Danfei Xu

I am a Ph.D. student in CS at Stanford University. My advisors are Fei-Fei Li and Silvio Savarese who co-lead the Stanford Vision and Learning Lab. I work in the intersection of robot learning and computer vision. I'm also the current instructor of Stanford's CS231n Course on Convolutional Neural Networks for Visual Recognition.

Prior to joining Stanford, I received my B.S. from Columbia University (2015). I've spent time at DeepMind UK (2019), ZOOX (2017), Autodesk Research (2016), CMU RI (2014), and Columbia Robotics Lab (2013-2015).

I'm currently on the academic job market (2020-2021)!

Email  /  Google Scholar  /  CV (Aug 2020)  /  Github  /  Twitter


My research tackles long-standing problems in robotics such as learning from demonstrations and task planning by drawing from robotics, computer vision, and structured learning approaches. Some examples are learning to follow video task demonstrations by neural program induction and neural graph inference, planning for long-horizon tasks using visually-grounded neural-symbolic planners, and learning diverse visuo-motor policies from human demonstrations. Key questions that I seek to answer are:

  • How to learn from rich but disembodied data sources such as instructional videos at scale.
  • How to infer intentions from observing human behaviors to better learn from and assist humans.
  • Modeling the world in full details is neither feasible nor necessary. What are the optimal abstract representations to support long-horizon planning and how do we extract them from data?

I also contribute to research in structured scene understanding in 2D and 3D. Examples are scene graph generation, 3D reconstructions from monocular views, and 2D-3D sensor fusion in detection and tracking.


Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations (2020)

6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints (2020)
Positive-Unlabeled Reward Learning
Danfei Xu, Misha Denil
(Long version) CoRL 2020
(Short version) Late-Breaking Paper, NeurIPS Deep Reinforcement Learning Workshop 2019

An algorithm framework that simultaneously addresses the reward delusion problem in supervised reward learning and the overfitting discriminator problem in adversarial imitation learning.

Procedure Planning in Instructional Videos
Chien-Yi Chang, De-An Huang, Danfei Xu, Ehsan Adeli, Li Fei-Fei
Juan Carlos Niebles
ECCV, 2020

Learning to plan from instruction videos.

Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations
Ajay Mandlekar*, Danfei Xu*, Roberto Martin-Martin, Silvio Savarese, Li Fei-Fei
RSS, 2020

[website] [video] [blog post]

Learning visuomotor policies that can generalize across long-horizon tasks by modeling latent compositional structures.

6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints
Chen Wang, Roberto Martin-Martin, Danfei Xu, Jun Lv, Cewu Lu, Li Fei-Fei, Silvio Savarese, Yuke Zhu
ICRA, 2020

[website] [video] [code]

Real-time category-level 6D object tracking from RGB-D data.

Regression Planning Networks
Danfei Xu, Roberto Martin-Martin, De-An Huang, Yuke Zhu, Silvio Savarese, Li Fei-Fei
NeurIPS, 2019

[code] [poster]

A flexible neural network architecture for learning to plan from video demonstrations.

Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning
De-An Huang, Danfei Xu, Yuke Zhu, Silvio Savarese, Li Fei-Fei, Juan Carlos Niebles
IROS, 2019

[blog post]

One-shot imitation learning via hybrid neural-symbolic planning.

Situational Fusion of Visual Representation for Visual Navigation
William B. Shen, Danfei Xu, Yuke Zhu, Leonidas Guibas, Li Fei-Fei, Silvio Savarese
ICCV, 2019

Learning generalizable navigation policy from mid-level visual representations.

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
Chen Wang, Danfei Xu, Yuke Zhu, Roberto Martin-Martin, Cewu Lu, Li Fei-Fei, Silvio Savarese
CVPR, 2019

[website] [video] [code]

Dense RGB-depth sensor fusion for 6D object pose estimation.

Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration
De-An Huang*, Suraj Nair*, Danfei Xu*, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, Juan Carlos Niebles
CVPR, 2019 (Oral)

[blog post]

Generate executable task graphs from video demonstrations.

Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
Danfei Xu*, Suraj Nair*, Yuke Zhu, Julian Gao, Animesh Garg, Li Fei-Fei, Silvio Savarese
ICRA, 2018

[website] [video] [Two Minute Papers] [blog post]

Neural Task Programming (NTP) is a meta-learning framework that learns to generate robot-executable neural programs from task demonstration video.

PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
Danfei Xu, Ashesh Jain, Dragomir Anguelov
CVPR, 2018

End-to-end 3D Bounding Box Estimation via sensor fusion.

Scene Graph Generation by Iterative Message Passing
Danfei Xu, Yuke Zhu, Christopher B. Choy, Li Fei-Fei
CVPR, 2017

[website] [code]

We propose an end-to-end model that jointly infers object category, location, and relationships. The model learns to iteratively improve its prediction by passing messages on a scene graph.

3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Christopher B. Choy, Danfei Xu*, JunYoung Gwak*, Silvio Savarese
ECCV, 2016

[website] [code]

We propose an end-to-end 3D reconstruction model that unifies single- and multi-view reconstruction.

Model-Driven Feed-Forward Prediction for Manipulation of Deformable Objects
Yinxiao Li , Yan Wang , Yonghao Yue , Danfei Xu, Michael Case , Shih-Fu Chang , Eitan Grinspun , Peter K. Allen


Deformable object manipulation with an application of personal assitive robot.

This is the journal paper of our "laundry robot" series:
ICRA 2015
IROS 2015
ICRA 2016

Topometric localization on a road network
Danfei Xu, Hernan Badino, Daniel Huber
IROS, 2015

Vision-based localization on a probabilistic road network.

Tactile identification of objects using Bayesian exploration
Danfei Xu, Gerald E. Loeb, Jeremy Fishel
ICRA, 2013

Object classification using multi-modal tactile sensing.

  • [2020] Stanford CS 231n instructor
  • [2019] Stanford CS 231n teaching assistant & lecturer
  • [2018] Stanford CS 231n teaching assistant
  • [2018] Stanford CS 231a teaching assistant
Other Services

Template source