90 citations · 161 across the 9 of their papers we have counts for
8 papers · 1 filter
GoalsEye: Learning High Speed Precision Table Tennis on a Physical Robot
Tianli Ding, Laura Graesser, Saminda Abeyruwan +5
Learning goal conditioned control in the real world is a challenging open problem in robotics. Reinforcement learning systems have the potential to learn autonomously via trial-and…
Interactive Language: Talking to Robots in Real Time
Corey Lynch, Ayzaan Wahid, Jonathan Tompson +5
We present a framework for building interactive, real-time, natural language-instructable robots in the real world, and we open source related assets (dataset, environment, benchma…
Demonstration-Bootstrapped Autonomous Practicing via Multi-Task Reinforcement Learning
Abhishek Gupta, Corey Lynch, Brandon Kinman +3
Reinforcement learning systems have the potential to enable continuous improvement in unstructured environments, leveraging data collected autonomously. However, in practice these…
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
Eric Jang, Alex Irpan, Mohi Khansari +5
In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning. We approach th…
Implicit Behavioral Cloning
Pete Florence, Corey Lynch, Andy Zeng +7
We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly…
Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning
Brian Ichter, Pierre Sermanet, Corey Lynch
Long-horizon planning in realistic environments requires the ability to reason over sequential tasks in high-dimensional state spaces with complex dynamics. Classical motion planni…