VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training (2210.00030v2)

Abstract: Reward and representation learning are two long-standing challenges for learning an expanding set of robot manipulation skills from sensory observations. Given the inherent cost and scarcity of in-domain, task-specific robot data, learning from large, diverse, offline human videos has emerged as a promising path towards acquiring a generally useful visual representation for control; however, how these human videos can be used for general-purpose reward learning remains an open question. We introduce $\textbf{V}$alue-$\textbf{I}$mplicit $\textbf{P}$re-training (VIP), a self-supervised pre-trained visual representation capable of generating dense and smooth reward functions for unseen robotic tasks. VIP casts representation learning from human videos as an offline goal-conditioned reinforcement learning problem and derives a self-supervised dual goal-conditioned value-function objective that does not depend on actions, enabling pre-training on unlabeled human videos. Theoretically, VIP can be understood as a novel implicit time contrastive objective that generates a temporally smooth embedding, enabling the value function to be implicitly defined via the embedding distance, which can then be used to construct the reward for any goal-image specified downstream task. Trained on large-scale Ego4D human videos and without any fine-tuning on in-domain, task-specific data, VIP's frozen representation can provide dense visual reward for an extensive set of simulated and $\textbf{real-robot}$ tasks, enabling diverse reward-based visual control methods and significantly outperforming all prior pre-trained representations. Notably, VIP can enable simple, $\textbf{few-shot}$ offline RL on a suite of real-world robot tasks with as few as 20 trajectories.

An Expert Review of "Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training"

This paper presents a novel approach to visual reward and representation learning for robot manipulation tasks. The proposed method, Value-Implicit Pre-training (VIP), leverages large-scale, unannotated human video data to create a representation that can generate dense, smooth reward functions for unseen robotic tasks without task-specific data fine-tuning. This capability addresses the inherent challenges of reward specification and representation learning in physical environments where privileged state information and predefined reward functions are often unavailable.

Core Contributions

1. Value-Implicit Pre-Training (VIP): The authors introduce a self-supervised learning framework, casting representation learning as an offline goal-conditioned reinforcement learning problem. VIP employs a novel goal-conditioned value function objective that is independent of actions, allowing it to train on unlabeled human videos. The method is underpinned by an implicit time contrastive learning mechanism, which promotes temporally smooth embeddings that define intrinsic value functions connected to goal-directed task progress.
2. Empirical Validations: Training on the extensive Ego4D dataset, VIP demonstrated superior performance in both simulated and real-world robot manipulation tasks across various test configurations. Notably, VIP outperformed prior methods in generating effective dense visual reward signals, enabling robots to successfully accomplish diverse tasks using simple few-shot offline reinforcement learning (RL) on as few as 20 trajectories.
3. Theoretical Foundations: The authors establish a connection between VIP and time contrastive learning, yet distinguish VIP by its implicit formulation which differs from conventional explicit temporal contrastive frameworks. This dual approach enables VIP to inherently smooth embedding spaces, effectively capturing long-range temporal dependencies and local temporal smoothness essential for RL applications.

Key Results and Implications

• Superior Performance in Control Tasks: VIP's dense reward functions demonstrated significant improvements over previous state-of-the-art representations in trajectory optimization and online RL settings. Specifically, VIP achieved around 30% success in complex control tasks without any task-specific representation tuning, improving further with increased computational resources.
• Correlation with Ground Truth Rewards: The paper highlights VIP's embedding rewards showing high correlation with ground-truth state-based rewards in certain tasks, which are indicative of its potential to replace manually-designed reward functions.
• Real-World Few-Shot Learning: Deploying VIP in a real-world setting, the authors revealed its capability to enable effective few-shot RL, which simplifies traditionally intensive data-driven methods by providing robust reward signals without additional human intervention.

Future Directions

The paper opens several avenues for further research, particularly in extending VIP to other goal-directed domains beyond robot manipulation, such as autonomous navigation. Another potential direction could involve optimizing fine-tuning strategies for VIP to improve task-specific performance further. Moreover, the application of quasimetrics as a refinement to the value function topology could enhance its adaptability to environments with asymmetric cost structures.

In summary, this paper marks a significant step in advancing universal visual reward functions through an innovative integration of human video data and innate value function learning. Although the current framework primarily targets robotic manipulation, its principles bear relevance to a broader spectrum of goal-conditioned AI applications.