The performance of prediction-based assistance for robot teleoperation degrades in unseen or goal-rich environments due to incorrect or quickly-changing intent inferences. Poor predictions can confuse operators or cause them to change their control input to implicitly signal their goal, resulting in unnatural movement. We present a new assistance algorithm and interface for robotic manipulation where an operator can explicitly communicate a manipulation goal by pointing the end-effector. Rapid optimization and parallel collision checking in a local region around the pointing target enable direct, interactive control over grasp and place pose candidates. We compare the explicit pointing interface to an implicit inference-based assistance scheme in a within-subjects user study (N=20) where participants teleoperate a simulated robot to complete a multi-step singulation and stacking task in cluttered environments. We find that operators prefer the explicit interface, which improved completion time, pick and place success rates, and NASA TLX scores. Our code is available at https://github.com/NVlabs/fast-explicit-teleop

翻译：由于意图推断不准确或快速变化，基于预测的遥操作辅助方法在未见或目标密集的环境中性能下降。错误的预测可能使操作者感到困惑，或导致他们改变控制输入以隐式传达目标，从而产生不自然的运动。我们提出一种新的辅助算法与接口，用于机器人操控，操作者可通过指向末端执行器显式传达操控目标。在指向目标周围的局部区域进行快速优化与并行碰撞检测，可实现对抓取与放置位姿候选的直接交互式控制。我们在被试内用户研究（N=20）中，将显式指向接口与基于隐式推断的辅助方案进行比较，参与者遥操作一台模拟机器人，在杂乱环境中完成多步骤的分拣与堆叠任务。研究发现，操作者更偏好显式接口，该接口在完成时间、抓取与放置成功率以及NASA TLX评分上均有改善。我们的代码已开源：https://github.com/NVlabs/fast-explicit-teleop