86 citations · 117 across the 7 of their papers we have counts for
8 papers
Planning for Manipulation among Movable Objects: Deciding Which Objects Go Where, in What Order, and How
Dhruv Saxena, Maxim Likhachev
We are interested in pick-and-place style robot manipulation tasks in cluttered and confined 3D workspaces among movable objects that may be rearranged by the robot and may slide,…
Planning for Complex Non-prehensile Manipulation Among Movable Objects by Interleaving Multi-Agent Pathfinding and Physics-Based Simulation
Dhruv Mauria Saxena, Maxim Likhachev
Real-world manipulation problems in heavy clutter require robots to reason about potential contacts with objects in the environment. We focus on pick-and-place style tasks to retri…
AMRA*: Anytime Multi-Resolution Multi-Heuristic A*
Dhruv Mauria Saxena, Tushar Kusnur, Maxim Likhachev
Heuristic search-based motion planning algorithms typically discretise the search space in order to solve the shortest path problem. Their performance is closely related to this di…
Manipulation Planning Among Movable Obstacles Using Physics-Based Adaptive Motion Primitives
Dhruv Mauria Saxena, Muhammad Suhail Saleem, Maxim Likhachev
Robot manipulation in cluttered scenes often requires contact-rich interactions with objects. It can be more economical to interact via non-prehensile actions, for example, push th…
Search-based Planning for Active Sensing in Goal-Directed Coverage Tasks
Tushar Kusnur, Dhruv Mauria Saxena, Maxim Likhachev
Path planning for robotic coverage is the task of determining a collision-free robot trajectory that observes all points of interest in an environment. Robots employed for such tas…
Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
Sangjae Bae, Dhruv Saxena, Alireza Nakhaei +3
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers. This paper focuses on h…
Driving in Dense Traffic with Model-Free Reinforcement Learning
Dhruv Mauria Saxena, Sangjae Bae, Alireza Nakhaei +2
Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle-…
A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction
Tushar Kusnur, Shohin Mukherjee, Dhruv Mauria Saxena +4
Planning for multi-robot coverage seeks to determine collision-free paths for a fleet of robots, enabling them to collectively observe points of interest in an environment. Persist…