Transferring Kinesthetic Demonstrations across Diverse Objects for Manipulation Planning

Given a demonstration of a complex manipulation task, such as pouring liquid from one container to another, we seek to generate a motion plan for a new task instance involving objects with different geometries. This is nontrivial since we need to simultaneously ensure that the implicit motion constraints are satisfied (glass held upright while moving), that the motion is collision-free, and that the task is successful (e.g., liquid is poured into the target container). We solve this problem by identifying the positions of critical locations and associating a reference frame (called motion transfer frames) on the manipulated object and the target, selected based on their geometries and the task at hand. By tracking and transferring the path of the motion transfer frames, we generate motion plans for arbitrary task instances with objects of different geometries and poses. We show results from simulation as well as robot experiments on physical objects to evaluate the effectiveness of our solution. A video supplement is available on YouTube: https://youtu.be/RuG9zMXnfR8

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Published
2025-10-19
DOI
https://doi.org/10.1109/iros60139.2025.11246024
Primary Topic
Robot Manipulation and Learning
Type
article
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article

Transferring Kinesthetic Demonstrations across Diverse Objects for Manipulation Planning

I. V. Ramakrishnan, Aditya Patankar, Dibyendu Das, Nilanjan Chakraborty et al.
Robot Manipulation and Learning
article

Transferring Kinesthetic Demonstrations across Diverse Objects for Manipulation Planning

I. V. Ramakrishnan, Aditya Patankar, Dibyendu Das, Nilanjan Chakraborty, C. R. Ramakrishnan
article en

Abstract

Given a demonstration of a complex manipulation task, such as pouring liquid from one container to another, we seek to generate a motion plan for a new task instance involving objects with different geometries. This is nontrivial since we need to simultaneously ensure that the implicit motion constraints are satisfied (glass held upright while moving), that the motion is collision-free, and that the task is successful (e.g., liquid is poured into the target container). We solve this problem by identifying the positions of critical locations and associating a reference frame (called motion transfer frames) on the manipulated object and the target, selected based on their geometries and the task at hand. By tracking and transferring the path of the motion transfer frames, we generate motion plans for arbitrary task instances with objects of different geometries and poses. We show results from simulation as well as robot experiments on physical objects to evaluate the effectiveness of our solution. A video supplement is available on YouTube: https://youtu.be/RuG9zMXnfR8

Stony Brook University (US)
Openalex Percentile: Top 54%
Robot Manipulation and Learning
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