Building autonomy by embuing hard problems with exploitable structures
Hi! I am Akshay. I develop new classes of optimization solvers that exploit problem structure and geometric representations, enabling scalable approaches to mathematically hard problems. Through projects such as Galileo for legged locomotion, PAAMP for long-horizon planning, differentiable collision detection for trajectory optimization, and Orthogonal Trust Region formulations for safe navigation, I have bridged rigorous theory with real-time autonomy. I now aim to extend these insights to exciting problems like navigation in hazardous environments, cooperative manipulation, and autonomous construction, advancing autonomy in complex systems where structure can be harnessed.
Also known as: Akshay, Jaitly
Publications
Papers and ResearchAnalytic Conditions for Differentiable Collision Detection in Trajectory Optimization
Jaitly, A. Jha, K D. Ota, K. Shirai, Y.
(IROS 25)
PAAMP: Polytopic Action-Set and Motion Planning for Long Horizon Dynamic Motion Planning via Mixed Integer Linear Programming
Jaitly, A. Farzan, S.
(IROS 24)
Galileo: An Efficient Pseudospectral Collocation Framework for Legged Robots
Chandler, E. Jaitly, A. Agheli, M.
(ICRA @40)
Video → · ArXiv → · Project page → · GitHub →
A MILP-Based Framework for Coordinated Multi-Agent Motion Planning and Collision Avoidance in Constrained Environments
Farzan, S. Jaitly, A. Cline J.
(CASE 25)
Experience
Aerial Control and Perception Lab. WPI - Visiting Researcher
● Created a new, speedy, solver for the Trust Region Problem with proof of convergence.
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● Created new parameterizations of free space, allowing trajectory optimization to be solved as an Orth-TRP, faster than established methods.
Mitsubishi Electric Research Labs (MERL) - Optimization and Intelligent Robotics (Under NDA) — Research Intern
- Outperformed other state-of-the-art methods like DCOL (Tracy et all).
- Led to a publication in IROS ‘25 and a patent. See “Publications” for specific contributions.
● Learning Traffic for Elevator Scheduling
- Learning based Multi-variate time-series prediction with improved synthetic data generation.
- Dynamic-programming based algorithm to perform optimal job scheduling.
Boston Dynamics - Spot Manipulation Software Engineering Intern
● Worked with image processing & legged control techniques to enhance camera calibration.
Autonomous Loco-Manipulation Systems Group. WPI - Research
● This work (>60 stars) was used in other projects, including HURON (humanoid robots) and BiQu (quadrupedal robot loco-manipulation).
Featured Projects
Polytopic Action & Motion Planning (PAAMP)
Linearizing long-horizon dynamic motion planning with learned polytopes.
Multi-Contact Planning on Unitree Go1
Perceptive locomotion: walking, hopping, trotting with switched-system collocation.
LLAMA-Q: A C++ Library to Abstract and Generalize Robot Control
MIT THINK award finalist; granted a presentation slot at MakerFaire 2020.
Degen_vert2lcon: A MATLAB library to find the Convex Hull of points that lie in an affine subspace
Published on MATLAB file exchange, this addressed limitations of existing methods for finding convex hulls.
Trajectory Tracking for Quadrotor
Robust Trajectory Tracking for Quadrotor using Sliding Mode Control.
Distributed Task Allocation for Communication in Intermittent Swarms
Solving a distributed optimization with analytic bounds on communication frequency.
Wirebot Platform
A < $50 platform for speedy locomotion in large indoor spaces.
Hephaestus Arm Control
Implementing and using Vision, Position/Velocity Kinematics, Trajectory Planning, Communication to enable robot control.
Teaching
● Oversaw completion of student projects, including satellite localization algorithms and prosthetics development.
● The XRP project has been used to teach robotics worldwide.
About
Hi! I am Akshay. I develop new classes of optimization solvers that exploit problem structure and geometric representations, enabling scalable approaches to mathematically hard problems. Through projects such as Galileo for legged locomotion, PAAMP for long-horizon planning, differentiable collision detection for trajectory optimization, and Orthogonal Trust Region formulations for safe navigation, I have bridged rigorous theory with real-time autonomy. I now aim to extend these insights to exciting problems like navigation in hazardous environments, cooperative manipulation, and autonomous construction, advancing autonomy in complex systems where structure can be harnessed.
Interests: Optimization, Algebraic Geometry, Motion Planning, Switched Systems, Underactuated Control.