565 citations · 4.8k across the 132 of their papers we have counts for
59 papers · 1 filter
Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty
Rowan McAllister, Gregory Kahn, Jeff Clune +1
Deep learning provides a powerful tool for machine perception when the observations resemble the training data. However, real-world robotic systems must react intelligently to thei…
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…
Visual Memory for Robust Path Following
Ashish Kumar, Saurabh Gupta, David Fouhey +2
Humans routinely retrace paths in a novel environment both forwards and backwards despite uncertainty in their motion. This paper presents an approach for doing so. Given a demonst…
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
Frederik Ebert, Chelsea Finn, Sudeep Dasari +3
Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability dem…
Learning to Walk via Deep Reinforcement Learning
Tuomas Haarnoja, Sehoon Ha, Aurick Zhou +3
Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domai…
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan +6
Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation to produce large amou…