Duet: Dual-Robot Understanding via Efficient Teaching

arXiv:2606.20990 · cs.RO · Submitted 2026-06-18 · Read on arXiv

cs.RO

Submitted: 2026-06-18

Updated: 2026-09-22

Code: https://github.com/huggingface/lerobot

Project page: https://zhaoy37.github.io/Duet

Terminology

• Transformer — 12× in this paper · explained in 2 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Fidelity — 6× in this paper · explained in 2 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Generalizability — 5× in this paper · explained in 6 episodes
• General Motion Retargeting — 4× in this paper
• IEEE International Conference on Robotics and Automation — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Action Chunking — 3× in this paper · explained in 1 episode
• Action Chunking Transformer — 3× in this paper
• Decoupling — 3× in this paper · explained in 2 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Affordance — 2× in this paper · explained in 1 episode
• Generalization — 2× in this paper · explained in 13 episodes
• Action Chunking Transformer (ACT) — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• World Action Models are Zero-shot Policies
• EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World
• SAM 3D Body: Robust Full-Body Human Mesh Recovery
• TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
• In-the-Wild Compliant Manipulation with UMI-FT
• $\Psi_0$: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation
• SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
• TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System
• R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations
• CollaBot: Vision-Language Guided Simultaneous Collaborative Manipulation
• State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey
• Multi-Quadruped Cooperative Object Transport: Learning Decentralized Pinch-Lift-Move
• DeReCo: Decoupling Representation and Coordination Learning for Object-Adaptive Decentralized Multi-Robot Cooperative Transport
• CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation
• HumDex: Humanoid Dexterous Manipulation Made Easy
• RoboPaint: From Human Demonstration to Any Robot and Any View
• EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration
• Humanoid Manipulation Interface: Humanoid Whole-Body Manipulation from Robot-Free Demonstrations
• Rhythm: Learning Interactive Whole-Body Control for Dual Humanoids
• Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations

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
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving