BiGym 2.0: Benchmarking Learned and Agent-Developed Policies for Humanoid Household Manipulation
cs.RO, cs.LG
Submitted: 2026-10-06
Updated: 2026-10-08
Comments: 23 pages. Updated the demonstration image in Figure 5, clarified observation interfaces, and improved HTML rendering of figures, author layout and code listings
Code: https://github.com/swirl-uk/BiGym2
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation
- HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning
- HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning
- SteadyTray: Learning Object Balancing Tasks in Humanoid Tray Transport via Residual Reinforcement Learning
- CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
- RHO: Your Coding Agent is Secretly a Roboticist
- ASPIRE: Agentic /Skills Discovery for Robotics
- EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos
- Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- Viser: Imperative, Web-based 3D Visualization in Python
- How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments
- World Action Models are Zero-shot Policies
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