This paper presents an approach for learning motion planners that are accompanied with probabilistic guarantees of success on new environments that hold uniformly for any disturbance to the robot's dynamics within an admissible set.
The key idea behind our approach is to utilize the generative model in order to implicitly specify a prior over policies.
In this paper, we consider the problem of adapting a dynamically walking bipedal robot to follow a leading co-worker while engaging in tasks that require physical interaction.
Our goal is to perform out-of-distribution (OOD) detection, i. e., to detect when a robot is operating in environments that are drawn from a different distribution than the environments used to train the robot.
In this paper, we introduce a video prediction model where the equations of motion are explicitly constructed from learned representations of the underlying physical quantities.
Control policies from imitation learning can often fail to generalize to novel environments due to imperfect demonstrations or the inability of imitation learning algorithms to accurately infer the expert's policies.
We present a novel algorithm -- convex natural evolutionary strategies (CoNES) -- for optimizing high-dimensional blackbox functions by leveraging tools from convex optimization and information geometry.
This paper presents an approach for learning vision-based planners that provably generalize to novel environments (i. e., environments unseen during training).