Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers (2107.03996v3)

Abstract: 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 that leverages both proprioceptive states and visual observations for locomotion control. We evaluate our method in challenging simulated environments with different obstacles and uneven terrain. We transfer our learned policy from simulation to a real robot by running it indoors and in the wild with unseen obstacles and terrain. Our method not only significantly improves over baselines, but also achieves far better generalization performance, especially when transferred to the real robot. Our project page with videos is at https://rchalyang.github.io/LocoTransformer/ .

• The paper introduces LocoTransformer, integrating visual and proprioceptive inputs using cross-modal Transformers to improve quadrupedal locomotion.
• It employs separate encoders and a shared self-attention mechanism to fuse sensory data for effective long-term planning on complex terrains.
• Experiments demonstrate significant enhancements in obstacle avoidance and real-world transferability, outperforming state-only and concatenation methods.

Overview of Vision-Guided Quadrupedal Locomotion with Cross-Modal Transformers

The paper "Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers" introduces an innovative approach to improve the locomotion capabilities of quadruped robots using reinforcement learning (RL) coupled with a Transformer-based architecture. Dubbed the LocoTransformer, this methodology exemplifies a significant shift from traditional proprioception-reliant methods towards leveraging high-dimensional visual inputs to enhance adaptability and maneuverability in complex terrains.

Motivation and Approach

Legged locomotion in robotics, particularly for quadrupeds, involves navigating diverse and challenging environments. Most conventional RL methods rely heavily on proprioceptive data—contact measurements that primarily inform immediate reactive movements. However, this strategy has limitations, especially in environments with varying terrains and obstacles. The introduction of visual sensory input aims to extend the anticipatory and planning capabilities of robots, akin to how human eye-body coordination works during locomotion.

LocoTransformer integrates proprioceptive and visual data by using cross-modal Transformers that allow for simultaneous processing and fusion of both modalities. The architecture involves two primary components:

1. Separate encoders for each modality, where an MLP processes proprioceptive states and a ConvNet processes depth images, producing respective feature tokens.
2. A shared Transformer encoder that applies self-attention mechanisms to combine and reason about these tokens, facilitating both long-term planning and immediate actions.

Experimental Setup and Results

The research demonstrates the efficacy of the proposed method across varied simulated environments such as obstacle-rich terrains and mountainous regions, alongside real-world trials. Notably, the LocoTransformer outperforms several baselines, including:

• State-only and depth-only models which singularly use proprioceptive and visual inputs.
• State-Depth concatenation models that lack sophisticated cross-modal reasoning.
• Hierarchical RL approaches which often struggle with inter-modal integration.

Strong performance metrics include increased distances moved without collision in trained environments and improved generalization in unseen scenarios. In simulation-to-reality transfers, the LocoTransformer showcases robustness—successfully navigating and avoiding obstacles that were absent during training.

Implications and Future Directions

The practical implications of this work are profound, highlighting the potential for robots to autonomously navigate complex environments such as rough terrains or dynamically changing outdoor settings. The integration of visual inputs heralds increased generalizability and interpretability in robot locomotion, overcoming the limitations posed by blind or state-only controllers.

From a theoretical perspective, this research underscores the transformative potential of Transformers in areas beyond natural language processing. The self-attention mechanism provides an elegant pathway for future studies seeking to enhance the interaction and fusion of multi-modal data in RL tasks.

Looking ahead, further exploration could involve scaling the complexity of visual inputs—potentially incorporating color or higher-dimensional imagery—and refining the real-world applicability of learned policies. Additionally, extending this approach to include multi-agent systems or collaborative robotic scenarios could offer new avenues for advancements in autonomous robotics.

Overall, this paper enriches the discourse on robotics by challenging the status quo of proprioceptive-centric models and opening collaborative vistas through the integration of visual perceptions using transformative AI methodologies.