CAREER: Concurrent Robot Learning from Simulation and Real for Closing the Sim-to-real Gap

Legged robots, like robot dogs or humanoid robots, offer the possibility of being able to move well in indoor and outdoor tasks, such as rearranging furniture at home or monitoring factories. However, controlling these robots is difficult and comes with the risk of robots falling. In recent years, artificial intelligence (AI) has shown promise in addressing the falling challenge by using computer simulations when teaching robots how to walk. However, the robot behaviors learned in simulation often underperform in the real world due to the difference between the simulation and the real physical robot. This Faculty Early Career Development (CAREER) project supports research that investigates a novel approach that simultaneously learns from both simulated and physical experiments. This novel approach in this project aims to leverage the advantage of simulations, such as scalability, while using the data from real robots. Ultimately, the project will maximize the potential of AI and robot learning algorithms to allow more capable and safer robots. Additionally, the project incorporates educational activities to engage students with real-world robot learning environments, fostering a broader understanding of robotics. This project will investigate a novel class of learning algorithms, Learning from Multiverse (LfM), which concurrently learns from both large-scale, inexpensive physics-based simulation and expensive, real world ground-truths to bridge the well-known “sim-to-real” gap in legged robots. The fundamental principle of the project involves seamless and continuous learning from both simulated and real experiences, as well as structured mathematical reasoning of experimental data. This approach differs from the majority of existing learning algorithms that rely solely on either simulation or real-world experience. This research involves the development of three main components: (i) autonomous and safe learning environments in the real world, (ii) novel robot learning algorithms that simultaneously leverage simulation and reality, and (iii) explainable artificial intelligence to understand hardware experiments. This project will facilitate the development of challenging motor skills for safety-critical legged robots, such as quadrupedal robots with manipulators and bipeds, while ensuring effective, efficient, and safe robot learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.