4 accepted papers
Batch planning is increasingly necessary to quickly produce diverse and quality motion plans for downstream learning applications, such as distillation and imitation learning. This paper presents Global Tensor Motion Planning (GTMP)---a sampling-based motion planning algorithm comprising only tensor…
Batch planning is increasingly necessary to quickly produce diverse and quality motion plans for downstream learning applications, such as distillation and imitation learning. This paper presents Global Tensor Motion Planning (GTMP)—a sampling-based motion planning algorithm comprising only tensor o
Motion planning is still an open problem for many disciplines, e.g., robotics, autonomous driving, due to their need for high computational resources that hinder real-time, efficient decision-making. A class of methods striving to provide smooth solutions is gradient-based trajectory optimization. H…
We evaluate the potential of Guided Policy Search (GPS), a model-based reinforcement learning (RL) method, to train a robot controller for human-robot object handovers. Handovers are a key competency for collaborative robots and GPS could be a promising approach for this task, as it is data efficien