38 citations · 75 across the 6 of their papers we have counts for
6 papers
Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation
Albert Yu, Chengshu Li, Luca Macesanu +4
Effective robotic systems for long-horizon human-robot collaboration must adapt to a wide range of human partners, whose physical behavior, willingness to assist, and understanding…
Natural Language Can Help Bridge the Sim2Real Gap
Albert Yu, Adeline Foote, Raymond Mooney +1
The main challenge in learning image-conditioned robotic policies is acquiring a visual representation conducive to low-level control. Due to the high dimensionality of the image s…
Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks
Albert Yu, Raymond J. Mooney
Demonstrations and natural language instructions are two common ways to specify and teach robots novel tasks. However, for many complex tasks, a demonstration or language instructi…
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning
Homer Walke, Jonathan Yang, Albert Yu +4
Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems. However, in practice, real-world robotic RL typically requires…
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
Avi Singh, Huihan Liu, Gaoyue Zhou +3
Reinforcement learning provides a general framework for flexible decision making and control, but requires extensive data collection for each new task that an agent needs to learn.…
COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning
Avi Singh, Albert Yu, Jonathan Yang +3
Reinforcement learning has been applied to a wide variety of robotics problems, but most of such applications involve collecting data from scratch for each new task. Since the amou…