20 citations · 36 across the 6 of their papers we have counts for
11 papers · 1 filter
On the Effectiveness of Iterative Learning Control
Anirudh Vemula, Wen Sun, Maxim Likhachev +1
Iterative learning control (ILC) is a powerful technique for high performance tracking in the presence of modeling errors for optimal control applications. There is extensive prior…
Improved Soft Duplicate Detection in Search-Based Motion Planning
Nader Maray, Anirudh Vemula, Maxim Likhachev
Search-based techniques have shown great success in motion planning problems such as robotic navigation by discretizing the state space and precomputing motion primitives. However…
Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs
Manash Pratim Das, Anirudh Vemula, Mayank Pathak +2
Consider a truck filled with boxes of varying size and unknown mass and an industrial robot with end-effectors that can unload multiple boxes from any reachable location. In this w…
CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models
Anirudh Vemula, J. Andrew Bagnell, Maxim Likhachev
Given access to accurate dynamical models, modern planning approaches are effective in computing feasible and optimal plans for repetitive robotic tasks. However, it is difficult t…
TRON: A Fast Solver for Trajectory Optimization with Non-Smooth Cost Functions
Anirudh Vemula, J. Andrew Bagnell
Trajectory optimization is an important tool for control and planning of complex, underactuated robots, and has shown impressive results in real world robotic tasks. However, in ap…
Planning and Execution using Inaccurate Models with Provable Guarantees
Anirudh Vemula, Yash Oza, J. Andrew Bagnell +1
Models used in modern planning problems to simulate outcomes of real world action executions are becoming increasingly complex, ranging from simulators that do physics-based reason…