-distance interactions in long-horizon tasks through the incorporation of our guided self-attention module; (3) Mitigating overfitting concerns and facilitating model convergence to higher accuracy levels via the introduction of our two-stage fine-tuning strategy. Through extensive experiments, we demonstrate the effectiveness of our approach by achieving the 1st prize in the soft-body track of the ManiSkill2 Challenge at the CVPR 2023 4th Embodied AI workshop. Our findings highlight the potential of our method to improve the generalization abilities of Embodied AI models and pave the way for their practical applications in real-world scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
