Abstract-We present a randomized kinodynamic planner that solves rearrangement planning problems. We embed a physics model into the planner to allow reasoning about interaction with objects in the environment. By carefully selecting this model, we are able to reduce our state and action space, gaining tractability in the search. The result is a planner capable of generating trajectories for full arm manipulation and simultaneous object interaction. We demonstrate the ability to solve more rearrangement by pushing tasks than existing primitive based solutions. Finally, we show the plans we generate are feasible for execution on a real robot.
Abstract-In this work we present a fast kinodynamic RRT-planner that uses dynamic nonprehensile actions to rearrange cluttered environments. In contrast to many previous works, the presented planner is not restricted to quasi-static interactions and monotonicity. Instead the results of dynamic robot actions are predicted using a black box physics model. Given a general set of primitive actions and a physics model, the planner randomly explores the configuration space of the environment to find a sequence of actions that transform the environment into some goal configuration.In contrast to a naive kinodynamic RRT-planner we show that we can exploit the physical fact that in an environment with friction any object eventually comes to rest. This allows a search on the configuration space rather than the state space, reducing the dimension of the search space by a factor of two without restricting us to non-dynamic interactions.We compare our algorithm against a naive kinodynamic RRT-planner and show that on a variety of environments we can achieve a higher planning success rate given a restricted time budget for planning.
We address the problem of motion planning for a robotic manipulator with the task to place a grasped object in a cluttered environment. In this task, we need to locate a collisionfree pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot manipulator and c) optimizes a user-given placement objective. Because of the placement objective, this problem is more challenging than classical motion planning where the target pose is defined from the start. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning for the robot manipulator with a novel hierarchical search for suitable placement poses. We evaluate our approach on a dualarm robot for two different placement objectives, and observe its effectiveness even in challenging scenarios.
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