Ran (Thomas) Tian - 田然
rantian [at] berkeley [dot] edu

I am a PhD student at UC Berkeley advised by Prof. Masayoshi Tomizuka and Prof. Andrea Bajcsy at Carnegie Mellon University. I am also a Qualcomm Innovation Fellow , a World Artificial Intelligence Conference Rising Star , and a Robotics: Science and Systems Pioneer.

My research lies in the intersection of robotics and AI with a focus on safe alignment between embodied agents and humans. I tackle the alignment and safety problems that emerge throughout the life-cycle of foundation models in robotics, ranging from: training (wherein we need to collect and quantify what kinds of embodied data will enable the desired robotics capabilities), to fine-tuning (wherein we must align these models with humans), to deployment (where these models must run in real-time, reliably detect out-of-distribution scenarios, and confidently handover control to fallback-strategies). I ground my work through a variety of applications, from autonomous cars, to personalized robots, to generative AI and in experiments with real human participants.

During my PhD study, I am a long-term intern at Waymo (2022-Now), working on foundational motion model for autonomous driving research (including pre-training, post-training preference alignment, and distillation for onboard deployment). I am fortunate to have the opportunity to intern at the Autonomous Vehicle Research Group at NVIDIA Research, working on visual foundational models for autonomous driving (2023-2024). Previously, I was a research intern at WeRide, Honda Research Institute, and Qualcomm AI Research.

I sincerely appreciate the fellowship support from WeRide (thank you! Dr. Yan Li, Dr. Hua Zhong, and Dr. Tony Han) for funding my research in my first two years of study!

I am actively looking for an industrial RS or a postdoctoral position! Please reach out to me if you think I might be a good fit!

google scholar   |   twitter   

profile photo

News

  • [Sep 2024]

    Three papers accepted by CoRL! One on using VLM to handle long-tail events in autonomous driving, one on system-level failure detection for motion prediction model refinement, and one on MoE policies for robot manipulation.
  • [Jun 2024]

    I was named a Rising Star and won the Yunfan Award at the World Artificial Intelligence Conference this year (top 15 early career Chinese AI researchers)!
  • [May 2024]

    I was named a Robotics: Science and Systems Pioneer this year (top 30 early career researchers around the world in the robotics field)!
  • [Apr 2024]

    I won the 2024 Qualcomm Innovation Fellowship (32 winners in North America)!
  • [Mar 2024]

    I won the finalist award in the Baidu AI Fellowship this year (top 20 Phd students in the AI field around the world)!
  • [Mar 2024]

    Our work RT-X won the 2024 Finalists for ICRA Best Paper, Best Student Paper, and Best Manipulation Paper Awards
  • [Mar 2024]

    Check out our new preprint on a general calibrated regret metric for detecting and mitigating system-level human-robot interaction failures and how it can be used to identify informative deployment data for efficiently improving behavior prediction models.
  • [Jan 2024]

    Our work on visual representation alignment for robot learning was accepted to ICLR 2024! We propose a tractable video-only method for solving the visual representation alignment problem and learning visual robot rewards. Check out the new results in the arXiv version.

Publications

For the most up-to-date list of publications, please see google scholar.

* indicates equal contribution and co-authorship.

Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving
Ran Tian, Boyi Li, Xinshuo Weng, Yuxiao Chen, Edward Schmerling, Yue Wang, Boris Ivanovic, Marco Pavone
CoRL, 2024

paper   website

MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention
Yuxin Chen, Chen Tang, Chenran Li, Ran Tian, Peter Stone, Masayoshi Tomizuka, Wei Zhan
preprint, 2024

paper  

Not All Errors Are Made Equal: A Regret Metric for Detecting System-level Trajectory Prediction Failures
Kensuke Nakamura, Ran Tian, Andrea Bajcsy
CoRL, 2024

paper   website

What Matters to You? Towards Visual Representation Alignment for Robot Learning
Ran Tian, Chenfeng Xu, Masayoshi Tomizuka, Jitendra Malik, Andrea Bajcsy
International Conference on Learning Representations, 2024

paper  

Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Google, Ran Tian, et al.
International Conference on Robotics and Automation (ICRA), 2024,

best paper award.



paper   website

Human-oriented Representation Learning for Robotic Manipulation
Mingxiao Huo, Mingyu Ding, Chenfeng Xu, Ran Tian, Xinghao Zhu, Yao Mu Lingfeng Sun, Masayoshi Tomizuka, Wei Zhan
Robotics: Science and Systems, 2024

paper   website

Towards Modeling and Influencing the Dynamics of Human Learning
Ran Tian, Masayoshi Tomizuka, Anca Dragan, Andrea Bajcsy
International Conference on Human-Robot Interaction (HRI), 2023

paper   talk

Safety Assurances for Human-Robot Interaction via Confidence-aware Game-theoretic Human Models
Ran Tian, Liting Sun, Andrea Bajcsy, Masayoshi Tomizuka, Anca Dragan
International Conference on Robotics and Automation (ICRA), 2022

paper   talk

Cost-Effective Sensing for Goal Inference: A Model Predictive Approach
Ran Tian, Nan Li, Anouck Girard, Ilya Kolmanovsky, Masayoshi Tomizuka
International Conference on Robotics and Automation (ICRA), 2022

paper

Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data
Ran Tian, Masayoshi Tomizuka, Liting Sun
International Conference on Intelligent Robots and Systems (IROS), 2021

paper  
Bounded Risk-sensitive Markov Games: Forward Policy Design and Inverse Reward Learning with Iterative Reasoning and Cumulative Prospect Theory
Ran Tian, Liting Sun, Masayoshi Tomizuka
AAAI Conference on Artificial Intelligence, 2021

paper  

Anytime Game-theoretic Planning with Active Reasoning about Humans’ Latent States for Human-centered Robots
Ran Tian, Liting Sun, Masayoshi Tomizuka, David Isele
International Conference on Robotics and Automation (ICRA), 2021

paper  

Controller Mode and Reference Governor for Constraint and Failure Management in Autonomous Vehicle Platooning
Ran Tian, Nan Li, Anouck Girard, Ilya Kolmanovsky
Conference on Control Technology and Applications, 2020

paper   talk  

Beating Humans in a Penny-matching Game by Leveraging Cognitive Hierarchy Theory and Bayesian Learning
Ran Tian, Nan Li, Anouck Girard, Ilya Kolmanovsky
American Control Conference(ACC), 2021

paper   talk

Game-Theoretic Modeling of Driver Social Interactions
Ran Tian, Nan Li, Ilya Kolmanovsky, Yildiray Yildiz, Anouck Girard
IEEE Transactions on Intelligent Transportation Systems, 2020

paper  

Adaptive Game-Theoretic Decision-Making for Autonomous Vehicle Control at Roundabouts
Ran Tian, Sisi Li, Nan Li, Ilya Kolmanovsky, Anouck Girard
Conference on Decision and Control , 2018

paper  

Reference Governor Strategies for Vehicle Rollover Avoidance
Ricardo Bencatel, Ran Tian, Anouck Girard, Ilya Kolmanovsky
IEEE Transactions on Control Systems Technology, 2017

paper  

Path Planning for Information Collection in Contested Environments using Marsupial Systems
Ran Tian, Hao Chen, Gregory Frey, Bingqing Zu, Anouck Girard, Ilya Kolmanovsky.
IEEE International Conference on Unmanned Aircraft Systems, 2017

paper  



website adapted from here