CLASP: Language-Driven Robot Skill Selection and Composition using Task-Parameterized Learning
Researchers have developed CLASP, a system that enables robots to understand and execute natural language commands by combining task-parameterized learning with pre-trained vision-language models. This approach allows robots to acquire skills from a small number of demonstrations and then use a VLM to interpret commands, select appropriate skills, and compose novel behaviors. CLASP also identifies its own capability gaps and requests targeted demonstrations without requiring model fine-tuning, achieving high success rates in complex scenarios. AI
IMPACT Enables robots to learn and perform tasks from natural language, potentially accelerating adoption in complex environments.