A full publication list, sortable by topic area, is available here.
The Legged Controls Group focuses on unlocking the potential of legged platforms to navigate unstructured environments. We promote agile and robust locomotion through computationally efficient algorithms that allow the robot to quickly reason about its abilities and surroundings. We focus on all aspects of this challenge, from high-level planning to low-level control, perception and mapping to state estimation and learning. Some of our recent works include:
fast global motion planning,
MPC for legs and tails,
and trajectory optimization.
Hybrid System Modeling, Estimation, and Control
This project seeks to further our understanding of how states evolve in hybrid systems to improve state estimation and control algorithms. Much of the recent work in this direction has focused on the use of saltation matrices, which describe the instantaneous uncertainty updates which occur during hybrid events such as leg touchdown. Some of the associated publications for this research direction include:
hybrid event shaping.
Learning to Drive Off-Road
Many real world tasks require robots to navigate through unstructured outdoor environments. These environments often include rough terrain with unavoidable obstacles, such as rocks, and unknown physical properties, such as contact friction. Autonomous navigation through such environments is difficult as the robot experiences complex, non-static, and divergent robot dynamics. We aim to enable off-road driving by having the robot learn from previous driving experience in real world and simulated environments to gain a probabilistic understanding of its dynamics in new environments. Given this probabilistic understanding, the robot can then perform robust decision making to find safe routes over difficult terrain to its destination.
Terramechanics and Environmental Monitoring
We want robots that can not just move over terrain but actually interact with the environment to enable planetary exploration and environmental monitoring applications. To achieve this, we work to develop new physical models of how the robot interacts with terrain features like soil and underbrush, new behaviors that leverage these robot-terrain interactions, and full systems that can go out and robustly work in natural environments. For example, nonprehensile terrain manipulation seeks to leverage incidental robot-terrain interactions for intentional manipulation of the environment, such as digging trenches with a rover's wheel.
The lab has many projects focused on robot designs that can work well in all sorts of environments. Some projects focus on individual components, like
hands, while others work on full systems, often with the goal of designing robots that are good at multiple different things. Past projects include
low cost hexapods,
springy climbing legs,
and a flamingo-inspired balancing robot.
Current projects include small
passive dynamics inspired walkers,
a lightweight climbing robot capable of ascending uneven cliff faces using microspine grippers, and multi-behavior design strategies to allow for more integrated limbs.