Guided Decoding for Robot On-line Motion Generation and Adaption
This work addresses motion generation for high-degree-of-freedom robots, enabling real-time adaptation to new tasks and obstacles, though it appears incremental as it builds on existing learning-from-demonstration and transformer methods.
The paper tackles the problem of generating adaptable motion for robot arms in complex settings by learning from demonstrations, resulting in a model that successfully generates and adapts trajectories online to obstacles and constraints across different robotic platforms.
We present a novel motion generation approach for robot arms, with high degrees of freedom, in complex settings that can adapt online to obstacles or new via points. Learning from Demonstration facilitates rapid adaptation to new tasks and optimizes the utilization of accumulated expertise by allowing robots to learn and generalize from demonstrated trajectories. We train a transformer architecture, based on conditional variational autoencoder, on a large dataset of simulated trajectories used as demonstrations. Our architecture learns essential motion generation skills from these demonstrations and is able to adapt them to meet auxiliary tasks. Additionally, our approach implements auto-regressive motion generation to enable real-time adaptations, as, for example, introducing or changing via-points, and velocity and acceleration constraints. Using beam search, we present a method for further adaption of our motion generator to avoid obstacles. We show that our model successfully generates motion from different initial and target points and that is capable of generating trajectories that navigate complex tasks across different robotic platforms.