We work on learning-based trajectory planning: training neural networks to produce onboard, in milliseconds, the motions a classical planner needs seconds to compute, and to keep doing so outside the simulator they were trained in. Tied closely to this is our work on imposing hard constraints on neural networks, so that a learned policy satisfies safety and dynamic limits by construction — for any weights and any input — rather than merely being penalised when it breaks them, which is what allows a learned planner to inherit the guarantees its classical counterpart came with. We also work on optimization-based trajectory planning, both in its own right and as the source of the supervision and the guarantees the learned methods are asked to preserve. Most of this flies, on aerial robots moving through unknown, cluttered and dynamic environments, and increasingly walks, on legged robots — along with the state estimation all of it rests on.
DiffSim2Real
Deploying Quadrupedal Locomotion Policies Purely Trained in Differentiable Simulation
Paper DiffOpt, CoRL 2024
Continuous-Time State Estimation
Continuous-Time State Estimation Methods in Robotics: A Survey
Paper T-RO
PRIMER
Perception-Aware Learning-based Multiagent Trajectory Planner
Paper ICRA 2025
Tutorial
Optimization, Optimal Control, Trajectory Optimization, and Splines
Video
RMADER Planner
Decentralized and Asynchronous Multiagent Trajectory Planner Robust to Comm. Delay
Best Poster Award in CAMRS Workshop (ICRA)
RA-L Paper ICRA Paper Video Code RA-L and ICRA 2023
PhD Thesis
Trajectory Planning for Flights in Multiagent and Dynamic Environments
Video of the defense Dissertation
NeBula Autonomy
DARPA Subterranean Challenge
1st place in Urban Circuit (Darpa Subt. Challenge)
2nd place in Tunnel Circuit (Darpa Subt. Challenge)
Paper Video Code JFR
FASTER Planner
Fast and Safe Trajectory Planner for Navigation in Unknown Environments.
Finalist: Best Paper Award in Search and Rescue Robotics
IROS Paper T-RO Paper Video Code T-RO and IROS 2019
Aerial Manipulation
Agile Plate Transport with a Hexacopter with Canted Motors.
Video
Reinforcement Learning vs Opt. Control
A numerical comparison of performance and robustness in model-based and model-free methodologies.
Paper
Robotic Manipulator
Robotic Manipulator design and building using Arduino and LabView.
Video
FPGA and VHDL
Schematic design and implementation on a FPGA.
Schematic designed