The continuous pursuit of developing adaptable agents capable of effortlessly carrying out human-specified tasks has presented an ongoing challenge. A potential remedy comes in the form of harnessing natural language as an intuitive means for task specification. Nevertheless, the endeavor to train robots to adeptly follow language instructions has proven to be a formidable task, prompting the exploration of various methodologies, each characterized by distinct strengths and limitations. One innovative solution, the Goal Representations for Instruction Following (GRIF) model, aims to harmonize the ease of task specification through language with the enhanced performance achieved through goal-conditioned learning.

 

GRIF introduces a novel perspective on two crucial aspects of robotic learning: understanding language instructions within the physical world and executing a series of actions to accomplish a given task. Conventionally, these capabilities have been tackled through end-to-end learning from trajectories annotated by humans. However, the GRIF model takes a distinct path. Rather than depending solely on annotated trajectories, GRIF adopts a dual-source learning approach. It taps into vision-language data from external, non-robotic sources to enhance language comprehension, and simultaneously utilizes unlabeled robot trajectories to refine the mastery of achieving specified goals. This innovative learning strategy positions GRIF as a potential game-changer in the pursuit of creating versatile and adaptable robotic agents.

 

The GRIF model is constructed around three pivotal components - a language encoder, a goal encoder, and a policy network. These components work in harmony to map language instructions and goal images into a shared task representation space. This shared space then conditions the policy network for predicting actions. What distinguishes GRIF is its flexibility; it can be conditioned on either language instructions or goal images, effectively merging the benefits of both paradigms.

 

The core conceptual underpinning of an instruction-following robot involves grounding language instructions and executing actions. What sets GRIF apart is its departure from the conventional reliance on human-annotated trajectories alone. By incorporating vision-language data from diverse sources and unlabeled robot trajectories, GRIF establishes a robust foundation for generalization across varied instructions and scenes.

 

To effectively leverage both types of data, GRIF employs a joint training approach with language-conditioned behavioral cloning (LCBC) and goal-conditioned behavioral cloning (GCBC). The labeled dataset, enriched with human annotations, serves as the training ground for both language- and goal-conditioned predictions. Simultaneously, the unlabeled dataset, focused solely on goals, enhances the goal-conditioned aspect of the model. The key to GRIF's success lies in recognizing that language instructions and goal images often specify the same behavior, enabling seamless transfer between the two modalities.

 

An integral part of GRIF's training methodology involves aligning representations of state-goal pairs with language instructions. This is achieved through an infoNCE objective on matching pairs of language and goal representations. The objective is clear - encourage high similarity for the same task and discourage it for others. While drawing inspiration from pre-trained vision-language models like CLIP, GRIF fine-tunes these models to align task representations effectively.

 

The true litmus test for GRIF comes in its real-world evaluation. The model is put through its paces with 15 tasks across three scenes, ranging from well-represented instructions to novel, compositionally challenging ones. The evaluation demonstrates GRIF's superiority over plain LCBC and other baseline models. GRIF excels not only in grounding language instructions but also showcases robust manipulation capabilities, even in scenarios featuring unseen combinations of objects.

 

A critical aspect of evaluating GRIF is comparing it against existing baselines. Baseline models, such as LCBC and LLfP, exhibit limitations in manipulation capabilities, especially when faced with complex or novel instructions. BC-Z, despite incorporating an alignment strategy, struggles to generalize to new instructions without external vision-language data. GRIF, on the other hand, emerges as a standout performer, demonstrating the best generalization capabilities and robust manipulation skills.

 

The GRIF model provides a groundbreaking approach to reconcile language-based task specification with the performance advantages of goal-conditioned learning. By aligning task representations across language and goal modalities, GRIF offers a versatile and robust solution for training generalist robots. While acknowledging current limitations, such as the handling of qualitative task instructions, the GRIF model paves the way for future work. One exciting direction could involve extending alignment losses to leverage human video data, enriching semantics and enabling broader generalization.

In the ever-evolving landscape of robotics, the quest for creating versatile agents capable of seamlessly executing tasks specified by humans has been an enduring challenge. A potential solution lies in the utilization of natural language as an intuitive interface for task specification. However, training robots to effectively follow language instructions has proven to be a complex endeavor. This challenge has given rise to various approaches, each with its strengths and limitations. One such approach, the Goal Representations for Instruction Following (GRIF) model, seeks to reconcile the simplicity of specifying tasks through language with the performance gains of goal-conditioned learning.

 

At the heart of the challenge lies the need for a robot to possess two critical capabilities: the ability to ground language instructions in the physical environment and the proficiency to execute a sequence of actions to fulfill the intended task. Traditionally, these capabilities have been attempted to be learned end-to-end from human-annotated trajectories. However, the GRIF model proposes a different approach. Instead of relying solely on annotated trajectories, GRIF leverages vision-language data from non-robot sources for language grounding and unlabeled robot trajectories for mastering goal-reaching skills. This dual-source learning strategy positions GRIF as a potential breakthrough in the quest for generalist robotic agents.

The GRIF model is constructed around three pivotal components - a language encoder, a goal encoder, and a policy network. These components work in harmony to map language instructions and goal images into a shared task representation space. This shared space then conditions the policy network for predicting actions. What distinguishes GRIF is its flexibility; it can be conditioned on either language instructions or goal images, effectively merging the benefits of both paradigms.

 

The core conceptual underpinning of an instruction-following robot involves grounding language instructions and executing actions. What sets GRIF apart is its departure from the conventional reliance on human-annotated trajectories alone. By incorporating vision-language data from diverse sources and unlabeled robot trajectories, GRIF establishes a robust foundation for generalization across varied instructions and scenes.

To effectively leverage both types of data, GRIF employs a joint training approach with language-conditioned behavioral cloning (LCBC) and goal-conditioned behavioral cloning (GCBC). The labeled dataset, enriched with human annotations, serves as the training ground for both language- and goal-conditioned predictions. Simultaneously, the unlabeled dataset, focused solely on goals, enhances the goal-conditioned aspect of the model. The key to GRIF's success lies in recognizing that language instructions and goal images often specify the same behavior, enabling seamless transfer between the two modalities.

An integral part of GRIF's training methodology involves aligning representations of state-goal pairs with language instructions. This is achieved through an infoNCE objective on matching pairs of language and goal representations. The objective is clear - encourage high similarity for the same task and discourage it for others. While drawing inspiration from pre-trained vision-language models like CLIP, GRIF fine-tunes these models to align task representations effectively.

 

The true litmus test for GRIF comes in its real-world evaluation. The model is put through its paces with 15 tasks across three scenes, ranging from well-represented instructions to novel, compositionally challenging ones. The evaluation demonstrates GRIF's superiority over plain LCBC and other baseline models. GRIF excels not only in grounding language instructions but also showcases robust manipulation capabilities, even in scenarios featuring unseen combinations of objects. A critical aspect of evaluating GRIF is comparing it against existing baselines. Baseline models, such as LCBC and LLfP, exhibit limitations in manipulation capabilities, especially when faced with complex or novel instructions. BC-Z, despite incorporating an alignment strategy, struggles to generalize to new instructions without external vision-language data. GRIF, on the other hand, emerges as a standout performer, demonstrating the best generalization capabilities and robust manipulation skills.

 

The GRIF model provides a groundbreaking approach to reconcile language-based task specification with the performance advantages of goal-conditioned learning. By aligning task representations across language and goal modalities, GRIF offers a versatile and robust solution for training generalist robots. While acknowledging current limitations, such as the handling of qualitative task instructions, the GRIF model paves the way for future work. One exciting direction could involve extending alignment losses to leverage human video data, enriching semantics and enabling broader generalization.