arXiv:2607.00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures. We introduce ASPIRE (Agentic Skill Programming through Iterative Robot Exploration), a continual learning system that autonomously writes and refines robot control programs in a code-as-policy paradigm while compounding experience into a reusable skill library. ASPIRE discovers skills that persist across tasks, simulation and real-world settings, and embodi
Advances in AI models and computational power are enabling more sophisticated approaches to autonomous system development, moving beyond traditional programming paradigms.
This development addresses a fundamental challenge in robotics by enabling autonomous skill acquisition, which is critical for scalable and flexible robot deployment across diverse environments.
Robots can now autonomously learn and refine complex manipulation and interaction skills, reducing the need for costly and time-consuming human programming for each new task.
- · Robotics companies
- · Logistics and manufacturing sectors
- · AI software developers
- · Hardware developers for advanced robotics
- · Traditional robot programmers
- · Companies reliant on highly specialized, single-task robotic solutions
ASPIRE could significantly accelerate the development and deployment of general-purpose robots in various industries.
Increased robot autonomy may lead to labor market shifts as more complex tasks become automatable, necessitating new skill sets for human workers.
A future where robots are capable of self-improving and adapting skills could blur the lines between machine and human intelligence, raising ethical and societal questions about intelligent agency.
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Read at arXiv cs.AI