Search Results for author:
Found 6 papers, 4 papers with code
Planning Goals for Exploration
We address this question within the goal-conditioned reinforcement learning paradigm, by identifying how the agent should set its goals at training time to maximize exploration.
Training Robots to Evaluate Robots: Example-Based Interactive Reward Functions for Policy Learning
Physical interactions can often help reveal information that is not readily apparent.
Transferable Visual Control Policies Through Robot-Awareness
Training visual control policies from scratch on a new robot typically requires generating large amounts of robot-specific data.
Know Thyself: Transferable Visual Control Policies Through Robot-Awareness
Training visual control policies from scratch on a new robot typically requires generating large amounts of robot-specific data.
To Follow or not to Follow: Selective Imitation Learning from Observations
Learning from demonstrations is a useful way to transfer a skill from one agent to another.
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks
The IKEA Furniture Assembly Environment is one of the first benchmarks for testing and accelerating the automation of complex manipulation tasks.
