Assistive Tele-op: Leveraging Transformers to Collect Robotic Task Demonstrations

Abstract

Sharing autonomy between robots and human operators could facilitate data collection of robotic task demonstrations to continuously improve learned models. Yet, the means to communicate intent and reason about the future are disparate between humans and robots. We present Assistive Tele-op, a virtual reality (VR) system for collecting robot task demonstrations that displays an autonomous trajectory forecast to communicate the robot's intent. As the robot moves, the user can switch between autonomous and manual control when desired. This allows users to collect task demonstrations with both a high success rate and with greater ease than manual teleoperation systems. Our system is powered by transformers, which can provide a window of potential states and actions far into the future -with almost no added computation time. A key insight is that human intent can be injected at any location within the transformer sequence if the user decides that the model-predicted actions are inappropriate. At every time step, the user can (1) do nothing and allow autonomous operation to continue while observing the robot's future plan sequence, or (2) take over and momentarily prescribe a different set of actions to nudge the model back on track. We host the videos and other supplementary material at https://sites.google.com/view/assistive-teleop.

IFOR: Iterative Flow Minimization for Robotic Object Rearrangement

“…Transformers for Object Manipulation. The success of transformers in vision and NLP has led its way into robot learning [42,43,44,17]. Especially in object manipulation, transformer-based models with an attention mechanism can be utilized to extract features from sensory inputs to improve policy learning [45,46,47,48,49].…”