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2025-12-15 06:24:04
raw text
Rope Manipulation Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation Ashvin Nair* , Dian Chen* , Pulkit Agrawal* , Phillip Isola , Pieter Abbeel , Jitendra Malik , Sergey Levine *Equal Contribution Abstract We present a system where a robot takes as input a sequence of images of a human manipulating a rope from an initial to goal configuration, and outputs a sequence of actions that can reproduce the human demonstration, using only monocular images as input. To perform this task, the robot learns a pixel-level inverse dynamics model of rope manipulation directly from images in a self-supervised manner, using more than 30K interactions with the rope collected autonomously by the robot. The human demonstration provides a high-level plan of what to do and the low-level inverse model is used to execute the plan. We show that by combining the high and low-level plans, the robot can successfully manipulate a rope into a variety of target...
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