Shared Autonomy with Learned Latent Actions
Shared Autonomy with Learned Latent Actions
复制标题
通过学习的潜在动作共享自主权
DOI：
10.15607/rss.2020.xvi.011
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Dorsa Sadigh
中科院分区：
文献类型：
--
作者：
Hong Jun Jeon;Dylan P. Losey;Dorsa Sadigh
辅助机器人使残疾人能够独立完成日常工作。然而，这些任务可能是复杂的，包含粗到达运动和细粒度操纵。例如，当进食时，不仅需要移动到正确的食物项目，而且还必须以不同的方式精确地操纵食物（例如，切、刺、挖）。共享自治方法通过仲裁用户输入与机器人控制，使机器人遥操作更安全，更精确。然而，这些作品主要集中在从离散集合中达到目标的高级任务上，而在很大程度上忽略了在该目标下对对象的操作。与此同时，用于遥操作的降维技术将有用的高维机器人动作映射到直观的低维控制器中，但目前还不清楚这些方法是否可以达到进食等任务所需的精度。我们的观点是，通过将学习到的潜在动作的直观嵌入与共享自主的机器人协助相结合，我们可以实现精确的辅助操作。在这项工作中，我们通过提出一个新的模型结构，改变了人类的输入的意义上的机器人的信心的目标，通过学习潜在的行动共享自治。我们展示了机器人到最有可能的目标的距离上的收敛边界，并开发了一个训练过程来学习一个控制器，该控制器即使在存在共享自主权的情况下也能够在目标之间移动。我们评估我们的方法在模拟和吃用户的研究。请在此查看我们的实验视频：此https URL
Assistive robots enable people with disabilities to conduct everyday tasks on their own. However, these tasks can be complex, containing both coarse reaching motions and fine-grained manipulation. For example, when eating, not only does one need to move to the correct food item, but they must also precisely manipulate the food in different ways (e.g., cutting, stabbing, scooping). Shared autonomy methods make robot teleoperation safer and more precise by arbitrating user inputs with robot controls. However, these works have focused mainly on the high-level task of reaching a goal from a discrete set, while largely ignoring manipulation of objects at that goal. Meanwhile, dimensionality reduction techniques for teleoperation map useful high-dimensional robot actions into an intuitive low-dimensional controller, but it is unclear if these methods can achieve the requisite precision for tasks like eating. Our insight is that---by combining intuitive embeddings from learned latent actions with robotic assistance from shared autonomy---we can enable precise assistive manipulation. In this work, we adopt learned latent actions for shared autonomy by proposing a new model structure that changes the meaning of the human's input based on the robot's confidence of the goal. We show convergence bounds on the robot's distance to the most likely goal, and develop a training procedure to learn a controller that is able to move between goals even in the presence of shared autonomy. We evaluate our method in simulations and an eating user study. See videos of our experiments here: this https URL
HARMONIC：辅助人类与机器人协作的多模式数据集
DOI：
10.1177/02783649211050677
发表时间：
2021
期刊：
The International Journal of Robotics Research
影响因子：
--
作者：
Newman, Benjamin A.;Aronson, Reuben M.;Srinivasa, Siddhartha S.;Kitani, Kris;Admoni, Henny
通讯作者：
Admoni, Henny