Deep reinforcement learning has achieved great success in many fields and has shown
promise in learning robust skills for robot control in recent years. However, sampling …

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds
significant promise for capturing expert motor skills through efficient imitation, facilitating …

Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a
“socially compliant” manner in the presence of other intelligent agents such as humans. With …

Numerous methods are proposed for the Traffic Signal Control (TSC) tasks aiming to provide
efficient transportation and mitigate congestion waste. In recent, promising results have …

We study a new paradigm for sequential decision making, called offline policy learning from
observations (PLfO). Offline PLfO aims to learn policies using datasets with substandard …

As learning-based approaches progress towards automating robot controllers design,
transferring learned policies to new domains with different dynamics (eg sim-to-real transfer) …

Imitation learning enables high-fidelity, vision-based learning of policies within rich,
photorealistic environments. However, such techniques often rely on traditional discrete-time …

Generalizing policies across different domains with dynamics mismatch poses a significant
challenge in reinforcement learning. For example, a robot learns the policy in a simulator …

One of the key challenges in high-speed off-road navigation on ground vehicles is that the
kinodynamics of the vehicle-terrain interaction can differ dramatically depending on the …

Abstract This paper studies Imitation Learning from Observations alone (ILFO) where the
learner is presented with expert demonstrations that consist only of states visited by an …