About Us
The Computational methods for Curious Robots Lab is a robotics research group focused on the development of rigorous mathematical, algorithmic, and optimization methods for robotic learning and control led by Dr. Ian Abraham.
Our research breakthroughs are deployed on a wide range of diverse robotic systems to expand their utility and overall success in the most extreme and remote environments. Check out our ongoing projects below!
Ongoing Research Projects
Theory of Search & Exploration
We develop theory and algorithms for ergodic control, steering robots to spend time in a space in proportion to its information or coverage value. This gives robots principled, scalable strategies for search and exploration under real-world constraints like limited sensing, energy, and time.
- Time- and scale-optimal coverage
- Exploration over complex and non-Euclidean domains
- Safety- and reachability-guaranteed exploration
Structured Learning and Control
We combine model-based structure with learning, using data-driven dynamics models, differentiable simulation, and distributionally robust control, so robots can plan and act reliably, from contact-rich manipulation to legged locomotion and long-term field deployment.
- Data-driven dynamics via Koopman operators
- Contact-rich manipulation through differentiable simulation
- Distributionally robust and Stein-variational control for uncertain and field environments
Collective Intelligence
We study how teams of robots coordinate to search, explore, and act together, developing multi-agent ergodic control and coordination algorithms for heterogeneous robot teams.
- Multi-agent ergodic exploration under sensing/visibility constraints
- Multi-objective coordination via branch-and-bound and Pareto-optimal methods
- Diverse, decentralized search strategies across robot teams
Try It Yourself
Drag around one of our robots, then get a feel for the idea behind our ergodic search research by marking points of interest and watching a search agent balance lingering where it matters against covering the rest of the space.
Interested in Joining? We are currently recruiting!
Those interested in joining our group are expected to have strong competency is programming, optimization, systems and control theory, kinematics and dynamics of robotic systems, and rigorous mathematical theory and proof writing. Candidates should have a strong curiosity and drive to develop algorithms and deploy them on physical robots to expand the capabilities, analyze their performance, and prove reliability through mathematical rigor and repeated experimentation.
Other useful skills:
- ROS 1/2 or LCM experience
- Extensive Python and C/C++ coding experience
- Exposure to modern ML/AI autodiff libraries like JAX/PyTorch
- Excellent speaking/writing abilities
If this sounds like you then please submit an expression of interest (EOI) via this form. Make sure to list Dr. Ian Abraham, School of Electrical and Computer Engineering as a potential advisor if possible.