Zero-Shot Visual Imitation (1804.08606v1)

Abstract: The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both 'what' and 'how' to imitate. We pursue an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its experience into a goal-conditioned skill policy with a novel forward consistency loss. In our framework, the role of the expert is only to communicate the goals (i.e., what to imitate) during inference. The learned policy is then employed to mimic the expert (i.e., how to imitate) after seeing just a sequence of images demonstrating the desired task. Our method is 'zero-shot' in the sense that the agent never has access to expert actions during training or for the task demonstration at inference. We evaluate our zero-shot imitator in two real-world settings: complex rope manipulation with a Baxter robot and navigation in previously unseen office environments with a TurtleBot. Through further experiments in VizDoom simulation, we provide evidence that better mechanisms for exploration lead to learning a more capable policy which in turn improves end task performance. Videos, models, and more details are available at https://pathak22.github.io/zeroshot-imitation/

• The paper introduces a zero-shot imitation learning framework that bypasses expert action supervision through autonomous exploration.
• It proposes a goal-conditioned skill policy optimized via a novel forward consistency loss to handle multi-modal action trajectories.
• Experimental results demonstrate significant improvements, including a boost in knot-tying accuracy on a Baxter robot for rope manipulation.

Overview of Zero-Shot Visual Imitation

The paper "Zero-Shot Visual Imitation" explores an innovative approach to imitation learning that diverges from the conventional reliance on expert actions as supervised input. Instead, it presents a framework where an agent autonomously explores an environment devoid of expert supervision. The exploration data is then leveraged to develop a goal-conditioned skill policy (GSP) using a novel forward consistency loss. During inference, the agent solely receives visual goals, communicated as a sequence of images, illustrating the task without any accompanying expert actions.

Core Concepts and Methodology

The central concept underpinning this research is the detachment of action supervision during the learning stage, instead requiring the agent to develop a self-guided understanding of transitional dynamics between observations. The policy learned, GSP, is robust enough to deduce the sequence of actions necessary to transition from the current state to the goal state, utilizing only visual input provided during inference.

The forward consistency loss introduced in this context mitigates the issue of multi-modal action trajectories by allowing varied action paths to be accepted, provided they result in the desired state transition. This is achieved by training a forward dynamics model to predict subsequent states given current observations and actions. The forward consistency loss ensures the GSP's actions lead to the correct state, even if they differ from a "ground truth" action set.

Experimental Evaluation

The research evaluates the zero-shot imitator in both real-world and simulated environments. Real-world evaluations were conducted on a Baxter robot for complex rope manipulation tasks and a TurtleBot for navigation in novel office environments. Notably, the results indicated a significant performance improvement in tasks with intricate dynamics—achieving a knot-tying accuracy of 60% as opposed to 36% with existing methods. The simulated experiments in VizDoom further illustrated the benefit of this approach, particularly when agent exploration was curiosity-driven rather than random.

Implications and Future Work

This work presents a substantial shift in autonomous learning paradigms, emphasizing the capacity for agents to generalize behavior understanding from unsupervised exploration to directed task imitation purely from visual goals. The introduction of a policy that doesn't require action-specific demonstrations broadens the applicability of imitation learning to scenarios where obtaining expert actions is impractical.

From a theoretical perspective, this approach contributes to the discourse on modeling action-related multi-modality effectively in autonomous systems. Practically, the method's ability to adapt without requiring substantial expert input during learning renders it advantageous in dynamic, real-world applications with visually discernable objectives but no clear action mapping.

Moving forward, this paradigm invites explorations into enhancing exploration strategies and adapting the model to accommodate third-person demonstrations. Furthermore, the integration of features such as domain adaptation to maintain robustness against environmental variability is a promising avenue for expanding the utility of zero-shot visual imitation in diverse operational contexts.