MetaMorph: Learning Universal Controllers with Transformers (2203.11931v1)

Abstract: Multiple domains like vision, natural language, and audio are witnessing tremendous progress by leveraging Transformers for large scale pre-training followed by task specific fine tuning. In contrast, in robotics we primarily train a single robot for a single task. However, modular robot systems now allow for the flexible combination of general-purpose building blocks into task optimized morphologies. However, given the exponentially large number of possible robot morphologies, training a controller for each new design is impractical. In this work, we propose MetaMorph, a Transformer based approach to learn a universal controller over a modular robot design space. MetaMorph is based on the insight that robot morphology is just another modality on which we can condition the output of a Transformer. Through extensive experiments we demonstrate that large scale pre-training on a variety of robot morphologies results in policies with combinatorial generalization capabilities, including zero shot generalization to unseen robot morphologies. We further demonstrate that our pre-trained policy can be used for sample-efficient transfer to completely new robot morphologies and tasks.

• The paper introduces a Transformer-based architecture that generalizes control across diverse modular robot morphologies.
• It demonstrates sample-efficient transfer learning, reducing training samples for adapting to new tasks and designs.
• The research achieves zero-shot generalization, successfully applying pre-trained policies to unseen morphologies without retraining.

MetaMorph: Learning Universal Controllers with Transformers

The paper presents a novel approach to achieving universal control in modular robotics through a method called MetaMorph. This approach leverages the capabilities of Transformer architectures to address the challenges associated with controlling a vast array of potential robot morphologies. In domains such as vision, language, and audio, the advancement of large-scale pre-training followed by task-specific fine-tuning has been prolific, leading to significant progress. However, this paradigm has not been fully realized in robotics, where the prevalent method involves training individual robots for specific tasks. MetaMorph aims to transform this landscape by applying large-scale pre-training in the robot design space, facilitating sample-efficient transfer to new morphologies and tasks.

Overview of MetaMorph

MetaMorph is based on the insight that robot morphology can be treated analogously to other modalities conditioned upon by Transformer outputs. The design space for modular robots allows for the construction of various morphologies using general-purpose building blocks. Given the combinatorial explosion of possible morphological configurations, training a separate controller for each configuration becomes impractical.

The proposed method revolves around:

• Universal Control: Developing policies capable of generalizing across unseen robot morphologies.
• Transformer-based Architecture: Utilizing a sequence of morphological and proprioceptive states as tokens for the Transformer.

Key Contributions

1. Universal Controller Architecture: MetaMorph's transformative architecture combines proprioceptive signals with morphological information as input tokens processed by the Transformer model. This conditioning enables the model to generalize across various dynamics and kinematics.
2. Sample-Efficient Transfer: The paper demonstrates that pre-trained policies can be adapted to new tasks and configurations with minimal sample input, providing an efficient path for applying robotics to practical, real-world scenarios.
3. Zero-Shot Generalization: Through extensive experimentation, the approach shows strong zero-shot generalization, effectively applying learned policies to new morphologies and tasks without prior training.
4. Dynamic Replay Buffer: Introduction of a balancing process to account for disparities in robot learning speeds, optimizing training efficiency for diverse morphologies simultaneously.

Experimental Evaluation

Experiments conducted on 100 different modular robots highlighted the efficacy of MetaMorph in several environments. The approach achieved comparative performance to single-morphology-trained models, with notable improvements in sample efficiency across diverse environments like Flat Terrain and Variable Terrain.

In-depth analysis revealed the emergence of motor synergies within the trained policies, indicating the ability of the model to activate coordinated joint movements, aligning with natural biological strategies for managing multiple degrees of freedom.

Implications and Future Directions

MetaMorph presents a significant advancement toward the development of generalized robotic controllers. The research bridges the gap between machine learning's success in non-embodied domains and its potential in robotic applications, where physical embodiment introduces unique challenges.

Future research may focus on integrating morphology and control optimization processes, enhancing the versatility of robots to perform an array of tasks using a singular foundational training model. Additionally, advancing algorithms for efficient zero-shot transfer could further improve real-world applicability, especially in settings demanding rapid adaptation to unforeseen conditions.

Overall, MetaMorph stands as a promising step in realizing the vision of universally adaptable and intelligent robotic systems, driven by large-scale pre-training architectures such as Transformers.