20 citations · 36 across the 4 of their papers we have counts for
12 papers
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…
Exploration in Action Space
Anirudh Vemula, Wen Sun, J. Andrew Bagnell
Parameter space exploration methods with black-box optimization have recently been shown to outperform state-of-the-art approaches in continuous control reinforcement learning doma…
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…