An Auto-Tuning Framework for Controllers using Auto-Differentiation
Train Offline, Test Online: A Real Robot Learning Benchmark
Watch and Match: Supercharging Imitation with Regularized Optimal Transport
Spotlight Session 2
Planning Goals for Exploration
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials
Leveraging Haptic Feedback to Improve Data Quality and Quantity for Deep Imitation Learning Models
Robots Need Initiative and Autonomy