Robots Influencing Humans to Reveal their Goals during Collaboration and Competition

Abstract

We propose a unified strategy for fast goal inference in human-robot interaction. The core idea is to drive the human toward Critical Decision Points (CDPs)-states where competing human strategies prescribe different next actions and thus maximally reveal the goal. We formalise CDPs using a goal-conditioned policy divergence measure and incorporate them into a Receding-Horizon Planner that explores future action sequences while optimizing a cost function balancing task progress and information gain. We evaluate this approach in both a collaborative, fully observable cooking task and a competitive, partially observable hide-and-seek game, each in simulation and on real robots. In both scenarios, our method infers human goals more accurately and earlier than baseline strategies.

Accepted to Autonomous Robots (AURO)

References in corpus (7)

• "No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy
• Model Predictive Control with Gaussian Processes for Flexible Multi-Modal Physical Human Robot Interaction
• ExpressEdit: Video Editing with Natural Language and Sketching
• Aligning Robot and Human Representations
• Learning Latent Representations to Co-Adapt to Humans
• Preemptive Motion Planning for Human-to-Robot Indirect Placement Handovers
• Workspace Optimization Techniques to Improve Prediction of Human Motion During Human-Robot Collaboration