Towards Embodiment Scaling Laws in Robot Locomotion (2505.05753v1)

Abstract: Developing generalist agents that can operate across diverse tasks, environments, and physical embodiments is a grand challenge in robotics and artificial intelligence. In this work, we focus on the axis of embodiment and investigate embodiment scaling laws$\unicode{x2013}$the hypothesis that increasing the number of training embodiments improves generalization to unseen ones. Using robot locomotion as a test bed, we procedurally generate a dataset of $\sim$1,000 varied embodiments, spanning humanoids, quadrupeds, and hexapods, and train generalist policies capable of handling diverse observation and action spaces on random subsets. We find that increasing the number of training embodiments improves generalization to unseen ones, and scaling embodiments is more effective in enabling embodiment-level generalization than scaling data on small, fixed sets of embodiments. Notably, our best policy, trained on the full dataset, zero-shot transfers to novel embodiments in the real world, such as Unitree Go2 and H1. These results represent a step toward general embodied intelligence, with potential relevance to adaptive control for configurable robots, co-design of morphology and control, and beyond.

• The paper explores the hypothesis that training robot locomotion policies on a diverse range of embodiments improves generalization to unseen robot morphologies, presenting evidence for "embodiment scaling laws."
• The methodology involves training on a large procedurally generated dataset of 1,000 varied robot embodiments using reinforcement learning and distilling policies into a unified model capable of handling different morphologies.
• Key results include a positive correlation between training embodiment diversity and generalization performance, as well as successful zero-shot transfer of the learned policies to real-world quadruped and humanoid robots.

Embodiment Scaling Laws in Robot Locomotion

The paper "Towards Embodiment Scaling Laws in Robot Locomotion" presents a comprehensive paper exploring the hypothesis that increasing the diversity of training embodiments improves the generalization of control policies to unseen robot morphologies. This investigation is pivotal as it addresses the broader challenge of creating generalist agents capable of operating across a diverse range of tasks, environments, and physical embodiments in robotics and artificial intelligence.

Methodology

This research employs a large-scale procedural generation approach to synthesize a dataset of approximately 1,000 distinct robot embodiments. These generated embodiments include humanoids, quadrupeds, and hexapods, with systematic variations in topology, geometry, and kinematic constraints. The underpinning hypothesis, termed "embodiment scaling laws," posits that policies trained on a wide range of embodiments are better at generalizing to new, unseen ones due to the capture of shared control strategies inherent across different robot morphologies.

The paper adopts a two-stage learning paradigm. Firstly, it trains single-embodiment expert policies using reinforcement learning (RL), specifically Proximal Policy Optimization (PPO), across diverse simulated environments. Secondly, it distills these varied expert behaviors into a unified policy using the Unified Robot Morphology Architecture (URMA), which is an attention-based model capable of handling varying observation and action spaces. This distillation stage employs behavior cloning, aggregating over billions of simulation steps to leverage cross-embodiment learning.

Key Results

1. In-Class and Cross-Class Analysis: The paper demonstrates a positive correlation between the number of training embodiments and the generalization performance on test sets across all three morphological classes. Notably, humanoid embodiments showed continuous improvement with increasing training data, indicating more significant benefits from scaling.
2. Zero-Shot Transfer: The trained policy exhibited impressive zero-shot transfer capabilities to real-world robots, namely the Unitree Go2 quadruped and Unitree H1 humanoid, without any additional fine-tuning. The policy could handle modified kinematic constraints, adapting to robots with varied joint specifications.
3. Embodiment Representation: Through t-SNE analysis, the learned latent space within the URMA model revealed structured clusters, segregating embodiments based on morphology and joint complexity. This suggests that the model successfully encapsulates meaningful features facilitating cross-embodiment policy transfer.

Implications and Future Directions

The preliminary evidence supporting embodiment scaling laws positions this paper as a step towards achieving general embodied intelligence. This insight has profound implications for adaptive control in configurable and modular robotics, where simultaneous design of morphology and control strategies could be optimized collectively.

Future research could expand on these findings by exploring more complex tasks beyond locomotion, such as manipulation or multi-modal interactions in dynamic environments. Additionally, extending the diversity of morphologies to include more variability in actuation types, mass distribution, and compliance could further test the robustness of embodiment scaling laws. Moreover, integrating this approach with sim-to-real frameworks could refine transfer learning strategies, ultimately paving the way for more resilient and versatile robotic systems.

In conclusion, this paper provides valuable insights into how increasing the diversity of training data across different robot morphologies enhances the flexibility and adaptability of control policies, a critical step forward in the pursuit of versatile robotic intelligence.