Learning Actionable Representations from Visual Observations

Debidatta Dwibedi*, Jonathan Tompson, Corey Lynch*, Pierre Sermanet

Google Brain

*Work done as a part of the Google AI Residency program

Paper accepted at IROS '18

[Paper] [Bibtex] [Video] [Slides]

ABSTRACT

In this work, we explore a new approach for robots to teach themselves about the world simply by observing it. In particular, we investigate the effectiveness of learning task-agnostic representations for continuous control tasks. We extend Time-Contrastive Networks (TCN) that learn from visual observations by embedding multiple frames jointly in the embedding space as opposed to a single frame. We show that by doing so, we are now able to encode both position and velocity attributes significantly more accurately. We test the usefulness of this self-supervised approach in a reinforcement learning setting. We show that the representations learned by agents observing themselves take random actions, or other agents perform tasks successfully, can enable the learning of continuous control policies using algorithms like Proximal Policy Optimization (PPO) using only the learned embeddings as input. We also demonstrate significant improvements on the real-world Pouring dataset with a relative error reduction of 39.4% for motion attributes and 11.1% for static attributes compared to the single-frame baseline.

APPROACH

Step 1: Observe agents interacting with their environments from multiple views

Cartpole taking random actions

Cheetah performing Walk task

Step 2: Learn visual representations by embedding multiple frames jointly

Step 3: Train control policies with learned visual representations as input