Jia Pan

TL;DR: A sequential convex optimization procedure, which penalizes collisions with a hinge loss and increases the penalty coefficients in an outer loop as necessary, and an efficient formulation of the no-collisions constraint that directly considers continuous-time safety are presented.

TL;DR: A new collision and proximity library that integrates several techniques for fast and accurate collision checking and proximity computation and is based on hierarchical representations and designed to perform multiple proximity queries on different model representations.

TL;DR: This work presents a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity and demonstrates that the learned policy can be well generalized to new scenarios that do not appear in the entire training period.

TL;DR: This work presents a novel optimization-based algorithm for motion planning in dynamic environments that uses a stochastic trajectory optimization framework to avoid collisions and satisfy smoothness and dynamics constraints.

TL;DR: A decentralized sensor-level collision-avoidance policy for multi-robot systems, which enables a robot to make effective progress in a crowd without getting stuck and has been successfully deployed on different types of physical robot platforms without tedious parameter tuning.