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            <h1 class="title is-1 publication-title"><span style="color:#A52B16; font-weight: bold;">Learning to Look</span> &#x1F440;</h1>
            <h2 class="subtitle is-2 publication-subtitle">Seeking information for Decision Making via Policy Factorization</h1>
            <div class="is-size-5 publication-authors">
              <!-- Paper authors -->
              <span class="author-block">
                <a href="https://shivindass.github.io/" target="_blank">Shivin Dass</a><sup>1</sup>,</span>
                <a href="https://jiahenghu.github.io/" target="_blank">Jiaheng Hu</a><sup>1</sup>,</span>
              <span class="author-block">
                <a href="https://babbatem.github.io/" target="_blank">Ben Abbatematteo</a><sup>1</sup>,</span>
              <span class="author-block">
                <a href="https://www.cs.utexas.edu/~pstone/" target="_blank">Peter Stone</a><sup>1,2</sup>,</span>
              <span class="author-block">
                <a href="https://robertomartinmartin.com/" target="_blank">Roberto Martín-Martín</a><sup>1</sup></span>
            </div>

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              <span class="author-block"><sup>1</sup>The University of Texas at Austin, <sup>2</sup>Sony AI
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        <h2 class="title is-3">Abstract</h2>
        <div class="content has-text-justified">
          <p>
            Many robot tasks require active or interactive exploration behavior in
            order to be performed successfully. Such tasks are ubiquitous in embodied do-
            mains, where agents must actively search for the information necessary for each
            stage of a task, e.g., moving the head of the robot to find information relevant
            to manipulation, or in multi-robot domains, where one scout robot may search
            for the information that another robot needs to make informed decisions. We
            identify these tasks with a new type of problem, factorized Contextual Markov
            Decision Processes, and propose DISaM, a dual-policy solution composed of an
            information-seeking policy that explores the environment to find the relevant con-
            textual information and an information-receiving policy that exploits the context
            to achieve the manipulation goal. This factorization allows us to train both poli-
            cies separately, using the information-receiving one to provide reward to train the
            information-seeking policy. At test time, the dual agent balances exploration and
            exploitation based on the uncertainty the manipulation policy has on what the next
            best action is. We demonstrate the capabilities of our dual policy solution in five
            manipulation tasks that require information-seeking behaviors, both in simulation
            and in the real-world, where DISaM significantly outperforms existing methods.
          </p>
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    <h2 class="title is-2" style="text-align: center;">Overview</h2>
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        <div class="content has-text-justified">
          <p>
            Traditionally, task conditioned robot polcies assume access to all information about the task such as the 
            reward function being optimized but intelligent agents such as humans know how to look for important information 
            in their surroundings and take relevant actions based on the context. For example, when given the task of serving 
            a beverage, looking at the time of day can inform the agent what to serve. 
          </p>
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    <h2 class="title is-2" style="text-align: center;">DISaM Training</h2>
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          <p>
            In this work we factorize the problem of looking for information and acting as 
            information-seeking (IS) and information-receiving (IR) respectively, where we train
            the IS agent to "look" for relevant task context and IR to act to complete the task. 
            Our method, DISaM (Dual Information-Seeking And Manipulation), splits the training into
            two phases -- In Phase 1, we learn the IR policy that takes in ground-truth context 
            information and controls the movement of the robot. In Phase 2, we learn an IS policy 
            as well as an image encoder such that the context can be correctly reconstructed from 
            the camera observation. Once all parts are trained, together they create a system that 
            takes in image observations and controls both the robot and the camera.
          </p>
        </div>
        <video poster="" id="tree" autoplay controls muted loop height="100%">
          <source src="static/videos/disam_training.mp4"
          type="video/mp4">
        </video>
      </div>
    </div>
  </div>
</section>

<section class="section">
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    <h2 class="title is-2" style="text-align: center;">DISaM Deployment</h2>
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        <div class="content has-text-justified">
          <p>
            During deployment, DISaM calculates the uncertainty of the IR policy over the next action by conditioning it on several contexts
            generated with the Encoder. If the uncertainty of the IR policy is high (above a threshold) then information-seeking actions are taken by the IS
            policy. When the correct context has been found by the IS policy, the IR uncertainty over the next action falls below the threshold
            and DISaM takes the IR actions to complete the task.
          </p>
        </div>
      </div>
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          <video poster="" id="tree" autoplay controls muted loop height="100%">
            <source src="static/videos/deployment.mp4"
            type="video/mp4">
          </video>
        </div>
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  </div>
</section>

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        <h2 class="title is-2" style="text-align: center;">Annotated Rollouts</h2>
        <div class="content has-text-justified">
          <p>
            Following videos demonstrate how the control is switched between the Information-Seeking (IS) and Information-Receiving (IR)
            agents. The text on top is semantic representation of the information that IR is uncertain about and we annotate the image 
            when IS is able to find that information. 
          </p>
        </div>
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              <div class="card-static-container">
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                      <h3><strong>Task: </strong>Cooking a dish</h3>
                      <video poster="" id="tree" autoplay controls muted loop height="100%">
                        <source src="static/videos/annotated_rollouts/cooking.mp4"
                        type="video/mp4">
                      </video>
                    </div>
                  </div>
                </div>

                <div class="card-container">
                  <div class="card-wide">
                    <div class="card-content">
                      <h3><strong>Task: </strong>Pick and place</h3>
                      <video poster="" id="tree" autoplay controls muted loop height="100%">
                        <source src="static/videos/annotated_rollouts/walls.mp4"
                        type="video/mp4">
                      </video>
                    </div>
                  </div>
                </div>

                <div class="card-container">
                  <div class="card-wide">
                    <div class="card-content">
                      <h3><strong>Task: </strong>Assemble the correct peg</h3>
                      <video poster="" id="tree" autoplay controls muted loop height="100%">
                        <source src="static/videos/annotated_rollouts/assembly.mp4"
                        type="video/mp4">
                      </video>
                    </div>
                  </div>
                </div>

                <div class="card-container">
                  <div class="card-wide">
                    <div class="card-content">
                      <h3><strong>Task: </strong>Choosing a beverage</h3>
                      <video poster="" id="tree" autoplay controls muted loop height="100%">
                        <source src="static/videos/annotated_rollouts/clock.mp4"
                        type="video/mp4">
                      </video>
                    </div>
                  </div>
                </div>

                <div class="card-container">
                  <div class="card-wide">
                    <div class="card-content">
                      <h3><strong>Task: </strong>Serving a beverage</h3>
                      <video poster="" id="tree" autoplay controls muted loop height="100%">
                        <source src="static/videos/annotated_rollouts/person.mp4"
                        type="video/mp4">
                      </video>
                    </div>
                  </div>
                </div>

                <div class="card-container">
                  <div class="card-wide">
                    <div class="card-content">
                      <h3><strong>Task: </strong>Pick fruit based on recipe</h3>
                      <video poster="" id="tree" autoplay controls muted loop height="100%">
                        <source src="static/videos/annotated_rollouts/button.mp4"
                        type="video/mp4">
                      </video>
                    </div>
                  </div>
                </div>

              </div>
          </div>
        </div>

      </div>
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    <h2 class="title is-2" style="text-align: center;">More Simulation Rollouts</h2>
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        <div class="content has-text-justified">
          <p>
            Below we provide more rollouts in the simulation environments to demonstrate the variety of behaviors IS policy learns. 
            The left frame corresponds to the the IS agent's observations and the right frame is a task visualization.
          </p>
        </div>
      </div>
    </div>
    <!-- Cooking -->
    <div class="columns is-centered">
      <div class="column is-one-third">
        <img src="static/images/task_gifs/cooking_1.gif" alt="cooking_1" width="90%" style="border: 1px solid #bbb; border-radius: 10px; margin: 1.0%;"/>
      </div>
      <div class="column is-one-third">
        <img src="static/images/task_gifs/cooking_2.gif" alt="cooking_2" width="90%" style="border: 1px solid #bbb; border-radius: 10px; margin: 1.0%;"/>
        <h4 class="title is-4" style="text-align: center;">Cooking</h3><br>
      </div>
      <div class="column is-one-third">
        <img src="static/images/task_gifs/cooking_3.gif" alt="cooking_3" width="90%" style="border: 1px solid #bbb; border-radius: 10px; margin: 1.0%;"/>
      </div>
    </div>

    <!-- Walls -->
    <div class="columns is-centered">
      <div class="column is-one-third">
        <img src="static/images/task_gifs/walls_1.gif" alt="walls_1" width="90%" style="border: 1px solid #bbb; border-radius: 10px; margin: 1.0%;"/>
      </div>
      <div class="column is-one-third">
        <img src="static/images/task_gifs/walls_2.gif" alt="walls_2" width="90%" style="border: 1px solid #bbb; border-radius: 10px; margin: 1.0%;"/>
        <h4 class="title is-4" style="text-align: center;">Walls</h3><br>
      </div>
      <div class="column is-one-third">
        <img src="static/images/task_gifs/walls_3.gif" alt="walls_3" width="90%" style="border: 1px solid #bbb; border-radius: 10px; margin: 1.0%;"/>
      </div>
    </div>

    <!-- Assembly -->
    <div class="columns is-centered">
      <div class="column is-one-third">
        <img src="static/images/task_gifs/assembly_1.gif" alt="assembly_1" width="90%" style="border: 1px solid #bbb; border-radius: 10px; margin: 1.0%;"/>
      </div>
      <div class="column is-one-third">
        <img src="static/images/task_gifs/assembly_2.gif" alt="assembly_2" width="90%" style="border: 1px solid #bbb; border-radius: 10px; margin: 1.0%;"/>
        <h4 class="title is-4" style="text-align: center;">Assembly</h3><br>
      </div>
      <div class="column is-one-third">
        <img src="static/images/task_gifs/assembly_3.gif" alt="assembly_3" width="90%" style="border: 1px solid #bbb; border-radius: 10px; margin: 1.0%;"/>
      </div>
    </div>

    <!-- Real -->
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         <img src="static/images/task_gifs/real_button.gif" alt="button_1" width="75%"/>
        </div>
        <h4 class="title is-4" style="text-align: center;">Button</h3>
      </div>
      <div class="column is-one-third">
        <img src="static/images/task_gifs/real_clock.gif" alt="clock_1" width="90%"/>
        <h4 class="title is-4" style="text-align: center;">Teatime (Clock)</h3>
      </div>
      <div class="column is-one-third">
        <img src="static/images/task_gifs/real_person.gif" alt="person_1" width="90%"/>
        <h4 class="title is-4" style="text-align: center;">Teatime (Person)</h3>
      </div>
    </div> -->

  </div>
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    <h2 class="title is-2" style="text-align: center;">Autonomous Policy Rollouts</h2>
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        <video poster="" id="video2" autoplay controls muted loop height="100%" style="border: 1px solid #bbb; border-radius: 10px; margin: 1.0%;">
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          type="video/mp4">
        </video>
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        <video poster="" id="video3" autoplay controls muted loop height="100%" style="border: 1px solid #bbb; border-radius: 10px; margin: 1.0%;">
          <source src="static/videos/il/slide_chair_no_border.mp4"
          type="video/mp4">
        </video>
      </div>
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  <!-- <section class="section" id="BibTeX">
    <div class="container is-max-desktop content">
      <h2 class="title">BibTeX</h2>
      <pre><code>
      @article{dass2024telemoma,
        title={TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation},
        author={Dass, Shivin and Ai, Wensi and Jiang, Yuqian and Singh, Samik and Hu, Jiaheng and Zhang, Ruohan and Stone, Peter and Abbatematteo, Ben and Martín-Martín, Roberto},
        journal={arXiv preprint arXiv:2403.07869},
        year={2024}
      }
      </code></pre>
    </div>
</section> -->
<!--End BibTex citation -->


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