20 citations · 52 across the 12 of their papers we have counts for
16 papers
Auditing Quality Filters for Long-Tail Human Data Curation
Rishav Agarwal, Nirshal Chandra Sekar, Anirudh Vemula
Robots on construction sites must detect workers who are kneeling or bending, which we call low poses. These workers can be lost from training datasets during automatic labeling. W…
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
Anirudh Vemula, Yuda Song, Aarti Singh +2
We propose a novel approach to addressing two fundamental challenges in Model-based Reinforcement Learning (MBRL): the computational expense of repeatedly finding a good policy in…
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…
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…
Task-Informed Fidelity Management for Speeding Up Robotics Simulation
Abhijeet Tallavajhula, Adrian Schoisengeier, Sung-Kyun Kim +3
Simulators are an important tool in robotics that is used to develop robot software and generate synthetic data for machine learning algorithms. Faster simulation can result in bet…
Planning, Learning and Reasoning Framework for Robot Truck Unloading
Fahad Islam, Anirudh Vemula, Sung-Kyun Kim +3
We consider the task of autonomously unloading boxes from trucks using an industrial manipulator robot. There are multiple challenges that arise: (1) real-time motion planning for…
Provably Efficient Imitation Learning from Observation Alone
Wen Sun, Anirudh Vemula, Byron Boots +1
We study Imitation Learning (IL) from Observations alone (ILFO) in large-scale MDPs. While most IL algorithms rely on an expert to directly provide actions to the learner, in this…
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective
Anirudh Vemula, Wen Sun, J. Andrew Bagnell
Black-box optimizers that explore in parameter space have often been shown to outperform more sophisticated action space exploration methods developed specifically for the reinforc…
Social Attention: Modeling Attention in Human Crowds
Anirudh Vemula, Katharina Muelling, Jean Oh
Robots that navigate through human crowds need to be able to plan safe, efficient, and human predictable trajectories. This is a particularly challenging problem as it requires the…
Modeling Cooperative Navigation in Dense Human Crowds
Anirudh Vemula, Katharina Muelling, Jean Oh
For robots to be a part of our daily life, they need to be able to navigate among crowds not only safely but also in a socially compliant fashion. This is a challenging problem bec…
Path Planning in Dynamic Environments with Adaptive Dimensionality
Anirudh Vemula, Katharina Muelling, Jean Oh
Path planning in the presence of dynamic obstacles is a challenging problem due to the added time dimension in search space. In approaches that ignore the time dimension and treat…