524 citations · 693 across the 10 of their papers we have counts for
10 papers
Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators
Alexander Herzog, Kanishka Rao, Karol Hausman +37
We describe a system for deep reinforcement learning of robotic manipulation skills applied to a large-scale real-world task: sorting recyclables and trash in office buildings. Rea…
Asking for Help: Failure Prediction in Behavioral Cloning through Value Approximation
Cem Gokmen, Daniel Ho, Mohi Khansari
Recent progress in end-to-end Imitation Learning approaches has shown promising results and generalization capabilities on mobile manipulation tasks. Such models are seeing increas…
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
Michael Ahn, Anthony Brohan, Noah Brown +42
Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extend…
Practical Imitation Learning in the Real World via Task Consistency Loss
Mohi Khansari, Daniel Ho, Yuqing Du +6
Recent work in visual end-to-end learning for robotics has shown the promise of imitation learning across a variety of tasks. Such approaches are expensive both because they requir…
Bayesian Imitation Learning for End-to-End Mobile Manipulation
Yuqing Du, Daniel Ho, Alexander A. Alemi +2
In this work we investigate and demonstrate benefits of a Bayesian approach to imitation learning from multiple sensor inputs, as applied to the task of opening office doors with a…
SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning
Yifeng Jiang, Tingnan Zhang, Daniel Ho +4
As learning-based approaches progress towards automating robot controllers design, transferring learned policies to new domains with different dynamics (e.g. sim-to-real transfer)…