Quantifying Mechanical Intelligence in Legged Robots with Information Theory

arXiv:2609.19588 · cs.RO · Submitted 2026-09-17 · Read on arXiv

cs.RO

Submitted: 2026-09-17

Updated: 2026-09-17

Comments: 8 pages, 6 figures, 1 table, submitted to IEEE ICRA 2027 for publication

Code: https://github.com/unitreerobotics/unitree

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 6× in this paper · explained in 2 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning
• Proximal Policy Optimization Algorithms

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• ARC: A Reasoning Recipe for Robot Foundation Models
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies