5 accepted papers
Human-robot collaboration (HRC) integrates the consistency and precision of robotic systems with the dexterity and cognitive abilities of humans to create synergy. However, human performance may degrade due to various factors (e.g., fatigue, trust) which can manifest unpredictably, and typically res
With the motivation of extending incremental inverse reinforcement learning (I2RL) to real-world robotics applications with noisy observations as well as an unknown observation model, we introduce a new method (RIMEO) that approximates the observation model in order to best estimate the noise-free g…
Recent renewed interest in multi-agent reinforcement learning (MARL) has generated an impressive array of techniques that leverage deep RL, primarily actor-critic architectures, and can be applied to a limited range of settings in terms of observability and communication. However, a continuing limit…
Multi-task IRL recognizes that expert(s) could be switching between multiple ways of solving the same problem, or interleaving demonstrations of multiple tasks. The learner aims to learn the reward functions that individually guide these distinct ways. We present a new method for multi-task IRL that…
Behavior trees (BT) are a popular control architecture in the computer game industry, and have been more recently applied in robotics. One open question is how can intelligent agents/robots autonomously acquire their behavior trees for task level control? In contrast with existing approaches that ei…