Adaptive Human-Robot Collaboration using Type-Based IRL

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 results in diminished output and reduced quality. To address this challenge toward successful HRCs, we present a human-aware approach to collaboration using a novel multi-agent decision-making framework. Type-based decentralized Markov decision processes (TB-DecMDP) additionally model latent, causal decision-making factors influencing agent behavior (e. g. , fatigue), leading to dynamic agent types. In this framework, agents can switch between types and each maintains a belief about others’ current type based on observed actions while aiming to achieve a shared objective. We introduce a new inverse reinforcement learning (IRL) algorithm, TB-DecAIRL, which uses TB-DecMDP to model complex HRCs. TB-DecAIRL learns a type-contingent reward function and corresponding vector of policies from team demonstrations. Our evaluations in a realistic HRC problem setting establish that modeling human types in TB-DecAIRL improves robot behavior on the default of ignoring human factors, by increasing throughput in a human-robot produce sorting task.

Authors

• Prasanth Sengadu Suresh
• Prashant Doshi
• Bikramjit Banerjee

Keywords

• robotics
• inverse reinforcement learning
• human-robot collaboration
• human types