CLASP: Language-Driven Robot Skill Selection and Composition using Task-Parameterized Learning
arXiv:2606.08169v1 Announce Type: cross Abstract: Enabling robots to understand and execute tasks from natural language commands while maintaining data efficiency remains challenging. Foundation models such as vision-language-action (VLA) and vision-language models (VLMs) provide intuitive interaction channels but require extensive data; task-parameterized imitation learning achieves data efficiency but lacks natural language grounding. This work bridges this gap through a modular architecture combining task-parameterized kernelized movement primitives (TP-KMPs) with pretrained VLMs. During le
This work is emerging now due to the ongoing need to bridge the gap between data-efficient robot learning and intuitive natural language interaction, leveraging recent advancements in both foundation models and task-parameterized learning.
A strategic reader should care because this development addresses a core challenge in robotics: enabling robots to learn complex tasks from language with less data, paving the way for more versatile and easily programmable robotic systems.
This paper demonstrates a method to combine the natural language understanding of large language models with the data efficiency of task-parameterized learning, which could accelerate the deployment of intelligent robots in diverse environments.
- · Robotics companies
- · Automation sector
- · AI researchers
- · Industrial manufacturing
- · Companies relying on expensive, manual robot programming
- · Legacy automation firms resistant to AI integration
More intuitive and data-efficient programming of robotic tasks using natural language.
Accelerated development and adoption of robots in complex, unstructured environments due to reduced training time and cost.
Enhanced human-robot collaboration in various sectors, leading to significant productivity gains and new service models.
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Read at arXiv cs.LG