강인 지능 및 로보틱스 연구실

We aim to develop fundamental learning methodologies for whole-body manipulation using humanoid robots, including a humanoid platform (e.g., Unitree G1) and a mobile humanoid platform (T.B.D). Whole-body manipulation has recently emerged as a key frontier in robotics. To lead advancements in this domain, the RIRO Lab focuses on two core directions: (1) developing novel policy learning methods based on imitation and reinforcement learning, and (2) designing high-level planning strategies that leverage large language models (LLMs) and other foundation models such as vision-language-action (VLA) models. Our long-term goal is to build hierarchical foundation models capable of generalizing across complex, real-world tasks. The proposed methods will be rigorously evaluated using our robotic platforms and through collaborative efforts with industry partners. In-hand manipulation of deformable objects offers unprecedented opportunities to resolve various real-world problems, such as binding and taping. This project aims to develop a visuotactile in-hand manipulation that repositions/reorientations deformable objects in hand as we want. Toward this line of research, we propose three research thrusts: 1) a physics-informed reinforcement learning (RL) framework, 2) an interactive RL framework, and 3) Sim2Real transfer learning method. We aim to introduce task-and-motion planning (TAMP) framework that is to solve complex and longer-time horizon of human tasks. To resolve completeness, optimality, and robustness issues, we are working on various task planning and motion planning approaches. We will show a generalizable TAMP framework under human operator’s cooperative or adversarial interventions. Natural language is one of the most intuitive and accessible ways for humans to convey