Currently, I am a master's student at EPFL and joining the Soft Robotics Lab at ETH as a research intern. I received honours degree (summa cum laude) from Turing class at the School of Computer Science, Peking University. At Peking University, I was mainly advised by Prof. Hao Dong on robotics learning. I also collaborated closely with Prof. Zongqing Lu on the spatial cognitive abilities of machine learning models.

Arnold is a single generalist muscle control policy trained on 14 different muscle control tasks, with 4 different embodiments. We used parallel on-policy behavior cloning, RL fine-tuning and self-distillation to match or even surpass expert performance on all tasks.

RoboVerse is a unified framework for robotic learning. It provides an API interface to run simulations with multiple back-ends, equipped with large-scale datasets and benchmarks for training and evaluating robotic learning algorithms.

We achieved state-of-the-art manipulation performance by combining reinforcement learning and multi-view pose estimation.

We investigated whether a compositional language for describing 2-D positional relation can emerge from communication under environmental pressure.

We proposed an image-guided depth completion model which is SOTA on NYUv2 dataset and won the first place on KITTY benchmark at the time of submission.

In this study, we take advantage of visual affordance by using the contact information generated during the RL training process to predict contact maps of interest.

Reconfiguring a die to match desired goal orientations. This task require delicate coordination of various muscles to manipulate the die without dropping it.

We propose a framework that utilizes only RGB inputs and enables 3D articulated object manipulation.

This template is a modification to Jon Barron's website.