Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environments

Understand

Data efficiency in robotic skill acquisition is crucial for operating robots in varied small-batch assembly settings.

• To operate in such environments, robots must have robust obstacle avoidance and versatile goal conditioning acquired from only a few simple demonstrations.
• Existing approaches, however, fall short of these requirements.
• Deep reinforcement learning (RL) enables a robot to learn complex manipulation tasks but is often limited to small task spaces in the real world due to sample inefficiency and safety concerns.