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Found 5 papers, 2 papers with code
Planning for Complex Non-prehensile Manipulation Among Movable Objects by Interleaving Multi-Agent Pathfinding and Physics-Based Simulation
In particular, our motivation is to allow the robot to reason over and consider non-prehensile rearrangement actions that lead to complex robot-object and object-object interactions where multiple objects might be moved by the robot simultaneously, and objects might tilt, lean on each other, or topple.
AMRA*: Anytime Multi-Resolution Multi-Heuristic A*
A fine discretisation allows for better approximations of the continuous search space, but makes the search for a solution more computationally costly.
Manipulation Planning Among Movable Obstacles Using Physics-Based Adaptive Motion Primitives
Unfortunately, it is infeasible to query the simulator for thousands of actions that need to be evaluated in a typical planning problem as each simulation is time-consuming.
Robotics
Driving in Dense Traffic with Model-Free Reinforcement Learning
Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction
We evaluate our framework in simulation as well as the real world.
