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<!DOCTYPE html>
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<title>MPI: Learning Manipulation by Predicting Interaction</title>
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<h1 class="title is-1 publication-title">Learning Manipulation by Predicting Interaction</h1>
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<!-- Author Names Omitted for Anonymous Review. -->
<!-- <a target="_blank" href="https://shikharbahl.github.io/">Jia Zeng</a><sup>*</sup><sup>1,2</sup>
<a target="_blank" href="https://russellmendonca.github.io/">Qingwen Bu</a><sup>*</sup><sup>1</sup>
<a target="_blank" href="http://www.lilichen.me/">Bangjun Wang</a><sup>2</sup>
<a target="_blank" href="https://unnat.github.io/">Unnat Jain</a><sup>1,2</sup>
<a target="_blank" href="https://www.cs.cmu.edu/~dpathak/">Deepak Pathak</a><sup>1</sup> -->
<a target="_blank" href="https://scholar.google.com/citations?user=kYrUfMoAAAAJ&hl=zh-CN">Jia Zeng</a><sup>*</sup><sup>1</sup>
<a target="_blank" href="https://scholar.google.com/citations?user=-JCRysgAAAAJ&hl=zh-CN&oi=ao">Qingwen Bu</a><sup>*</sup><sup>2,1</sup>
<a target="_blank" href="http://wbjsamuel.github.io/">Bangjun Wang</a><sup>*</sup><sup>2,1</sup>
<a target="_blank" href="https://scholar.google.com/citations?user=v69hlTUAAAAJ&hl=zh-CN&oi=ao">Wenke Xia</a><sup>*</sup><sup>3,1</sup>
<br>
<a target="_blank" href="https://scholar.google.com/citations?user=ulZxvY0AAAAJ&hl=zh-CN">Li Chen</a><sup>1</sup>
Hao Dong</a><sup>4</sup>
Haoming Song</a><sup>1</sup>
Dong Wang</a><sup>1</sup>
Di Hu<sup>3</sup>
Ping Luo<sup>1</sup>
<br>
Heming Cui<sup>1</sup>
Bin Zhao<sup>1,5</sup>
Xuelong Li<sup>1,6</sup>
Yu Qiao<sup>1</sup>
Hongyang Li<sup>1,2</sup>
<br /><sup>1</sup>Shanghai AI Lab <sup>2</sup>Shanghai Jiao Tong University <sup>3</sup>Renmin University of China
<br /><sup>4</sup>Peking University <sup>5</sup>Northwestern Polytechnical University <sup>6</sup>TeleAI, China Telecom Corp Ltd
<span class="brmod" style="color:rgb(183, 0, 0)"><b>RSS 2024</b></span>
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<h2 class="subtitle">
<!-- Given a scene, our approach (VRB) learns <strong> actionable representations </strong> for robot learning. VRB predicts contact points and a post-contact trajectory learned from <strong> human videos </strong>. -->
<strong> MPI </strong> is an <strong>interaction-oriented</strong> representation learning pipeline for robotic manipulation. Diverging from prior arts grounded in (a) Contrastive Learning, (b) Masked Signal Modeling, or (c) Video Prediction using random frames, our proposed approach in (d) instructs the model towards predicting transition frames and detecting manipulated objects with keyframes as input. As such, the model fosters better comprehension of “how-to-interact” and “where-to-interact”. MPI acquires more informative representations during pre-training and achieves evident improvement across downstream tasks.
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<h2 class="title is-3">Abstract</h2>
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Representation learning methods towards robot manipulation have blossomed in recent years. Due to the scarcity of in-domain robot demonstrations, prevailing approaches tend to leverage large-scale video datasets of human-object interaction, to build reusable features for visuomotor policy learning. However, these methods focus only on high-level semantic cues or low-level pixel information, and disregard the interactive dynamics in manipulation processes, resulting in suboptimal empowerment for downstream tasks. Hereby, we propose <strong>MPI</strong>, which enhances interaction-oriented representation by simulating the manipulation process. We decompose each action sequence into the initial state, transitional state, and final state. Given a pair of keyframes representing the initial and final states, along with language instructions, the model is required to predict the image of the transition state and locate the interaction object. We conduct a comprehensive evaluation across five distinct robot learning tasks. The experimental results demonstrate that <strong>MPI</strong> exhibits remarkable improvement compared to state-of-the-art methods in both real-world robot platforms and simulation environments.
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<h1 class="title is-2" style="text-align: center; padding-bottom: 10px;">Model Overview</h1>
<!-- <h2 class="subtitle is-4" style="text-align: center;">Defining Visual Affordances</h2> -->
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<p style="text-align: center;font-size: 18px"> MPI comprises a multi-modal transformer encoder and a transformer decoder designed for predicting the image of the target interaction state and detecting interaction objects respectively. We achieve synergistic modeling and optimization of the two tasks through information transition between the prediction and detection transformers. The decoder is solely engaged during the pre-training phase while deprecated for downstream adaptations.</p>
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<h1 class="title is-2" style="text-align: center; padding-bottom: 10px;">Real-world Visuomotor Control Tasks</h1>
<!-- <h2 class="subtitle is-4" style="text-align: center;">Defining Visual Affordances</h2> -->
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<video class="center" playsinline autoplay loop muted src="./resources/mpi/visuomotor/take_the_spatula_2.mp4" width="100%" style="border-radius:10px;"></video>
<center>Take the spatula off the shelf (2x speed)</center>
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<video class="center" playsinline autoplay loop muted src="./resources/mpi/visuomotor/lift_up_the_pot_lid_2.mp4" width="100%" style="border-radius:10px;"></video>
<center>Lift up the pot lid (2x speed)</center>
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<video class="center" playsinline autoplay loop muted src="./resources/mpi/visuomotor/close_drawer_2.mp4" width="100%" style="border-radius:10px;"></video>
<center>Close the drawer (2x speed)</center>
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<video class="center" playsinline autoplay loop muted src="./resources/mpi/visuomotor/lift_up_the_pot_lid_2.mp4" width="100%" style="border-radius:10px;"></video>
<center> Put pot into sink (2x speed)</center>
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<h2 class="subtitle is-3" style="text-align: center;">Success Rates of Real-World Experiments</h2>
<p>
To provide a comprehensive evaluation of the effectiveness of different pre-trained encoders, we design two distinct scenarios with varying levels of complexity. The first scenario consists of ten diverse manipulation tasks in a clean background. These tasks require fundamental manipulation skills such as Pick & Place, articulated object manipulation, etc. In addition, we construct a more challenging kitchen environment that incorporates various interfering objects and backgrounds relevant to the target tasks. In this environment, we present five tasks: 1) taking the spatula off the shelf, 2) putting the pot into the sink, 3) putting the banana into the drawer, 4) lifting the lid, and 5) closing the drawer. As shown in Fig. 3(a), the complexity of these scenarios necessitates the visual encoder to possess both the “how-to-interact” and “where-to-interact” abilities to effectively handle these tasks.
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<img src="resources/mpi/real_world_exp.png" alt="Image description" width="100%">
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<h2 class="subtitle is-3" style="text-align: center;">Analysis: Real-World Experiment Details</h2>
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<h1 class="title is-2" style="text-align: center; padding-bottom: 10px;">Robustness to Real-World Distractions</h1>
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<p>
To genuinely reflect various encoder architectures’ capabilities in data-efficient robotic learning within real-world environments, we developed a series of complex manipulation tasks both in kitchen environment(5 tasks) and in clean background(10 tasks). The complex scenarios need the visual encoder to have not only <strong>"how to interact"</strong> but also <strong>"where to interact"</strong> ability to handle these tasks. Here are some examples.
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<h2 class="subtitle is-4" style="text-align: center;">Task 1: Background Distraction (Put banana into drawer)</h2>
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<video class="center" playsinline autoplay loop muted src="./resources/mpi/distraction/banana.mp4" width="100%" style="border-radius:10px;"></video>
<center>Original Setting (Real-time)</center>
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<video class="center" playsinline autoplay loop muted src="./resources/mpi/distraction/banana_r3m.mp4" width="100%" style="border-radius:10px;"></video>
<center>R3M (Real-time)</center>
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<video class="center" playsinline autoplay loop muted src="./resources/mpi/distraction/banana_ours_distracted.mp4" width="100%" style="border-radius:10px;"></video>
<center><strong>MPI(Ours)</strong> (Real-time)</center>
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<h2 class="subtitle is-4" style="text-align: center;">Task 2: Object Variation (Lift up the pot lid)</h2>
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<video class="center" playsinline autoplay loop muted src="./resources/mpi/distraction/pot.mp4" width="100%" style="border-radius:10px;"></video>
<center>Original Setting (Real-time)</center>
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<div class="col">
<video class="center" playsinline autoplay loop muted src="./resources/mpi/distraction/pot_r3m.mp4" width="100%" style="border-radius:10px;"></video>
<center>R3M (Real-time)</center>
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<video class="center" playsinline autoplay loop muted src="./resources/mpi/distraction/pot_distracted_ours.mp4" width="100%" style="border-radius:10px;"></video>
<center><strong>MPI(Ours)</strong> (Real-time)</center>
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<h3 class="subtitle is-3" style="text-align: center;">Generalization Experiment Results</h3>
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<h1 class="title is-2" style="text-align: center; padding-bottom: 10px;">More Generalization Evaluation in the Real World</h1>
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<h1 class="title is-2" style="text-align: center; padding-bottom: 10px;">Simulation Visuomotor Control Tasks</h1>
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Previous studies have established imitation learning for visuomotor control in simulation as the standard evaluation method. This enables direct comparisons with prior works and focuses on assessing the sample-efficient generalization of visual representations and their impact on learning policies from limited demonstrations. We conduct this evaluation to compare the capabilities of different representations in acquiring both the knowledge of “where-to-interact” and “how-to-interact” in complex simulation environments.
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<h2 class="titile">BibTeX</h2>
If you find the project helpful for your research, please consider citing our paper:
<pre><code>@inproceedings{zeng2024mpi,
title={Learning Manipulation by Predicting Interaction},
author={Jia, Zeng and Qingwen, Bu and Bangjun, Wang and Wenke, Xia and Li, Chen and Hao, Dong and Haoming, Song and Dong, Wang and Di, Hu and Ping, Luo and Heming, Cui and Bin, Zhao and Xuelong, Li and Yu, Qiao and Hongyang, Li},
booktitle= {Proceedings of Robotics: Science and Systems (RSS)},
year={2024}
}</code></pre>
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