Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Developing agents that can learn to follow natural language instructions has been an emerging research direction.
While being accessible and flexible, natural language instructions can sometimes be ambiguous even to humans.
To address this, we propose to utilize programs, structured in a formal language, as a precise and expressive way to specify tasks.
We then devise a modular framework that learns to perform a task specified by a program – as different circumstances give rise to diverse ways to accomplish the task, our framework can perceive which circumstance it is currently under, and instruct a multitask policy accordingly to fulfill each subtask of the overall task.
Experimental results on a 2D Minecraft environment not only demonstrate that the proposed framework learns to reliably accomplish program instructions and achieves zero-shot generalization to more complex instructions but also verify the efficiency of the proposed modulation mechanism for learning the multitask policy.
We also conduct an analysis comparing various models which learn from programs and natural language instructions in an end-to-end fashion.
Humans are capable of leveraging instructions to accomplish complex tasks.
A comprehensive instruction usually comprises a set of descriptions detailing a variety of situations and the corresponding subtasks that are required to be fulfilled.
To accomplish a task, we can leverage instructions to estimate the progress, recognize the current state, and perform corresponding actions.
For example, to make a gourmet dish, we can follow recipes and procedurally create the desired dish by recognizing what ingredients and tools are missing, what alternatives are available, and what corresponding preparations are required.
With sufficient practice, we can improve our ability to perceive (e.g. knowing when food is well-cooked) as well as master cooking skills (e.g. cutting food into same-sized pieces), and eventually accomplish difficult recipes.
Can machines likewise learn to follow and exploit comprehensive instructions like humans?
Utilizing expert demonstrations to instruct agents has been widely studied in (Finn et al., 2017; Yu et al., 2018b; Xu et al., 2018; Pathak et al., 2018; Stadie et al., 2017; Duan et al., 2017; Wang et al., 2017b) .
However, demonstrations could be expensive to obtain and are less flexible (e.g. altering subtask orders in demonstrations is nontrivial).
On the other hand, natural language instructions are flexible and expressive (Malmaud et al., 2014; Jermsurawong & Habash, 2015; Kiddon et al., 2015; Misra et al., 2018; Fried et al., 2018; Kaplan et al., 2017; Bahdanau et al., 2019 ).
Yet, language has the caveat of being ambiguous even to humans, due to its lacking of structure as well as unclear coreferences and entities.
Andreas et al. (2017a) ; Oh et al. (2017) investigate a hierarchical approach, where the instructions consist of a set of symbolically represented subtasks.
Nonetheless, those instructions are not a function of states (i.e. describe a variety of circumstances and the corresponding desired subtasks), which substantially limits their expressiveness.
We propose to utilize programs, written in a formal language, as a structured, expressive, and unambiguous representation to specify tasks.
Specifically, we consider programs, which are composed of control flows (e.g. if/else and loops), environmental conditions, as well as corresponding subtasks, as shown in Figure 1 .
Not only do programs have expressiveness by describing diverse situations (e.g. a river exists) and the corresponding subtasks which are required to be executed (e.g. mining wood), but they are also unambiguous due to their explicit scoping.
To study the effectiveness of using programs as task specifications, we introduce a new problem, where we aim to develop a framework We are interested in learning to fulfill tasks specified by written programs.
A program consists of control flows (e.g. if, while), branching conditions (e.g. is_there [River] ), and subtasks (e.g. mine(Wood)).
which learns to comprehend a task specified by a program as well as perceive and interact with the environment to accomplish the task.
To address this problem, we propose a modular framework, program guided agent, which exploits the structural nature of programs to decompose and execute them as well as learn to ground program tokens with the environment.
Specifically, our framework consists of three modules: (1) a program interpreter that leverages a grammar provided by the programming language to parse and execute a program, (2) a perception module that learns to respond to conditional queries (e.g. is_there [River] ) produced by the interpreter, and (3) a policy that learns to fulfill a variety of subtasks (e.g. mine(Wood)) extracted from the program by the interpreter.
To effectively instruct the policy with symbolically represented subtasks, we introduce a learned modulation mechanism that leverages a subtask to modulate the encoded state features instead of concatenating them.
Our framework (shown in Figure 3 ) utilizes a rule-based program interpreter to deal with programs as well as learning perception module and policy when it is necessary to perceive or interact with the environment.
With this modularity, our framework can generalize to more complex program-specified tasks without additional learning.
To evaluate the proposed framework, we consider a Minecraft-inspired 2D gridworld environment, where an agent can navigate itself across different terrains and interact with objects, similar to Andreas et al. (2017a) ; Sohn et al. (2018) .
A corresponding domain-specific language (DSL) defines the rules of constructing programs for instructing an agent to accomplish certain tasks.
Our proposed framework demonstrates superior generalization ability -learning from simpler tasks while generalizing to complex tasks.
We also conduct extensive analysis on various end-to-end learning models which learns from not only program instructions but also natural language descriptions.
Furthermore, our proposed learned policy modulation mechanism yields a better learning efficiency compared to other commonly used methods that simply concatenate a state and goal.
We propose to utilize programs, structured in a formal language, as an expressive and precise way to specify tasks instead of commonly used natural language instructions.
We introduce the problem of developing a framework that can comprehend a program as well as perceive and interact with the environment to accomplish the desired task.
To address this problem, we devise a modular framework, program guided agent, which executes programs with a program interpreter by altering between querying a perception module when a branching condition is encountered and instructing a policy to fulfill subtasks.
We employ a policy modulation mechanism to improve the efficiency of learning the multitask policy.
The experimental results on a 2D Minecraft environment demonstrate that the proposed framework learns to reliably fulfill program instructions and generalize well to more complex instructions without additional training.
We also investigate the performance of various models that learn from programs and natural language descriptions in an end-to-end fashion.
Michael Janner, Karthik Narasimhan, and Regina Barzilay.
To fuse the information from an input domain (e.g. an image) with another condition domain (e.g. a language query, image such as segmentation map, noise, etc.), a wide range of works have demonstrated the effectiveness of predicting affine transforms based on the condition to scale and bias the input in visual question answering (Perez et al., 2018; 2017) , image synthesis (Almahairi et al., 2018; Karras et al., 2019; Park et al., 2019; Huh et al., 2019) , style transfer (Dumoulin et al., 2017) , recognition (Hu et al., 2018; Xie et al., 2018 ), reading comprehension (Dhingra et al., 2017 , few-shot learning (Oreshkin et al., 2018; Lee & Choi, 2018) , etc.
Many of those works present an extensive ablation study to compare the learned modulation against traditional ways to merge the information from the input and condition domains.
Recently, a few works have employed a similar learned modulation technique to reinforcement learning frameworks on learning to follow language instruction (Bahdanau et al., 2019) and metareinforcement learning (Vuorio et al., 2018; 2019) .
However, there has not been a comprehensive ablation study that suggests fusing the information from the input domain (e.g. a state) and the condition domain (e.g. a goal or a task embedding) for the reinforcement learning setting.
In this work, we conduct an ablation study in our 2D Minecraft environment where an agent is required to fulfill a navigational task specified by a program and show the effectiveness of learning to modulate input features with symbolically represented goal as well as present a number of modulation variations (i.e. modulating the fully-connected layers or the convolutional layers or both).
We look forward to future research that verifies if this learned modulation mechanism is effective in dealing with more complex domains such as robot manipulation or locomotion.
We propose a modular framework that can accomplish tasks specified by programs and achieve zero-shot generalization to more complex tasks.