Playful Agentic Robot Learning
- Published
- Source
- arXiv
- Paper number
- 457
- Field
- Robotics
- arXiv ID
- 2606.19419
Key points
- During play time, the system autonomously proposes tasks, executes them, verifies the results, diagnoses failures, and accumulates a skill library.
- It uses a novelty-learnability rule to propose curiosity-driven tasks, with curiosity play improving from 24.7 percent under random play to 32.3 percent.
- On LIBERO-PRO, it improves by 20.6 points over CaP-Agent0, from 23.2 percent to 43.8 percent, and exceeds every VLA baseline, including pi0.5 at 12.8 percent.
- When the learned skills are plugged into CaP-Agent0, they improve RoboSuite by 8.9 points and real-robot performance by 8.8 points.
- Skills learned in a single-arm environment also transfer to two-arm tasks, such as two-arm lifting, with a 24.0-point gain.
- Curiosity play and execution-system improvements are complementary, and both together reach the best score of 44.3 percent.
Paper links
External research summaries. These are not HDATF publications or measured product results.