7 accepted papers
Robust motion planning under uncertainty is critical for unlocking real-world robotics applications. This paper introduces SupeR-MPC, a computationally-efficient, sensitivity-aware, chance-constrained optimization framework that systematically accounts for multiple sources of uncertainty, including
The hybrid discrete/continuous nature of task and motion planning (TAMP) results often in a combinatorial explosion. This challenge is even more pronounced in multi-robot TAMP problems due to the increase in dimensionality of the action space. Previous works use action feasibility prediction as a he…
This letter addresses the problem of generating robust and accurate trajectories taking into account uncertainties in the robot dynamic model. Based on the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">closed-loop sensitivity</i>, which q
Closed-loop state sensitivity [1], [2] is a recently introduced notion that can be used to quantify deviations of the closed-loop trajectory of a robot/controller pair against variations of uncertain parameters in the robot model. While local optimization techniques are used in [1], [2] to generate…
In Task and motion planning (TAMP), symbolic search is combined with continuous geometric planning. A task planner finds an action sequence while a motion planner checks its feasibility and plans the corresponding sequence of motions. However, due to the high combinatorial complexity of discrete sea…
In this paper, we address task and motion plan-ning (TAMP) which is an important yet challenging robotics problem. It is known to suffer from the high combinatorial complexity of discrete search, often requiring a large number of geometric planning calls. We build upon recent works in TAMP by taking…
The overall performance of sampling-based motion planning algorithms strongly depends on the use of suitable sampling and connection strategies, as well as on the accuracy of the distance metric considered to select neighbor states. Defining appropriate strategies and metrics is particularly hard wh…