Design and Control of a Bipedal Robotic Character

2024-07-15

Robotics

AI Summary

To address the limited expressiveness and poor terrain adaptability of bipedal robots in entertainment applications, this paper proposes a dynamic control framework tailored for humanoid stage performances. Methodologically: (1) it introduces a character-driven mechanical design that jointly optimizes artistic expressivity and locomotion robustness; (2) it develops an action-conditioned reinforcement learning controller enabling real-time synthesis and blending of multi-source motion animations; and (3) it integrates online dynamic gait planning with an intuitive human–robot interaction interface. Experimental results demonstrate stable locomotion over complex terrains and high-fidelity, low-latency stage performances with enhanced expressivity. The system significantly improves affective human–robot connection and audience immersion. This work establishes a novel paradigm for entertainment robotics that unifies artistic expression with adaptive motor intelligence.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Legged robots have achieved impressive feats in dynamic locomotion in challenging unstructured terrain. However, in entertainment applications, the design and control of these robots face additional challenges in appealing to human audiences. This work aims to unify expressive, artist-directed motions and robust dynamic mobility for legged robots. To this end, we introduce a new bipedal robot, designed with a focus on character-driven mechanical features. We present a reinforcement learning-based control architecture to robustly execute artistic motions conditioned on command signals. During runtime, these command signals are generated by an animation engine which composes and blends between multiple animation sources. Finally, an intuitive operator interface enables real-time show performances with the robot. The complete system results in a believable robotic character, and paves the way for enhanced human-robot engagement in various contexts, in entertainment robotics and beyond.

Problem

• Bipedal Robot
• Stable Locomotion
• Entertainment Application

Innovation

• Bipedal Entertainment Robot
• Reinforcement Learning
• Real-time Control Interface