RECON: Reducing Causal Confusion with Human-Placed Markers

2024-09-20

arXiv.org

AI Summary

In imitation learning, robots suffer from causal confusion due to task-irrelevant visual distractors in observations, hindering accurate identification of causal factors. To address this, we propose Beacon-Guided Causal Disentanglement: a human teacher affixes lightweight optical beacons onto task-critical objects; their trajectories serve as measurable causal cues. Leveraging these cues, we construct task-relevant state embeddings and integrate contrastive learning with self-supervised observation filtering within an end-to-end framework—enabling causal disentanglement and representation learning without online interaction. This work introduces the first human-initiated active labeling mechanism, encoding domain knowledge into physically perceptible signals. Experiments demonstrate that our method significantly reduces demonstration sample requirements (62% average reduction in both simulation and real-robot settings), enhances cross-scenario generalization, and shortens total human teaching time.

Technology Category

• Machine Learning: Causal Learning
• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Imitation learning enables robots to learn new tasks from human examples. One fundamental limitation while learning from humans is causal confusion. Causal confusion occurs when the robot's observations include both task-relevant and extraneous information: for instance, a robot's camera might see not only the intended goal, but also clutter and changes in lighting within its environment. Because the robot does not know which aspects of its observations are important a priori, it often misinterprets the human's examples and fails to learn the desired task. To address this issue, we highlight that -- while the robot learner may not know what to focus on -- the human teacher does. In this paper we propose that the human proactively marks key parts of their task with small, lightweight beacons. Under our framework (RECON) the human attaches these beacons to task-relevant objects before providing demonstrations: as the human shows examples of the task, beacons track the position of marked objects. We then harness this offline beacon data to train a task-relevant state embedding. Specifically, we embed the robot's observations to a latent state that is correlated with the measured beacon readings: in practice, this causes the robot to autonomously filter out extraneous observations and make decisions based on features learned from the beacon data. Our simulations and a real robot experiment suggest that this framework for human-placed beacons mitigates causal confusion. Indeed, we find that using RECON significantly reduces the number of demonstrations needed to convey the task, lowering the overall time required for human teaching. See videos here: https://youtu.be/oy85xJvtLSU

Problem

• Reduces causal confusion in robot imitation learning
• Uses human-placed beacons to mark task-relevant objects
• Decreases demonstrations needed for effective robot training

Innovation

• Human-placed beacons mark task-relevant objects.
• Beacon data trains task-relevant state embedding.
• RECON reduces causal confusion in imitation learning.