updated readme

README.md

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 # Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation
 [Mingyo Seo](https://mingyoseo.com), [Steve Han](https://www.linkedin.com/in/stevehan2001), [Kyutae Sim](https://www.linkedin.com/in/kyutae-sim-888593166), [Seung Hyeon Bang](https://sites.utexas.edu/hcrl/people/), [Carlos Gonzalez](https://sites.utexas.edu/hcrl/people/), [Luis Sentis](https://sites.google.com/view/lsentis), [Yuke Zhu](https://www.cs.utexas.edu/~yukez)
 
-[Project](https://ut-austin-rpl.github.io/TRILL) | [arXiv](https://arxiv.org/abs/2309.01952)
+[Project](https://ut-austin-rpl.github.io/TRILL) | [arXiv](https://arxiv.org/abs/2309.01952) | [code](https://github.com/UT-Austin-RPL/TRILL)
 
 ## Abstract
 We tackle the problem of developing humanoid loco-manipulation skills with deep imitation learning. The challenge of collecting human demonstrations for humanoids, in conjunction with the difficulty of policy training under a high degree of freedom, presents substantial challenges. We introduce TRILL, a data-efficient framework for learning humanoid loco-manipulation policies from human demonstrations. In this framework, we collect human demonstration data through an intuitive Virtual Reality (VR) interface. We employ the whole-body control formulation to transform task-space commands from human operators into the robot's joint-torque actuation while stabilizing its dynamics. By employing high-level action abstractions tailored for humanoid robots, our method can efficiently learn complex loco-manipulation skills. We demonstrate the effectiveness of TRILL in simulation and on a real-world robot for performing various types of tasks.