307 citations · 724 across the 15 of their papers we have counts for
7 papers · 1 filter
Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision
Ashvin Nair, Brian Zhu, Gokul Narayanan +2
Learning-based methods in robotics hold the promise of generalization, but what can be done if a learned policy does not generalize to a new situation? In principle, if an agent ca…
Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks
Kuan Fang, Patrick Yin, Ashvin Nair +3
The utilization of broad datasets has proven to be crucial for generalization for a wide range of fields. However, how to effectively make use of diverse multi-task data for novel…
Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space
Kuan Fang, Patrick Yin, Ashvin Nair +1
General-purpose robots require diverse repertoires of behaviors to complete challenging tasks in real-world unstructured environments. To address this issue, goal-conditioned reinf…
What Can I Do Here? Learning New Skills by Imagining Visual Affordances
Alexander Khazatsky, Ashvin Nair, Daniel Jing +1
A generalist robot equipped with learned skills must be able to perform many tasks in many different environments. However, zero-shot generalization to new settings is not always p…
Contextual Imagined Goals for Self-Supervised Robotic Learning
Ashvin Nair, Shikhar Bahl, Alexander Khazatsky +3
While reinforcement learning provides an appealing formalism for learning individual skills, a general-purpose robotic system must be able to master an extensive repertoire of beha…
Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards
Gerrit Schoettler, Ashvin Nair, Jianlan Luo +4
Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction. Since it is difficult to capture related phy…
Residual Reinforcement Learning for Robot Control
Tobias Johannink, Shikhar Bahl, Ashvin Nair +6
Conventional feedback control methods can solve various types of robot control problems very efficiently by capturing the structure with explicit models, such as rigid body equatio…