CLIP-Motion: Learning Reward Functions for Robotic Actions Using
Consecutive Observations
- URL: http://arxiv.org/abs/2311.03485v1
- Date: Mon, 6 Nov 2023 19:48:03 GMT
- Title: CLIP-Motion: Learning Reward Functions for Robotic Actions Using
Consecutive Observations
- Authors: Xuzhe Dang and Stefan Edelkamp and Nicolas Ribault
- Abstract summary: This paper presents a novel method for learning reward functions for robotic motions by harnessing the power of a CLIP-based model.
Our approach circumvents this challenge by capitalizing on CLIP's capability to process both state features and image inputs effectively.
- Score: 1.03590082373586
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents a novel method for learning reward functions for robotic
motions by harnessing the power of a CLIP-based model. Traditional reward
function design often hinges on manual feature engineering, which can struggle
to generalize across an array of tasks. Our approach circumvents this challenge
by capitalizing on CLIP's capability to process both state features and image
inputs effectively. Given a pair of consecutive observations, our model excels
in identifying the motion executed between them. We showcase results spanning
various robotic activities, such as directing a gripper to a designated target
and adjusting the position of a cube. Through experimental evaluations, we
underline the proficiency of our method in precisely deducing motion and its
promise to enhance reinforcement learning training in the realm of robotics.
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