TaskSeg: Task-Specific Object Segmentation Through Demonstrations | Teeratham Vitchutripop
TaskSeg: Task-Specific Object Segmentation Through Demonstrations
Teeratham Vitchutripop, RISS 2023 Cohort
Carnegie Mellon University
Robotics Institute Summer Scholars: https://riss.ri.cmu.edu/
Relative placement tasks are a class of manipulation problems where one object, referred to as the “action object”, is moved to a pose and position relative to another object (the “anchor object”). Existing vision-based policy learning algorithms often assume an accurate segmentation of the action object from the perceived scene a priori. In real-world scenes, object segmentation in the context of manipulation tasks often requires collecting a large dataset of scenes, manually annotating them with segmentation masks, and training a supervised model to predict segmentation masks from raw images. However, this process is costly and infeasible when quickly adapting to new scenes or tasks. We propose TaskSeg, a novel architecture that learns to segment the action object through unlabeled video demonstrations. A core assumption we make is that, in many robotic tasks, the action object is the object which experiences the most motion during a demonstration compared to other objects within the scene. Our method trains a standard semantic segmentation neural network that uses optical flow as a source of supervision, thus requiring zero human annotation.
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Carnegie Mellon University’s Robotics Institute is committed to opening doors and creating opportunities for future leaders in robotics. Carnegie Mellon University is home to the top-ranked School of Computer Science, the world’s first university robotics department, the world’s first Ph.D. in robotics, and the largest university-affiliated robotics research group. Launched in 2006, CMU’s Robotics Institute Summer Scholars (RISS) program (http://riss.ri.cmu.edu/)comprises a ten-week summer undergraduate research program that immerses a diverse cohort of scholars in cutting-edge robotics and extensive post-program mentoring. The program provides opportunities for students from across the country and the world to conduct research with leaders in the field. The program aspires to foster a diverse and inclusive working and learning environment where all students enjoy the educational benefits of diversity and are actively welcomed, included, and supported by the community. The quality and breadth of research, high level of institute and university engagement, and powerful professional development programming, graduate school application counseling, and alumni network create transformative experiences and remarkable post-program trajectories.
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