Interacting with Intelligent Characters in AR

In this paper, we explore interacting with virtual characters in AR along real-world environments. Our vision is that virtual characters will be able to understand the real-world environment and interact in an intelligent and realistic manner with it. For example, a character can walk around uneven stairs and slopes, or be pushed away by collisions with real-world objects like a ball. We describe how to automatically animate a new character, and imbue it’s motion with adaption to environments and reactions to perturbations from the real world

Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers

We propose to address quadrupedal locomotion tasks using Reinforcement Learning (RL) with a Transformer-based model that learns to combine proprioceptive information and high-dimensional depth sensor inputs. While learning-based locomotion has made great advances using RL, most methods still rely on domain randomization for training blind agents that generalize to challenging terrains. Our key insight is that proprioceptive states only offer contact measurements for immediate reaction, whereas an agent equipped with visual sensory observations can learn to proactively maneuver environments with obstacles and uneven terrain by anticipating changes in the environment many steps ahead. In this paper, we introduce LocoTransformer, an end-to-end RL method for quadrupedal locomotion that leverages a Transformer-based model for fusing proprioceptive states and visual observations. We evaluate our method in challenging simulated environments with different obstacles and uneven terrain. We show that our method obtains significant improvements over policies with only proprioceptive state inputs, and that Transformer-based models further improve generalization across environments. Our project page with videos is at https://RchalYang.github.io/LocoTransformer .Comment: Our project page with videos is at https://RchalYang.github.io/LocoTransforme