Let Me Help You! Neuro-Symbolic Short-Context Action Anticipation

In an era where robots become available to the general public, the applicability of assistive robotics extends across numerous aspects of daily life, including in-home robotics. This work presents a novel approach for such systems, leveraging long-horizon action anticipation from short-observation contexts. In an assistive cooking task, we demonstrate that predicting human intention leads to effective collaboration between humans and robots. Compared to prior approaches, our method halves the required observation time of human behavior before accurate future predictions can be made, thus, allowing for quick and effective task support from short contexts. To provide sufficient context in such scenarios, our proposed method analyzes the human user and their interaction with surrounding scene objects by imbuing the system with additional domain knowledge, encoding the scene object's affordances. We integrate this knowledge into a transformer-based action anticipation architecture, which alters the attention mechanism between different visual features by either boosting or attenuating the attention between them. Through this approach, we achieve an up to 9% improvement on two common action anticipation benchmarks, namely 50Salads and Breakfast. After predicting a sequence of future actions, our system selects an appropriate assistive action that is subsequently executed on a robot for a joint salad preparation task between a human and a robot.