Imitation learning techniques aim to mimic human behavior in a given task. An agent (a
learning machine) is trained to perform a task from demonstrations by learning a mapping …

Modern nonlinear control theory seeks to endow systems with properties of stability and
safety, and have been deployed successfully in multiple domains. Despite this success …

When learning policies for real-world domains, two important questions arise:(i) how to
efficiently use pre-collected off-policy, non-optimal behavior data; and (ii) how to mediate …

Classical machine learning implicitly assumes that labels of the training data are sampled
from a clean distribution, which can be too restrictive for real-world scenarios. However …

We study the problem of imitation learning from demonstrations of multiple coordinating
agents. One key challenge in this setting is that learning a good model of coordination can …

We present Neural-Swarm2, a learning-based method for motion planning and control that
allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation …

We study the problem of programmatic reinforcement learning, in which policies are
represented as short programs in a symbolic language. Programmatic policies can be more …

Current state-of-the-art sports statistics compare players and teams to league average
performance. For example, metrics such as “Wins-above-Replacement”(WAR) in baseball …

In this paper, we present Neural-Swarm, a nonlinear decentralized stable controller for close-
proximity flight of multirotor swarms. Close-proximity control is challenging due to the …

Consider learning a policy purely on the basis of demonstrated behavior---that is, with no
access to reinforcement signals, no knowledge of transition dynamics, and no further …