We present Unified-IO 2 a multimodal and multi-skill unified model capable of following
novel instructions. Unified-IO 2 can use text images audio and/or videos as input and can …

Task and motion planning (TAMP) integrates high-level task planning and low-level motion
planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic …

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a
feedback loop with four core functionalities: monitoring, analyzing, planning, and execution …

Diffusion models surpass previous generative models in sample quality and training
stability. Recent works have shown the advantages of diffusion models in improving …

Training general robotic policies from heterogeneous data for different tasks is a significant
challenge. Existing robotic datasets vary in different modalities such as color, depth, tactile …

Large monolithic generative models trained on massive amounts of data have become an
increasingly dominant approach in AI research. In this paper, we argue that we should …

DoF grasp detection has been a fundamental and challenging problem in robotic vision.
While previous works have focused on ensuring grasp stability, they often do not consider …

One promising approach towards effective robot decision making in complex, long-horizon
tasks is to sequence together parameterized skills. We consider a setting where a robot is …

Learning from Demonstrations, the field that proposes to learn robot behavior models from
data, is gaining popularity with the emergence of deep generative models. Although the …

The ability to manipulate objects in desired configurations is a fundamental requirement for
robots to complete various practical applications. While certain goals can be achieved by …