We study how an autonomous agent learns to perform a task from demonstrations in a
different domain, such as a different environment or different agent. Such cross-domain …

The skill of pivoting an object with a robotic system is challenging for the external forces that
act on the system, mainly given by contact interaction. The complexity increases when the …

The ability to reuse collected data and transfer trained policies between robots could
alleviate the burden of additional data collection and training. While existing approaches …

Reinforcement learning (RL) has shown impressive results on a variety of robot tasks, but it
requires a large amount of data for learning a single RL policy. However, in manufacturing …

Transferring learned skills across diverse situations remains a fundamental challenge for
autonomous agents, particularly when agents are not allowed to interact with an exact target …

In reinforcement learning for legged robot locomotion, crafting effective reward strategies is
crucial. Pre-defined gait patterns and complex reward systems are widely used to stabilize …

This paper focuses on transferring control policies between robot manipulators with different
morphology. While reinforcement learning (RL) methods have shown successful results in …

The limited transferability of learned policies is a major challenge that restricts the
applicability of learning-based solutions in decision-making tasks. In this paper, we present …

Integrative Approaches to Behavior Prediction, Generation, and Skill Learning in Autonomous
Systems By Lingfeng Sun A dissertati Page 1 Integrative Approaches to Behavior Prediction …

Recovering reward function from expert demonstrations is a fundamental problem in
reinforcement learning. The recovered reward function captures the motivation of the expert …