DMMP: Diffusion Model Motion Primitives
for Proactive Assistance in Teleoperation Tasks

In this paper, we present a novel teleoperation approach that significantly enhances the efficiency and intuitiveness of remote operations. Recognizing the challenges inherent in direct human control of robots, particularly for complex tasks, we explore the concept of Shared Autonomy (SA) as a means to bridge the gap between human cognitive abilities and robotic precision. Key to our approach is the implementation of a Diffusion Model (DM) as a Motion Primitive (MP) generator. This model excels at forecasting and extending the operator's trajectory towards the intended goal, enabling the robot to assist in task completion proactively. We outline the development of a versatile teleoperation framework designed for broad applicability, alongside the practical application of DM for learning and predicting motion sequences. The efficacy and innovation of our system are demonstrated through experiments conducted with real manipulator, showcasing the effectiveness of the proposed framework.

Details coming soon