Original article: http://bair.berkeley.edu/blog/2023/10/17/grif/

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Title: Enhancing Robot Learning with Goal Representations for Instruction Following

Introduction:
The fusion of natural language and robotics holds immense promise in enabling humans to interact with machines seamlessly. However, training robots to comprehend and execute language instructions remains a significant challenge in the field of AI. This article explores the evolution of language-conditioned behavioral cloning (LCBC) to more advanced goal-conditioned learning approaches and delves into the synergy between task specificity and performance improvements.

The Dual Requirements of an Instruction-Following Robot
To successfully follow instructions, a robot must first ground language commands in its physical environment and then execute a sequence of actions to accomplish the task at hand. Rather than relying solely on annotated human demonstrations, a more effective strategy involves learning these capabilities separately using diverse data sources. Vision-language data from external sources aids in learning language grounding with versatility, while unlabeled robot trajectories contribute to training the robot to achieve specific goal states independently of language cues.

Integrating Visual Goals in Policy Learning
Conditioning robot policies on visual goals, such as goal images, offers a compelling method for scaling task specification and training policies efficiently. Visual goals allow for hindsight relabeling, enabling policies to be trained on substantial amounts of unstructured trajectory data, including autonomous robot-collected data. The direct comparison of goal images with other states facilitates efficient grounding of tasks, enhancing policy learning and performance.

Balancing Task Specification and User Interaction
While visual goals excel in policy learning, they may lack the intuitive appeal of natural language for human users. In most cases, users find it more straightforward to articulate desired tasks through language instructions rather than providing visual representations. By incorporating a language interface for goal-conditioned policies, a harmonious blend of task specificity and user-friendliness can be achieved, empowering the creation of versatile and easily commanded robots.

The GRIF Model: A Fusion of Language and Goal Representations
The Goal Representations for Instruction Following (GRIF) model embodies a novel approach by concurrently training a language- and goal-conditioned policy with aligned task representations. This alignment bridges the gap between traditional language-focused learning and goal-driven approaches, offering policies that generalize across diverse instructions and scenes, leveraging primarily unlabeled demonstration data.

Contrastive Learning for Representation Alignment
GRIF employs contrastive learning to align task representations in both language-conditioned and goal-conditioned scenarios, emphasizing the relationship between language instructions and goal images. This alignment strategy, using a combination of language and goal representations, empowers policies to understand task semantics and perform actions effectively across varied environments.

Empirical Results and Policy Performance
Through real-world evaluations across multiple tasks and scenes, the GRIF policy demonstrates superior generalization and manipulation capabilities compared to baseline approaches like LCBC and LangLfP. By effectively grounding language instructions and executing tasks accurately, GRIF showcases the potential of aligning language-goal task representations for enhanced robot learning and performance.

Conclusion:
GRIF’s innovative integration of language instruction and visual goal representations paves the way for robust, generalist robots capable of seamless interaction. By combining the strengths of goal-conditioned learning and language grounding, GRIF exemplifies a pathway towards efficient and versatile robot learning, leveraging unlabeled trajectory data for improved performance.

For further exploration into the GRIF model and its implications for instruction following in robotics, refer to the paper “Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control” by Vivek Myers et al. presented at the Conference on Robot Learning, 2023.

By embracing the principles and insights of GRIF in your work, remember to cite the research as follows:

@inproceedings{myers2023goal,
title={Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control},
author={Vivek Myers and Andre He and Kuan Fang and Homer Walke and Philippe Hansen-Estruch and Ching-An Cheng and Mihai Jalobeanu and Andrey Kolobov and Anca Dragan and Sergey Levine},
booktitle={Conference on Robot Learning},
year={2023},
}