Yevgen Chebotar

About

Yevgen Chebotar is a prominent robotics and machine learning researcher whose work spans embodied AI, self-supervised learning, and large-scale robotic control. Rising to recognition through foundational contributions at the intersection of perception and manipulation, Chebotar has helped shape modern approaches to teaching robots through observation and language. His 2018 paper on Time-Contrastive Networks (555 citations) pioneered self-supervised learning from unlabeled video, enabling robots to imitate human behaviors without hand-labeled data. Early in his career, he also made significant contributions to tactile sensing and grip control, developing biomimetic sensor techniques (215 citations) that remain influential in dexterous manipulation research. Chebotar's most consequential impact has come through landmark collaborations on foundation models for robotics. His involvement in "Do As I Can" (516 citations), RT-1 (512 citations), PaLM-E (350 citations), and RT-2 (267 citations) represents a transformative research arc demonstrating how large-scale vision, language, and action models can enable generalizable robotic behavior in real-world settings. His work on Inner Monologue (206 citations) further explored language-driven embodied reasoning. Across reinforcement learning, tactile sensing, and multimodal AI, Chebotar's research consistently bridges theoretical innovation with practical robotic deployment, cementing his status as a leading voice in next-generation intelligent robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Time-Contrastive Networks: Self-Supervised Learning from Video

   555 citations · 2018
2. 2
   Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

   516 citations · 2022
3. 3
   RT-1: Robotics Transformer for Real-World Control at Scale

   512 citations · 2023
4. 4
   PaLM-E: An Embodied Multimodal Language Model

   350 citations · 2023
5. 5
   RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

   267 citations · 2023
6. 6
   Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor

   215 citations · 2015
7. 7
   Inner Monologue: Embodied Reasoning through Planning with Language Models

   206 citations · 2022
8. 8
   Collective robot reinforcement learning with distributed asynchronous guided policy search

   139 citations · 2017
9. 9
   Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning

   88 citations · 2017
10. 10
    Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning

    75 citations · 2016

Key Collaborators

Contact & Links