Christopher Agia

About

Christopher Agia is an emerging robotics and AI researcher whose work bridges natural language processing, robot planning, and embodied intelligence. His most influential contribution, *Text2Motion* (2023, 197 citations), introduced a language-based planning framework enabling robots to construct and execute long-horizon manipulation plans from natural language instructions — a significant leap toward intuitive human-robot interaction. Agia has also played a notable role in large-scale collaborative efforts, contributing to both *Open X-Embodiment* (2024, 119 citations) and *DROID* (2024, 108 citations), landmark dataset initiatives that are reshaping how generalizable robotic policies are trained across diverse real-world environments. His research further explores robot safety and reliability through semantic anomaly detection using large language models (2023, 85 citations), and extends to autonomous navigation in challenging terrain via sim-to-real reinforcement learning pipelines (2021, 84 citations). Work on task planning over 3D scene graphs and the *STAP* framework for sequencing task-agnostic policies reflects his broader interest in structured, scalable robot reasoning. Collectively, Agia's portfolio — spanning perception, planning, and safety — positions him as a versatile contributor to the next generation of capable, language-guided robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Text2Motion: from natural language instructions to feasible plans

   197 citations · 2023
2. 2
   Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration⁰

   119 citations · 2024
3. 3
   DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

   108 citations · 2024
4. 4
   Semantic anomaly detection with large language models

   85 citations · 2023
5. 5
   A Sim-to-Real Pipeline for Deep Reinforcement Learning for Autonomous Robot Navigation in Cluttered Rough Terrain

   84 citations · 2021
6. 6
   Real-Time Anomaly Detection and Reactive Planning with Large Language Models

   28 citations · 2024
7. 7
   STAP: Sequencing Task-Agnostic Policies

   13 citations · 2023
8. 8
   TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs

   12 citations · 2022
9. 9
   Text2Motion: From Natural Language Instructions to Feasible Plans

   10 citations · 2023
10. 10
    S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds

    8 citations · 2020

Key Collaborators

Contact & Links