OSVI-WM: One-Shot Visual Imitation for Unseen Tasks using World-Model-Guided Trajectory Generation (2505.20425v1)

Abstract: Visual imitation learning enables robotic agents to acquire skills by observing expert demonstration videos. In the one-shot setting, the agent generates a policy after observing a single expert demonstration without additional fine-tuning. Existing approaches typically train and evaluate on the same set of tasks, varying only object configurations, and struggle to generalize to unseen tasks with different semantic or structural requirements. While some recent methods attempt to address this, they exhibit low success rates on hard test tasks that, despite being visually similar to some training tasks, differ in context and require distinct responses. Additionally, most existing methods lack an explicit model of environment dynamics, limiting their ability to reason about future states. To address these limitations, we propose a novel framework for one-shot visual imitation learning via world-model-guided trajectory generation. Given an expert demonstration video and the agent's initial observation, our method leverages a learned world model to predict a sequence of latent states and actions. This latent trajectory is then decoded into physical waypoints that guide the agent's execution. Our method is evaluated on two simulated benchmarks and three real-world robotic platforms, where it consistently outperforms prior approaches, with over 30% improvement in some cases.

Analysis of OSVI-WM: One-Shot Visual Imitation for Unseen Tasks using World-Model-Guided Trajectory Generation

The paper presents a novel framework named OSVI-WM, aiming to enhance one-shot visual imitation learning (OSVI) by employing world-model-guided trajectory generation. Traditional imitation learning allows robotic agents to acquire skills by observing demonstration videos, usually without consideration for unseen or semantically different tasks. However, OSVI-WM introduces a paradigm capable of tackling these challenges, effectively predicting trajectories for unseen tasks.

Key Contributions

OSVI-WM leverages a learned world model to predict sequences of latent states and actions. The proposed architecture significantly improves upon traditional methodologies by encoding an expert demonstration video alongside the agent’s initial observation. From this input, it generates a latent trajectory which is decoded into physical waypoints guiding the agent’s actions. The paper claims a performance improvement exceeding 30% over existing techniques in various benchmarks. An efficient end-to-end training approach is pivotal, negating the need for large-scale pretraining and significantly simplifying the solution pipeline.

Approach and Methodology

The architecture comprises several key components:

• World-Model-Guided Trajectory Generation: This module predicts future latent states recursively using action and world models, which together forecast the trajectory critical for task completion.
• Latent Space Operations: Transitions occur within a latent space, alleviating the complexity associated with multi-modality inherent in action distributions.
• Waypoint Prediction Module: Using the predicted latent trajectory, the module computes physical waypoints necessary for control, incorporating spatial and temporal pooling for efficient processing.
• Training Loss Functions: A robust training regimen incorporating the Soft Dynamic Time Warping (Soft-DTW) loss and a supervised world model loss ensures the state predictions align accurately with ground truth observations.

Experimental Validation

OSVI-WM's efficacy is demonstrated across both simulated and real-world settings. Evaluations on the Meta-World simulation benchmark show impressive generalization to unfamiliar tasks. Performance in real-world environments further corroborates its versatility, with intricate setups such as embodiment mismatch between human and robotic entities posing no barrier to successful task imitation.

The findings accentuate the importance of OSVI-WM’s world model, which grants the agent foresight in terms of future states and guides it to reason beyond mere observation-based learning. Moreover, the capability to re-plan midway through execution underscores its resilience in facing unexpected execution errors.

Implications and Future Directions

Practically, OSVI-WM presents significant implications for autonomous systems. It can be instrumental in domains demanding fluid adaptation to novel tasks, such as medical assistance or industrial robotics. The framework's ability to generalize from a single demonstration renders it apt for inclusion in settings where data availability is sparse, and costs for data collection are high.

Looking forward, expanding upon the current trajectory predictions to incorporate orientation changes could address complex tasks involving rotational elements. Furthermore, adaptive strategies for sequential task execution or multitasking could propel OSVI-WM toward more comprehensive robotic autonomy.

This paper delineates a formidable advancement in OSVI by effectively addressing task generalization through a learned model predictive framework, setting a precedent for subsequent research endeavors in AI-driven robotic imitation learning.