Robot picking and packing tasks require dexterous manipulation skills, such as rearranging objects to establish a good grasping pose, or placing and pushing items to achieve tight packing. These tasks are challenging for robots due to the complexity and variability of the required actions. To tackle the difficulty of learning and executing long-horizon tasks, we propose a novel framework called the Multi-Head Skill Transformer (MuST). This model is designed to learn and sequentially chain together multiple motion primitives (skills), enabling robots to perform complex sequences of actions effectively. MuST introduces a “progress value” for each skill, guiding the robot on which skill to execute next and ensuring smooth transitions between skills. Additionally, our model is capable of expanding its skill set and managing various sequences of sub-tasks efficiently. Extensive experiments in both simulated and real-world environments demonstrate that MuST significantly enhances the robot's ability to perform long-horizon dexterous manipulation tasks.

The large-scale deployment of robotic manipulation systems in warehouses has highlighted the rare but costly problem of robot-induced object damage. We present a system that uses a classification model to predict whether an object will get damaged during robotic manipulation. The model uses object attributes retrieved from warehouse information systems as well as attributes available at our robotic workcell. We evaluated different classical machine learning models, as well as a large language model (BERT) and a multimodal-transformer for our task. We show that the multi-modal transformer model that is able to leverage text and image data outperforms models that only rely on categorical and numerical data. Furthermore, our comparative analysis equips the selection the optimal model for an application. We validate our system during an experiment in which the output of the damage prediction system is used to avoid picking objects that are likely to get damaged. In over 50k pick-and-place activities, our system reduces damage rate by 64%.