2025 Spring VIP Projects

Mentor name: Zhaoyuan Gu (zgu78@gatech.edu)

Project Description: In this project, we will explore the state-of-the-art machine-learning approaches that enable our humanoid robot to perform a series of locomotion and manipulation tasks, such as pushing through a spring-loaded door. Instead of traditional reinforcement learning, we will explore the diffusion model, an imitation learning technique that has been shown to achieve versatile skills.

Mentor name: Fukang Liu (fukangliu@gatech.edu)

Project Description: This project explores the capabilities of humanoid robots enhanced with supernumerary limbs. Potential tasks include opening doors while carrying a box, interacting with overhead areas, and using the extra arm as a contact point for extreme movements. The project will leverage diverse datasets (e.g., from model-based methods, model-free methods, motion capture, and video) to develop and refine the locomotion and manipulation skills of the Digit humanoid robot and its third arm. Additionally, the project aims to attach grippers to all three arms and investigate sequential collaborative tri-arm loco-manipulation tasks. Simulation will be conducted in IsaacLab, followed by testing on hardware. The scope includes both software development (policy training and deployment) and hardware design (third-arm prototyping and control).

Mentor name: Feiyang Wu (feiyangwu@gatech.edu)

Project Description: This project will train and deploy Reinforcement Learning agents on humanoid robots. We will develop our own training algorithm and use IsaacLab to train RL agents. We will conduct extensive hardware experiments on our Digit robot for locomotion tasks.

Mentor name: Wei Zhu (wzhu328@gatech.edu)

Project Description: We are going to develop novel social navigation algorithms based on deep reinforcement learning, enabling humanoid robots to safely and kindly interact with pedestrians on rough terrains. This project has three missions: simulating interactive motions for humans using either imitation learning or deep reinforcement learning in Isaac Lab; developing a stable and versatile locomotion controller for humanoid robots with deep reinforcement learning; and exploring a general social navigation policy for humanoids robots, enabling a socially-aware motion in human-rich environments with uneven terrains.

Mentor name: Yuntian Zhao (yzhao801@gatech.edu)

Project Description: This bipedal deformable terrain research is currently focused on reconfigurable foot design, and need students to cooperate in hardware and mechtronics deisgn, assembly, and testing. When the foot is ready, the experiment will be carried out on the Cassie hardware.

Mentor name: Yuntian Zhao, Ziyi Zhou (yzhao801@gatech.edu, zhouziyi@gatech.edu)

Project Description: This project will use a hierarchy approach to solve the multi-robot cooperative loco-manipulation through model-based methods and formal methods. The highest level reference trajectory generation will use formal methodto generative collision-free, kinematics feasible, long horizonal trajectory towrads the goal; while the middle level predictive planner will use simplified dyanmics model (for now, SRBM) to generated a fixed time horizon force input trajectory to track the reference trajectory, while makes sure dynamics feasible; the lowest level controller is a whole-body dynamics reactive controller, which use hierarchy inverse dynamics to command joint motors to track the force input trajectory, while makes sure the robot is still dynamics feasible, and deals with priority issues. The project will have highest planner running in stlpy, while middle level MPC running in OCS2, while lowest WBC modified from legged_control. However, this project will not only aim at two quadruped, but aim at multi-robots, say a humanoid and a quadruped. When the framework is ready in simulation, it will be tested on real hardware, i.e., B1Z1 quadruped w/ robot arm, and Digit.

Mentor name: Ziwon Yoon (zyoon6@gatech.edu)

Project Description: Recent advances in bipedal robotics have driven significant progress, yet traversability and autonomous navigation for bipedal locomotion remain underexplored. Although bipedal robots have distinct advantages, including the ability to navigate diverse terrains and manipulate objects, they are more prone to instability and falls compared to mobile or quadrupedal robots. To address these challenges, we developed a Stability-Aware Traversability Estimation and Navigation framework for bipedal robots. Its learning-based traversability estimator helps the navigation stack to plan optimal paths that keep the robot within the desired stability limits. We validated it through data collected on challenging terrains—such as rough, sloped, and deformable surfaces—demonstrating its effectiveness in both simulated and real-world environments.

Mentor name: Jaehwi Jang

Project Description:

The goal of this research is the development of a whole-body control method for humanoid robots for collaborative transportation tasks with humans. Most recent developments in real-world humanoid control have focused on single-agent motion in open environments. Our work extends these capabilities to support dynamic, responsive teamwork in complex, interactive environments.

Our approach combines imitation learning with social skill learning to enable robots to effectively coordinate with humans. We are developing the Sim2Real framework for transfer to a real-world collaborative task.