539 citations · 1.5k across the 37 of their papers we have counts for
8 papers · 1 filter
Training Robots to Evaluate Robots: Example-Based Interactive Reward Functions for Policy Learning
Kun Huang, Edward S. Hu, Dinesh Jayaraman
Physical interactions can often help reveal information that is not readily apparent. For example, we may tug at a table leg to evaluate whether it is built well, or turn a water b…
Long-HOT: A Modular Hierarchical Approach for Long-Horizon Object Transport
Sriram Narayanan, Dinesh Jayaraman, Manmohan Chandraker
We address key challenges in long-horizon embodied exploration and navigation by proposing a new object transport task and a novel modular framework for temporally extended navigat…
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman +3
Reward and representation learning are two long-standing challenges for learning an expanding set of robot manipulation skills from sensory observations. Given the inherent cost an…
Vision-based Perimeter Defense via Multiview Pose Estimation
Elijah S. Lee, Giuseppe Loianno, Dinesh Jayaraman +1
Previous studies in the perimeter defense game have largely focused on the fully observable setting where the true player states are known to all players. However, this is unrealis…
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming
Chuan Wen, Jianing Qian, Jierui Lin +3
Across applications spanning supervised classification and sequential control, deep learning has been reported to find "shortcut" solutions that fail catastrophically under minor c…
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via -Advantage Regression
Yecheng Jason Ma, Jason Yan, Dinesh Jayaraman +1
Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill learning in the form of reaching diverse goals from purely offline datasets. We propose $\text…
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
Yecheng Jason Ma, Andrew Shen, Dinesh Jayaraman +1
We propose State Matching Offline DIstribution Correction Estimation (SMODICE), a novel and versatile regression-based offline imitation learning (IL) algorithm derived via state-o…
Prospective Learning: Principled Extrapolation to the Future
Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…