2k citations · 3.4k across the 66 of their papers we have counts for
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Guiding Policies with Language via Meta-Learning
John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev +5
Behavioral skills or policies for autonomous agents are conventionally learned from reward functions, via reinforcement learning, or from demonstrations, via imitation learning. Ho…
Learning Actionable Representations with Goal-Conditioned Policies
Dibya Ghosh, Abhishek Gupta, Sergey Levine
Representation learning is a central challenge across a range of machine learning areas. In reinforcement learning, effective and functional representations have the potential to t…
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost
Henry Zhu, Abhishek Gupta, Aravind Rajeswaran +2
Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills, making them an appealing component for general-purpose robotic manipulators. However, such h…
Adversarial Reinforcement Learning for Observer Design in Autonomous Systems under Cyber Attacks
Abhishek Gupta, Zhaoyuan Yang
Complex autonomous control systems are subjected to sensor failures, cyber-attacks, sensor noise, communication channel failures, etc. that introduce errors in the measurements. Th…
Automatically Composing Representation Transformations as a Means for Generalization
Michael B. Chang, Abhishek Gupta, Sergey Levine +1
A generally intelligent learner should generalize to more complex tasks than it has previously encountered, but the two common paradigms in machine learning -- either training a se…
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
John D. Co-Reyes, YuXuan Liu, Abhishek Gupta +3
In this work, we take a representation learning perspective on hierarchical reinforcement learning, where the problem of learning lower layers in a hierarchy is transformed into th…