AI Research

EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics | AI Learning Platform

BenchmarkAgentReasoningCode Generation

EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics

Haoxiang You, Zeyu Shen, Yilang Liu et al.1 min readHugging FaceSep 23, 2026

Key Takeaway for Students

We study coding agents for long-horizon, dexterous robotics and ask whether their solutions can provide scalable supervision for learning general robot policies. To test this, we develop EMBODIEDSWE-BENCH, a simulation benchmark for coding agents spanning contact-rich manipulation, deformable objects, and long-horizon tasks requiring up to half an hour of continuous interaction

Reading in Plain English mode — toggle below to switch

This work explores the use of coding agents for complex, long-horizon robotic tasks, pushing the boundaries of autonomous systems. It highlights the potential for AI to generate scalable supervision for general robot policies, a critical step towards more capable and autonomous robotic systems.

Read original on Hugging Face

Career Insight

high demand

Why it matters

Skills to learn

Understanding of reinforcement learning principles for roboticsFamiliarity with simulation environments for robotic control (e.g., Isaac Gym, MuJoCo)Knowledge of prompt engineering and agentic workflows for code generationAbility to design and evaluate benchmarks for embodied AI tasksExperience with integrating large language models into robotic control pipelines

Interview questions

Describe the challenges of applying coding agents to long-horizon, dexterous robotics tasks compared to traditional control methods.
How would you design a robust evaluation metric for a coding agent solving a contact-rich manipulation task, considering both success rate and efficiency?
Propose a system architecture for deploying a coding agent to control a physical robot arm, detailing the components for perception, planning, and execution, and how the agent interacts with each.

Relevant for ~24 months · generated by the enrichment pipeline