用触觉传感提升机器人抓取与拆卸精度，减少对视觉依赖
Improving Robotic Manipulation: Techniques for Object Pose Estimation, Accommodating Positional Uncertainty, and Disassembly Tasks from Examples
- 结合触觉时序特征估计物体姿态
- 触觉反馈驱动强化学习，降低抓取失败次数
- 借鉴人类示范加速拆卸路径学习，适合复杂操作场景
为让机器人在非结构化环境中更高效工作，需增强环境感知能力以应对不确定性。尽管摄像头广泛用于机器人任务，但其存在遮挡、可视性差及信息范围有限等问题，促使研究转向触觉传感。本论文探索利用触觉传感器的时序特征来估计物体姿态；通过强化学习结合触觉碰撞信号，减少因相机估计偏差导致的抓取尝试次数；最后，利用触觉提供的信息，让强化学习智能体学习从狭窄通道中移除物体的轨迹，并通过借鉴人类示范显著缩短训练时间。
原文摘要 · Abstract (English)
To use robots in more unstructured environments, we have to accommodate for more complexities. Robotic systems need more awareness of the environment to adapt to uncertainty and variability. Although cameras have been predominantly used in robotic tasks, the limitations that come with them, such as occlusion, visibility and breadth of information, have diverted some focus to tactile sensing. In this thesis, we explore the use of tactile sensing to determine the pose of the object using the temporal features. We then use reinforcement learning with tactile collisions to reduce the number of attempts required to grasp an object resulting from positional uncertainty from camera estimates. Finally, we use information provided by these tactile sensors to a reinforcement learning agent to determine the trajectory to take to remove an object from a restricted passage while reducing training time by pertaining from human examples.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。