384 citations · 1.5k across the 50 of their papers we have counts for
5 papers · 1 filter
Asymmetric Actor Critic for Image-Based Robot Learning
Lerrel Pinto, Marcin Andrychowicz, Peter Welinder +2
Deep reinforcement learning (RL) has proven a powerful technique in many sequential decision making domains. However, Robotics poses many challenges for RL, most notably training o…
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Arun Venkatraman, Nicholas Rhinehart, Wen Sun +5
Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement trai…
CASSL: Curriculum Accelerated Self-Supervised Learning
Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi +1
Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces. However, scaling this framewor…
Learning to Fly by Crashing
Dhiraj Gandhi, Lerrel Pinto, Abhinav Gupta
How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles? One approach is to use a small dataset collected by human experts: however, high capacity learnin…
Robust Adversarial Reinforcement Learning
Lerrel Pinto, James Davidson, Rahul Sukthankar +1
Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL). However, most current RL-based…