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+# Decomposing NeRF for Editing via Feature Field Distillation
+
+Sosuke Kobayashi Preferred Networks, Inc. sosk@preferred.jp
+
+Eiichi Matsumoto Preferred Networks, Inc. matsumoto@preferred.jp
+
+Vincent Sitzmann Massachusetts Institute of Technology sitzmann@mit.edu
+
+pfnet-research.github.io/distilled-feature-fields/
+
+# Abstract
+
+Emerging neural radiance fields (NeRF) are a promising scene representation for computer graphics, enabling high-quality 3D reconstruction and novel view synthesis from image observations. However, editing a scene represented by a NeRF is challenging, as the underlying connectionist representations such as MLPs or voxel grids are not object-centric or compositional. In particular, it has been difficult to selectively edit specific regions or objects. In this work, we tackle the problem of semantic scene decomposition of NeRFs to enable query-based local editing of the represented 3D scenes. We propose to distill the knowledge of off-the-shelf, supervised and self-supervised 2D image feature extractors such as CLIP-LSeg or DINO into a 3D feature field optimized in parallel to the radiance field. Given a user-specified query of various modalities such as text, an image patch, or a point-and-click selection, 3D feature fields semantically decompose 3D space without the need for re-training and enable us to semantically select and edit regions in the radiance field. Our experiments validate that the distilled feature fields can transfer recent progress in 2D vision and language foundation models to 3D scene representations, enabling convincing 3D segmentation and selective editing of emerging neural graphics representations.
+
+# 1 Introduction
+
+Emerging neural implicit representations or neural fields have been shown to be a promising approach for representing a variety of signals [82, 53, 65, 106, 56]. In particular, they play an important role in 3D scene reconstruction and novel view synthesis from a limited number of context images. Neural radiance fields (NeRF) [56] enabled the recovery of a continuous volume density and radiance field from a limited number of observations, producing high-quality images from arbitrary views via volume rendering with promising applications in computer graphics. However, editing a scene reconstructed by NeRF is non-obvious because the scene is not object-centric and is implicitly encoded in the weights of a connectionist representation such as an MLP [56] or a voxelgrid [23]. Although we can transform the scene in input or output space or via optimization-based editing [37, 97], this does not enable selective object-centric or semantic, local edits, such as moving a single object. Prior work has addressed this challenge via coordinate-level, semantic decompositions which allow to selectively move, deform, paint, or optimize parts of a NeRF, but relies on costly annotation of instance segmentations and training of instance-specific networks [104]. While this can be alleviated with pre-trained segmentation models [25, 41], such models require pre-defined closed label sets and domains (e.g., traffic scenes), limiting decomposition and editing. Local editing of NeRFs ideally requires an efficient, open-set method for coordinate-level decomposition.
+
+In this work, we present distilled feature fields (DFFs), a novel approach to query-based scene decomposition for local, interactive editing of NeRFs. We focus on 3D neural feature fields, which map every 3D coordinate to a semantic feature descriptor of that coordinate. Conditioned on a user query such as a text or image patch, this 3D feature field can compute a decomposition of a scene without re-training. We train a scene-specific DFF via teacher-student distillation [34], using supervision from feature encoders pre-trained on the image domain. Unlike the domain of 3D scenes, the image domain boasts massive high-quality datasets and abundant prior work on self-supervised and supervised training of effective feature extraction models. Notably, recently proposed transformer-based models [96, 22] have demonstrated impressive capabilities across various vision- and text-based tasks (e.g., CLIP [69], LSeg [44], DINO [12]). Such feature spaces capture the semantic properties of regions and make it possible to correspond and segment them well by text, image queries, or clustering. We employ these models as teacher networks and distill them into 3D feature fields via volume rendering. The trained feature field enables us to semantically select and edit specific regions in 3D NeRF scenes and render multi-view consistent images from the locally edited scenes.
+
+In extensive experiments, we investigate the applications of neural feature fields with two different pre-trained teacher networks, (1) LSeg [44], a CLIP-inspired language-driven semantic segmentation network, and (2) DINO [12, 3], a self-supervised network aware of various object boundaries and correspondences. LSeg and DINO features allow us to select 3D regions by a simple text query or an image patch, respectively. We first quantitatively demonstrate that LSeg-based DFFs with label queries can have high 3D segmentation performance compared with an existing point-cloud based 3D segmentation baseline trained on ScanNet [20], a supervised point-cloud dataset. We then demonstrate a variety of 3D appearance and geometry edits across real-world NeRF scenes with no annotations of segmentation; and show that we may edit regions with a single query of text, image, pixel, or cluster choice.
+
+# 2 Related Work
+
+Neural Implicit Representations. Neural implicit representations or neural fields have recently advanced neural processing for 3D data and multi-view 2D images [82, 53, 65, 106, 56]. For a review of this emerging space we point the reader to the reports by Kato et al. [39], Tewari et al. [90], and Xie et al. [102]. In particular, a neural radiance field (NeRF) can be fitted to a set of posed 2D images and maps a 3D point coordinate and a view direction to RGB color and density. When observations are limited, NeRF often overfits and fails to synthesize novel views with correct geometry and appearance. Pre-trained vision models have been used for regularizing NeRF via flows [62], multi-view consistency [35], perceptual loss [110], or depth estimation [100, 77]. Some pre-trained models operate not only in the visual world but also in other modalities such as language. The recently proposed CLIP model [69] has demonstrated impressive performance in image-and-text alignment, with strong generalization to various textual and visual concepts. Wang et al. [97], Jain et al. [36], and Poole et al. [68] use CLIP or Imagen [79] to edit or generate a single-object NeRF with a text prompt query by optimizing the NeRF parameters to generate images matched with the text. While such methods are promising, they do not enable accurate selective editing of specific scene regions. For example, the prompt “yellow flowers” may affect unintended scene regions, such as the leaves of a plant. Our proposed decomposition method leverages pre-trained foundation models to enable selective editing of real-world NeRF scenes. Neural descriptor fields [80] use intermediate features that emerge in a 3D occupancy field network [53] for efficiently teaching robots object grasping. Instead of a pre-trained object-centric 3D model, we use 2D vision models as teacher networks via distillation, exploiting recent progress in pre-trained foundation models [7].
+
+Geometric Decomposition of Neural Scene Representations Kohli et al. [40] and Zhi et al. [112] show that neural implicit representations can be combined with the supervision of semantic labels. Yang et al. [104] demonstrate that given view-consistent ground-truth instance segmentation masks during training, NeRF can be trained to represent each object as different volumes, although such an annotation is expensive in practice. Concurrently, Benaim et al. [6] also experiment with the different parametarization. Conditional [49, 37, 21, 63] and generative models [60, 61, 31] enable a degree of category-specific decomposition (e.g., human bodyparts) and editing on constrained domains with large datasets. Regular structures such as voxelgrids or octrees [13, 48, 14, 88, 94, 89, 43, 81, 107, 59, 60] or unsupervised decomposition [73, 85, 109, 83] enable editability via manipulation of localized parameters. However, the decomposition is limited due to the inflexibly structured boundaries or strong assumptions about scenes; self-supervised object-centric learning is a difficult task. Other studies also explored reconstruction with more structured hybrid representations via pipelines specialized to a domain (e.g., traffic scene) [64, 25, 41] or situation (e.g., each object data is independently accessible) [28, 27, 105]. Note that this line of work defines and constrains domains or the types of segmentation during or before training and thus limits the degrees of freedom for editable scenes and objects. In contrast, our method can decompose scene-specific NeRFs into arbitrary semantic units via text and image queries, enabling versatile scene edits without retraining. A concurrent paper by Tschernezki et al. [93] also explores the same training framework and, in particular, investigates how fused features are improved from 2D teacher networks. It also complementarily shows the results with other teacher models (MoCo-v3 [17] and DeiT [91]), dimension reduction via PCA, and NeuralDiff [92]-based neural fields. Other concurrent studies explore decomposition through training scene-specific segmentation field [113] or 3DCNN [76] supervised by click or scribble annotations. Lastly, in a different but related task, video editing, Kasten et al. [38] use foreground-background decomposition and atlas representation for time-consistent, local editing; Loeschcke et al. [51] and Bar-Tal et al. [5] further use CLIP for editing.
+
+Zero-shot Semantic Segmentation. Zero-shot semantic segmentation is a challenging task [24, 2, 10] where a model has to predict semantic labels of pixels in images without a-priori information of the categories. A typical solution is to use vision-and-language cross-modal encoders. They are trained to encode images (pixels) and text labels into the same semantic space and perform zero-shot prediction based on the similarity or alignments of the two inputs. Recent development of image encoder architectures [96, 22, 71] and large-scale training [69, 12] have improved the ability and generalization of vision models, including zero-shot models [44, 52, 99, 103, 114, 72]. On the other hand, ongoing studies on zero-shot perception in 3D still suffer from the lack of effective, efficient, and high-resolution architectures and large-scale annotated datasets [54, 33, 29, 101, 78, 30]. Our method is a new approach to perform zero-shot semantic segmentation on scene-specific 3D fields by exploiting progress in the image domain without semantic 3D supervision. We note that the goal of this paper is not to achieve state-of-the-art performance on 3D semantic segmentation tasks. Instead, our goal is the decomposition of neural scene representations for editing, which requires smooth segmentation results on continuous 3D space rather than segmentation of discrete point clouds or voxelgrids.
+
+# 3 Preliminaries
+
+# 3.1 Neural Radiance Fields (NeRF)
+
+NeRF [56] uses MLPs to output density $\sigma$ and color c given a point coordinate $\mathbf { x } = ( x , y , z )$ in a 3D scene. This simple scene representation can be rendered and optimized via volume rendering. Given a pixel’s camera ray $\mathbf { r } ( t ) = \mathbf { o } + t \mathbf { d }$ , depth $t$ with bounds $[ t _ { \mathrm { n e a r } } , t _ { \mathrm { f a r } } ]$ , camera position $\mathbf { o }$ , and its view direction $\mathbf { d }$ , NeRF calculates the color of a ray using quadrature of $K$ sampled points $\{ \mathbf { x } _ { k } \} _ { k = 1 } ^ { K }$ with depths $\{ t _ { k } \} _ { k = 1 } ^ { K }$ as
+
+$$
+\hat { \mathbf { C } } ( \mathbf { r } ) = \sum _ { k = 1 } ^ { K } \hat { T } ( t _ { k } ) \alpha \left( \sigma ( \mathbf { x } _ { k } ) \delta _ { k } \right) \mathbf { c } ( \mathbf { x } _ { k } , \mathbf { d } ) , \quad \hat { T } ( t _ { k } ) = \exp \left( - \sum _ { k ^ { \prime } = 1 } ^ { k - 1 } \sigma ( \mathbf { x } _ { k ^ { \prime } } ) \delta _ { k ^ { \prime } } \right) ,
+$$
+
+where $\alpha \left( x \right) = 1 - \exp ( - x )$ , and $\delta _ { k } = t _ { k + 1 } - t _ { k }$ is the distance between adjacent point samples. NeRFs are optimized solely on a dataset of images and their camera poses by minimizing a rerendering loss.
+
+# 3.2 Pre-trained Models and Zero-shot Segmentation of Images
+
+Most semantic segmentation models pre-define a closed set of labels, and cannot flexibly change the segmentation categories or boundaries without supervised training. In contrast, zero-shot semantic segmentation predicts target regions given open-set queries. Li et al. [44] proposes LSeg, a model to feature field f and the pretrained text encoder f . Specifically, probability of a label l of a point x in    2    2Rperform zero-shot semantic segmentation by aligning pixel-level features and a text query feature. 182 the 3D space, p(l x), are predicted by dot product of the 3D feature f (x) and text label feature fq(l)        = Lp + Lf , Lp = C (r)  C(r)  2 , Lf = F(r)  fimg(I, r)  1 , (4)4LSeg employs an image feature encoder with the DPT architecture [71] and a CLIP-based text label 183 followed by softmax: 5stency. In addition, importantly for user-friendly interactive editing, w52R 2Rfeature encoder [69], trained via large-scale language-image contrastive learning. The probability of a text label $l$ 4given a pixel $r$ in an image $I$ , $\mathbf { p } ( l | I , r )$ T 4, is then calculated via dot product of pixel-level image feature ${ \bf f } _ { \mathrm { i m g } } ( I , r )$ p(l|x) = Pand queried text feature ${ \bf f } _ { \mathrm { q } } ( l )$ q  T . followed by a softmax:
+
+![](images/01c5d617234d256546d0bd2a253fa5222d00322fa46decdbdcc409fa1bb32e21.jpg)  
+as distillation from 2D teacher network to 3D student 4122      k  k+1  k            ˆ                 214 breaks 3D consistency. In addition, importantly f214 breaks 3D consistency. In addition, importantly for usemize f through SGD on minimizing the difference between rendered features F (r) andLp = X  Cˆ (r)  C(r)  , Lf = X  Fˆ(r)  fimg(I, r)  , (4)iginal NeRF [46] for the training objective and the volume rendering strategy. Inps://huggingface.co/sentence-transformers/clip-ViT-B-32-multilingual-v1Figure 1: Left: A Distilled Feature Field (DFF) maps a coordinate x and a viewing direction d to ll this mode 124    work via ther2Rs Fˆ (r) anddensity $\sigma$ eural Perceptual Fields (NePeRF).    els and ground-truth pixels of real images.             nconsistent supervision with noise could harm reconstruction quality of geometry, although the                  lume rendering trick. We call this model distilled feature field (DFF).r2Re teacher’s outputs f (I, r). For volume rendering, we use two 4184         , color c, and feature f . It is trained by minimizing the difference between rendered features sformers/clip-ViT-B-32-multilingual-v1178 effect seems negligible in preliminary experiments.            d at any 3D point without limiting resolution, so naturally used tog41 It is an interesting direction to introduce view de1 It is an interesting direction to introduce view dependehe original NeRF [46] for the training objective and the volume rendering strategy. Inume rendering with coarse-and-fine hierarchical sampling as well as the original185 and feature loss Lf , in total, L:and features as predicted by a pre-trained image feature encoder, as well as the rendered color and 3t is an interesting direction to introduce view dependency to the segmentation for discriminating view-dependent query like referring expressions (e.g., “the chdependent query like referring expressions (e.g., “the chair lef the photometric loss, we add a new objective for minimizing the difference betweenˆ 4          pLf , in total, L:     2ground-truth pixel color. Right: At test time, we may decompose and edit 3D space via selecting and 4 179 4.2 Query-based Decomposition and Editingdent query like referring expressions (e.g., “the chair left to the table”      2020, Liu et      img      or volume rendering with coarse-and-fine hierarchical sampling as well as the originalX  ˆ  2 X  ˆ L = Lp + Lf , Lp manipulating different 3D regions with a variety of queries.
+
+$$
+{ \bf p } ( l | I , r ) = \frac { \exp ( { \bf f } _ { \mathrm { i m g } } ( I , r ) { \bf f } _ { \mathrm { q } } ( l ) ^ { \mathrm { T } } ) } { \sum _ { l ^ { \prime } \in \mathcal { L } } \exp ( { \bf f } _ { \mathrm { i m g } } ( I , r ) { \bf f } _ { \mathrm { q } } ( l ^ { \prime } ) ^ { \mathrm { T } } ) } ,
+$$
+
+186 where $\mathcal { L }$ is a set of possible labels. If negative labels are not available, we may use other scores like and the query2 188 , so it and view synthesis with it are 3D consistent as well as the original NeRF. Unlikethresholded cosine similarity to directly compute the probability of a label. During training, LSeg 189 the proposed method, editing optimizes only the image encoder ${ \bf f } _ { \mathrm { i m g } } \bar { ( } I , r )$ nthesized images by image-based postprocessing breaksby minimizing cross-entropy on supervised semantic 190 3D consistency. In addition, we can chsegmentation datasets. The text encoder ${ \bf f } _ { \mathrm { q } } ( l )$ he segmentation by changing only the query withoutis obtained from a pre-trained CLIP model [69]. 191 retraining, which cannot be realized by existing methods using closed-set semantic segmentation [ZhiRecently, pre-trained CLIP has been leveraged as the backbone for a variety of tasks and has been 192 et al., 2021a] or instance segmentation annotation [Yang et al., 2021], but important for user-friendlyextended with additional modules sharing the same latent space. For example, Reimers and Gurevych [74, 75] trains a multi-lingual (more than $5 0 +$ languages) text encoder, which enables CLIP and 2 It is an interesting direction to introduce view dependency to the segmentation for discriminating view-CLIP-inspired variants to use non-English queries like Japanese. We similarly use the latent space of dependent query like referring expressions (e.g., “the chair left to the table”), but left for future work.a pre-trained CLIP for LSeg via distillation, enabling the decomposition of NeRFs with both English and non-English queries. Segmentation can further be performed with other modalities such as image, patch or pixel query features $\mathbf { f } _ { \mathrm { q } }$ 5using a similar dot-product similarity formulation as in Eq. 2. Notably, DINO [12], a self-supervised vision model, solves video instance segmentation and tracking by calculating similarity among features in adjacent frames. Amir et al. [3] also demonstrate that DINO features work well on co-segmentation and point correspondence by similarity and clustering. In our experiments, we use these two publicly available models, LSeg and DINO, to obtain features of images and texts for 3D decomposition.
+
+# 4 Distilled Feature Fields
+
+# 4.1 Distilling Foundation Modules into 3D Feature Fields via Volume Rendering
+
+NeRF learns a neural field to compute the density and view-dependent color, $\sigma ( \mathbf { x } )$ and $\mathbf { c } ( \mathbf { x } , \mathbf { d } )$ . We may extend NeRF by adding decoders for other quantities of interest. For example, SemanticNeRF [112] adds a branch outputting a probability distribution of closed-set semantic labels, trained with supervision via images with ground-truth semantic labels. This enables the prediction of pairs of RGB and semantic segmentation masks from novel views, useful for data augmentation. However, because ground-truth annotation is costly, the method is inefficient as a means of scene editing [104]. For specific domains like traffic scenes [25, 41], we may instead train a closed-set segmentation model and use its prediction for training object-aware neural fields. However, this approach is possible only if the types of objects are limited and the domain-specific supervised dataset is available; limiting the application of scene editing in terms of domain and flexibility of decomposition.
+
+We build on top of these ideas and perform 3D zero-shot segmentation of NeRFs using open-set text labels or other feature queries. Instead of a branch performing closed-set classification, we propose to add a feature branch outputting a feature vector itself. This branch models a 3D feature field describing the semantics of each spatial point. We supervise the feature field by a pretrained pixel-level image encoder $\mathbf { f } _ { \mathrm { i m g } }$ as a teacher network. Given a 3D coordinate $\mathbf { x }$ , the feature field outputs a feature vector $\mathbf { f } \left( \mathbf { x } \right)$ in addition to density $\sigma ( \mathbf { x } )$ and color $\mathbf { c } ( \mathbf { x } , \mathbf { d } )$ , as shown in Fig. 1. Volume rendering of the feature field is similarly performed via
+
+$$
+\hat { \mathbf { F } } ( \mathbf { r } ) = \sum _ { k = 1 } ^ { K } \hat { T } ( t _ { k } ) \alpha ( \sigma ( \mathbf { x } _ { k } ) \delta _ { k } ) \mathbf { f } ( \mathbf { x } _ { k } ) \ .
+$$
+
+We can optimize f by minimizing the difference between rendered features $\hat { \mathbf { F } } ( \mathbf { r } )$ and the teacher’s features ${ \bf f } _ { \mathrm { i m g } } ( I , r )$ . Effectively, we are distilling [34] the 2D teacher network into our 3D student network via differentiable rendering, and thus dub this model a distilled feature field (DFF). We add a feature objective $\mathcal { L } _ { f }$ penalizing the difference between rendered features $\hat { \mathbf { F } } ( \mathbf { r } )$ and the teacher’s outputs ${ \bf f } _ { \mathrm { i m g } } ( I , r )$ to the photometric loss of the original NeRF. We use two networks for volume rendering with coarse-and-fine hierarchical sampling. We thus minimize the sum of photometric loss $L _ { p }$ and feature loss $L _ { f }$ , in total, $L$ :
+
+$$
+L = L _ { p } + \lambda L _ { f } , L _ { p } = \sum _ { \mathbf { r } \in \mathbb { R } } \left\| \hat { \mathbf { C } } ( \mathbf { r } ) - \mathbf { C } ( r ) \right\| _ { 2 } ^ { 2 } , L _ { f } = \sum _ { \mathbf { r } \in \mathbb { R } } \left\| \hat { \mathbf { F } } ( \mathbf { r } ) - \mathbf { f } _ { \mathrm { i m g } } ( I , r ) \right\| _ { 1 } ,
+$$
+
+where $\mathcal { R }$ are sampled rays, $\mathbf { C } ( r )$ is the ground truth pixel color of ray $r$ , $\lambda$ is the weight of the feature loss and is set to 0.04 to balance the losses [112]. We apply stop-gradient to density in rendering of features $\hat { \mathbf { F } } ( \mathbf { r } )$ in Equation 3 as the teacher’s features ${ \bf f } _ { \mathrm { i m g } } ( I , r )$ are not fully multi-view consistent, which could harm the quality of reconstructed geometry.
+
+# 4.2 Query-based Decomposition and Editing
+
+A trained DFF model can perform 3D zero-shot segmentation by its feature field $\mathbf { f }$ and a query encoder $\mathbf { f } _ { \mathrm { q } }$ . Probability of a label $l$ of a point $\mathbf { x }$ in the 3D space, ${ \bf p } ( l | { \bf x } )$ , is calculated by dot product of the 3D feature $\mathbf { f } \left( \mathbf { x } \right)$ and text label feature ${ \bf f } _ { \mathrm { q } } ( l )$ followed by a softmax:
+
+$$
+\mathbf { p } ( l | \mathbf { x } ) = \frac { \exp ( \mathbf { f } ( \mathbf { x } ) \mathbf { f } _ { \mathrm { q } } ( l ) ^ { \mathrm { T } } ) } { \sum _ { l ^ { \prime } \in \mathcal { L } } \exp ( \mathbf { f } ( \mathbf { x } ) \mathbf { f } _ { \mathrm { q } } ( l ^ { \prime } ) ^ { \mathrm { T } } ) } \mathrm { ~ . ~ }
+$$
+
+This query-based segmentation field is at the core of the proposed method. It can be calculated at any 3D point without limiting resolution, naturally used in tandem with a radiance field and volume rendering. Note that the segmentation depends on only the 3D coordinate and the query1. As the original NeRF, it is thus multi-view consistent. In addition and important for interactive editing, we can change the segmentation via queries without re-training, which cannot be realized by closed-set methods using semantic [112] or instance segmentation annotation [104]. We may now use this query-conditional segmentation to identify a specific 3D region for editing. Various edits can be generalized to the merging of two NeRF scenes $\sigma _ { 1 } ( \mathbf { x } ) , \mathbf { c } _ { 1 } ( \mathbf { x } , \mathbf { d } )$ and $\sigma _ { 2 } ( \mathbf { x } ) , \mathbf { c } _ { 2 } ( \mathbf { x } , \mathbf { d } )$ , where we use the segmentation field $\mathbf { p }$ for blending. In the experiments section, we simply modify Eq. 1 as a blend of two scenes based on the ratio of $\alpha$ :
+
+$$
+\begin{array} { l } { { \displaystyle { \hat { \bf C } } ( { \bf r } ) = \sum _ { k = 1 } ^ { K } { \hat { T } } ( t _ { k } ) \left( \alpha ( \sigma _ { 1 } ( { \bf x } _ { k } ) \delta _ { k } ) { \bf c } _ { 1 } ( { \bf x } _ { k } , { \bf d } ) \rho _ { k } + \alpha ( \sigma _ { 2 } ( { \bf x } _ { k } ) \delta _ { k } ) { \bf c } _ { 2 } ( { \bf x } _ { k } , { \bf d } ) ( 1 - \rho _ { k } ) \right) } , } \\ { { \displaystyle ~ , ~ \rho _ { k } = \frac { \alpha \left( \sigma _ { 1 } ( { \bf x } _ { k } ) \delta _ { k } \right) } { \alpha \left( \sigma _ { 1 } ( { \bf x } _ { k } ) \delta _ { k } \right) + \alpha \left( \sigma _ { 2 } ( { \bf x } _ { k } ) \delta _ { k } \right) } , ~ { \hat { T } } ( t _ { k } ) = \prod _ { k ^ { \prime } = 1 } ^ { k - 1 } \alpha ( \sigma _ { 1 } ( { \bf x } _ { k ^ { \prime } } ) \delta _ { k ^ { \prime } } ) + \alpha ( \sigma _ { 2 } ( { \bf x } _ { k ^ { \prime } } ) \delta _ { k ^ { \prime } } ) } . } \end{array}
+$$
+
+For example, if we want to apply a geometric transformation $\mathbf { g }$ to a region of a query $l$ in a NeRF scene $( \sigma , \mathbf { c } )$ , we can render the transformed scene via Eqs. 6 and 7 by setting $\alpha ( \sigma _ { 1 } ( { \bf x } _ { k } ) \delta _ { k } ) =$ $( 1 - \mathbf { p } ( l | \mathbf { x } _ { k } ) ) \alpha ( \sigma ( \mathbf { x } _ { k } ) \delta _ { k } )$ , $\boldsymbol { \alpha } ( \sigma _ { 2 } ( \mathbf { x } _ { k } ) \boldsymbol { \delta } _ { k } ) = \mathbf { p } ( l | \mathbf { g } ^ { - 1 } ( \mathbf { x } _ { k } ) \big ) \boldsymbol { \alpha } ( \sigma ( \mathbf { g } ^ { - 1 } ( \mathbf { x } _ { k } \big ) ) \boldsymbol { \delta } _ { k } ) ,$ $\mathbf { c } _ { 1 } ( \mathbf { x } _ { k } , \mathbf { d } ) = ( 1 -$ $\mathbf { p } ( l | \mathbf { x } _ { k } ) ) \mathbf { c } ( \mathbf { x } _ { k } , \mathbf { d } )$ , and $\mathbf { c } _ { 2 } ( \mathbf { x } _ { k } , \mathbf { d } ) = \mathbf { p } ( l | \mathbf { g } ^ { - 1 } ( \mathbf { x } _ { k } ) ) \mathbf { c } ( \mathbf { g } ^ { - 1 } ( \mathbf { x } _ { k } ) , \mathbf { g } ^ { - 1 } ( \mathbf { d } ) )$ . More details of editing for colorization, translation, and deletion are shown in Appendix B. We can combine this with more complex edits, including optimization-based methods like CLIPNeRF [97]. While CLIPNeRF itself cannot selectively edit specific regions in multi-object scenes, our decomposition method enables it to update only desired objects without breaking unintended areas.
+
+![](images/cdc3251167f73e53e37f2b60398d4b7251996484f615fd018821909fb180547e.jpg)  
+Figure 2: Comparison of predictions by coarse and fine MLPs.
+
+Table 1: Performance of 3D semantic segmentation on Replica dataset. DFF outperforms a supervised point-cloud segmentation model MinkowskiNet42.   
+
+<table><tr><td></td><td>mIoU</td><td>accuracy</td></tr><tr><td>Supervised 3DCNN</td><td>0.475</td><td>0.758</td></tr><tr><td>DFF (Coarse)</td><td>0.589</td><td>0.855</td></tr><tr><td>DFF (Fine)</td><td>0.583</td><td>0.855</td></tr></table>
+
+# 5 Experiments
+
+We first conduct a quantitative evaluation of the decomposition achieved by DFF. We demonstrate that DFF enables 3D semantic segmentation in a benchmark dataset using scanned point clouds with human-annotated semantic segmentation labels. We then investigate the capabilities of DFF for editing and subsequent novel-view synthesis on real-world datasets. We use two teacher networks, LSeg [44] and DINO [12], which are pre-trained and publicly available. Each training image is encoded by the image encoders of the networks and used as target feature maps, ${ \bf f } _ { \mathrm { i m g } } ( I , r )$ , defined in Equation 4. Because the feature maps are of reduced sizes due to the limitation of the networks, we first resize them to the original image size. The implementation and settings of NeRF, unless otherwise stated, follow Zhi et al. [112]. During the training of 200K iterations, the loss $L$ in Equation 4 is minimized by Adam with a linearly decaying learning rate (5e-4 to 8e-5). During training, Gaussian noise for density is also applied. The number of coarse and fine samplings is 64 and 128, respectively. The MLP of the neural radiance field consists of eight ReLU layers with 256 dimensions, followed by a linear layer for density, three layers for color, and three layers for feature, as shown in Fig. 1. Positional encoding of length 10 is used for the input coordinate and its skip connection, and that of length 4 is for viewing direction. If an independent MLP is prepared for the feature field, it consists of four layers (with a skip connection at the third layer if the positional encoding is used). The size of a training image is $3 2 0 \times 2 4 0$ for the Replica dataset and $1 0 0 8 \times 7 5 6$ for the other datasets. The batchsize of training rays is 1024 for Replica and 2048 for the others. During finetuning of feature fields or radiance fields, Gaussian noise is removed, and the learning rate is set to 1e-4. See appendix A and C for further training details.
+
+# 5.1 3D Semantic Segmentation
+
+We construct a 3D semantic segmentation benchmark from four scenes in the Replica dataset [86] with data split and posed images provided by [112]. See appendix D for further details of the dataset. We train DFF to reconstruct each scene with radiance and feature fields from training images and evaluate the quality of novel view synthesis and 3D segmentation of the annotated point clouds. We use LSeg as a teacher network. The LSeg text encoder encodes each label, and the probability of each point is calculated by Equation $\cdot$ . Note that the training uses only the photometric and feature losses (Equation 4) and does not access any supervision via semantic labels.
+
+Semantic Segmentation Results. First, we show evaluation metrics of 3D semantic segmentation, mean intersection-over-union (mIoU) and accuracy in Table 1. For comparison, we also experiment with a sparse 3D convolution-based segmentation model, MinkowskiNet42 [18] taking a colored point cloud as input. It has a standard state-of-the-art architecture for point cloud segmentation and is trained on the ScanNet dataset [20], the largest annotated training dataset of 3D semantic segmentation3. Results demonstrate that DFF, taught by $\mathrm { L S e g }$ , achieves promising performance, even better than the supervised model. This indicates that DFF succeeds at distilling 3D semantic segmentation from the 2D teacher network.
+
+Table 2: Performance of novel view synthesis on Replica dataset. PSNR, SSIM, and LPIPS are metrics of image synthesis. $\delta { < } 1 . 2 5$ and absrel are metrics of geometry (depth estimation).   
+
+<table><tr><td></td><td>PSNR↑</td><td>SSIM↑</td><td>LPIPS↓</td><td>δ&lt;1.25↑</td><td>absrel</td></tr><tr><td>basic NeRF</td><td>32.87</td><td>0.934</td><td>0.148</td><td>0.993</td><td>0.018</td></tr><tr><td>DFF</td><td>32.85</td><td>0.932</td><td>0.150</td><td>0.993</td><td>0.017</td></tr><tr><td>DFF (overweighting 入)</td><td>32.68</td><td>0.927</td><td>0.162</td><td>0.993</td><td>0.018</td></tr></table>
+
+Impact of Sampling on Semantic Segmentation. NeRF employs two MLPs for hierarchical sampling, where the coarse MLP performs volume rendering with fewer points (64) using stratified sampling, and the fine MLP works with importance sampling (192 in total). So, we have two sampling options to train a feature field. Although fine sampling is critical for training accurate radiance fields, segmentation is of significantly lower spatial frequency than texture. We thus analyze the impact of coarse and fine training in Fig. 2. As expected, the coarse model produces smooth segmentations, while the fine version introduces high-frequency artifacts. This smoothness property is important for natural editable novel view synthesis and is discussed again later.
+
+Compatibility with View Synthesis. We also check and compare the quality of novel view synthesis with NeRF, which does not learn feature fields. Because the feature branch partially shares the layers with the radiance field (as shown in Fig. 1), learning feature fields could possibly harm the radiance field. Despite this concern, as shown in Tab. 2, the performance of view synthesis is not degraded. Thus, we can train and use the branch-based DFF with small computational and parameter overhead compared to the original NeRF. If we excessively increased the weight of the feature loss, $\lambda \times 1 0$ , it hurt view synthesis while not improving segmentation performance further. We further confirm that training independent, light-weight feature-field MLP, instead of a branch of the radiance-field MLP, achieves semantic segmentation results competitive with the branch-based approach (see appendix Tab. 3 for the result of all variants)4. This option is useful especially when we want to introduce DFF decomposition into arbitrary 3D scene representations, including off-the-shelf NeRF models, dynamic NeRFs [26, 66, 45], or meshes, without re-training of the radiance field.
+
+# 5.2 Editable Novel View Synthesis
+
+In the previous section, we quantitatively validated the ability of DFF to perform semantic decomposition. We now discuss the capability for editable view synthesis on real-world scenes, including the LLFF dataset [55] and our own dataset. Our method can be used even for LLFF scenes based on normalized device coordinates. Please see the supplemental web page for further results, including videos. In addition to $\mathrm { L S e g }$ using a text query, we also experiment with self-supervised DINO [12] as another teacher network to enable query-based decomposition using image patch queries. Here, we use thresholded cosine similarity to directly compute the probability of a query instead of softmax with negative queries in Eq. 5 and set $\mathbf { p } = 1$ if the similarity exceeds the threshold 5, and $\mathbf { p } = 0$ otherwise for hard decomposition. We first train NeRFs without a feature branch for each scene for 200K iterations $( L _ { p } )$ and then finetune them with a feature branch via distillation for 5K iterations $( L _ { p } + \lambda L _ { f } )$ since we found that the feature loss converged significantly faster than the photometric loss and short training was thus sufficient. We use coarse sampling for training feature branches and use it for edited rendering with fine sampling. See appendix A for the details.
+
+![](images/3e4bdbcd0ee006e447412849578ec81ec584654fa6d202f2ca0f5bf4cdd9fee3.jpg)  
+Figure 3: Appearance edits of specific objects via different query modalities: an image patch or text.
+
+![](images/7404df9e48bdaa6e1a8268d1d979b98fc6f3287ce1ea0da6bc98e1a11c73e6a2.jpg)  
+Figure 4: Extraction and deletion of specific objects via different query modalities, an image patch or text. The edited views are 3D consistent, unlike an image inpainting baseline [87]
+
+Appearance Editing, Deletion, Extraction. We show qualitative evaluations of novel view synthesis in Fig. 3 and Fig. 4. Specific 3D regions in these scenes are identified and locally edited via decomposition depending on various query modalities. In these experiments, we use a text query for LSeg-DFF as in Section 5.1 and use an image patch query for DINO-DFF. Because DINO features capture the similarity and correspondences of regions well thanks to self-supervised learning [12, 3], image patch queries help select all semantically similar areas at once. The patch feature is then calculated by averaging the features of all pixels in the patch.
+
+In Fig. 3, we demonstrate that the DFF enables convincing selective appearance edits. Because our focus is region selection via decomposition, we use simple color transformation for clarity here (e.g., flip RGB to BGR, blend colors). One might think that the MLP of a radiance field by the original NeRF also has hidden layers, and their features could possibly be used for decomposition. We confirm that the naive usage of NeRF features is not robust to decomposition, as shown in Fig. 5, especially in a complex multi-object scene. We use the 8th hidden layer of the fine radiance field network (i.e., the layer just before branching in Fig. 1) 6. NeRF features cannot clearly decompose even objects with simple shapes and colors. The region selections are leaked to other parts with similar colors, geometry, or positions while they do not entirely cover the targets. For example, floor selection is leaked to walls, a table, bins, or ceilings. Chair selection is leaked to irrelevant black parts like television, cables, lighting equipment, or shadows. This indicates that the feature space of the original NeRF does not learn semantic similarity well and is entangled with unpredictable and more low-level factors like color or spatial adjacency.
+
+In Fig. 4, we demonstrate that the DFF also works well on deletion or extraction of objects, using two patch queries (query- $\textcircled{1}$ for leaves and ground, query- $\textcircled{2}$ for flowers) and a text query- $\textcircled{3}$ “flower”. For comparison with a baseline editing method, we show the results by a state-of-the-art image inpainting model, LaMa [87]. Because the model requires masks for inpainting regions, we manually annotate the views for evaluation. As shown in the figure, the image inpainting model cannot generate clear and realistic images, and the different views are inconsistent. On the other hand, DFF produces multi-view consistent plausible results, especially succeeding at extracting foreground objects. Although the performance on deleting foreground objects is high, a remaining shortcoming is the existence of floating artifacts and blurred volumes in the far distance behind the deleted object.
+
+Priors for Smooth Decomposition We can organize the challenges of editable NeRFs into several categories: surface decomposition, volume decomposition, lighting decomposition, and estimation of less or never observed parts. If we edit appearances only, it practically requires decomposing regions only near the surface of objects, i.e., surface decomposition, because the color of a ray is determined mostly in a condensed interval around the surface. On the other hand, geometric transformations often require a higher level of decomposition. As shown in the deletion examples, geometric transformation may move or remove some surfaces and expose the space behind them. This forces models to render unknown regions less or never observed due to occlusions, including even the inside of objects. Thus, it is desirable to decompose volumes smoothly while synthesizing their inside and back7. Although these include the same challenges as novel view synthesis tackles, editability further highlights their importance.
+
+![](images/c939fb96e6e4902aed9d68fa8dc31ba5a222c811e23d1799a0f3fc81582e3213.jpg)  
+Figure 5: Appearance edits of specific objects, compared with decomposition using features of a NeRF hidden layer. For reference, we also show PCA-based visualizations of the features.
+
+![](images/61fc82c51e2244604a1b6d407b0140d0323b63542d00369b70a05f15bc3c75a5.jpg)  
+Figure 6: Comparison of predictions by a branch-based feature field MLP and independent MLP with no positional encoding, each of which is trained with coarse and fine sampling.
+
+Apart from lighting decomposition discussed in prior work [8, 9, 111], we further investigate the new challenge of smooth volume decomposition by experimenting with different DFF setups. As discussed in Section 5.1, DFF has two sampling options to train feature fields. The coarse training may introduce smoothness regularization and help cohesive decomposition and smoother in-painting of unobserved regions. Another reasonable smoothness regularizer is to eliminate the high-frequency positional encoding (PE). We thus train an independent MLP network for a feature field without PE. We compare four combinations of renderings in Fig. 6. To better understand their behavior, we use the DINO-DFF, show k-means clusters of the rendered feature map, and delete the head of the Triceratops by a query choosing its corresponding clusters. As expected, coarsely trained models and no-PE models succeed in smoother volume decomposition, and this combination can minimize high-frequency floating artifacts. A side effect is the lack of high-frequency representation power, which sometimes deletes disparate background regions and misses to represent features of complex structures (e.g., see the cluster visualization of the thin frames of the window). Towards the best of both worlds, developing proper priors or inductive biases is an important direction for future work [70]. Otherwise, surface-aware representations like IDR [106, 98] could avoid problems with floating artifacts. Note that not all geometric edits suffer from these problems. For example, it is often less problematic to move objects closer to the camera, enlarge them, or warp them to other scenes, as shown in Fig. 7.
+
+![](images/94ef6feac4bdb3c7abc043ba1a322b841939e4bb1869650893cad77a6305695f.jpg)  
+Figure 7: Editing with warping, deformation, shift, and rotation.
+
+Localizing Optimization-based Editing. Finally, we show a combination with an optimizationbased editing method. CLIPNeRF [97] optimizes the parameters of a radiance field so that its rendered images match with a text prompt via CLIP. While it is mainly designed for a single-object scene of specific categories, it is possible to apply to other real-world NeRFs. However, because it cannot control the scope of editing, a prompt like “white flower” may change the color of unintentional targets like leaves. Our DFF-based decomposition can upgrade such an optimization-based method to render a scene via the composition of a CLIP-optimized NeRF scene and the original NeRF scene. We show the results in Fig. 88. Although the naive CLIPNeRF edits unintentional parts, our method helps it to locally edit intentional parts only. In addition to switching rendering, we can also use the decomposition for controlling training signals during backpropagation. The additional experiment is shown in Appendix F. These extensions broaden the application of CLIPNeRF or other optimization-based editing methods to complex scenes.
+
+![](images/deb2446a46f560389b39605466e9be865e729e6a2b7dd1065730e768995a8c3c.jpg)  
+Figure 8: Comparison of appearance editing by CLIPNeRF and our extension.
+
+# 6 Discussion, Limitations, and Conclusions
+
+In this work, we propose distilled feature field (DFF), a novel method of NeRF scene decomposition for selective editing. We present quantitative evaluations of segmentation and extensive qualitative evaluations of editable novel view synthesis. In addition to these promising results, DFF-based models will benefit from future improvements to self-supervised 2D foundation models. We also clarify future directions on editable view synthesis through our experiments, especially for smoothness priors and estimation of unobserved regions. Furthermore, while this work focuses on editable view synthesis, it is also intriguing to transfer DFF to other applications, including 3D registration of text queries [16, 1, 47, 4] or robot teaching [32, 80].
+
+The limitations of the DFF framework are two-fold. The first one is the upper bduround of the performance due to distillation. The student model of distillation cannot largely outperform the teacher model9. If the resolution of teacher encoders is low, the corresponding DFFs also becomes coarse-grained. If the LSeg cannot understand a text query, the LSeg-DFF also cannot. Secondly, the DFF uses volume rendering depending on the 3D reconstruction by NeRF. A NeRF model is sometimes optimized to geometrically wrong solutions (e.g., floaters). Such geometry errors of the radiance fields would make supervision to DFFs noisy.
+
+As a possible negative societal impact, one might use our method for making realistic but fake content by editing NeRFs as desired. Automatic fake detection methods may help in preventing such misuse. NeRFs are further computation-intense, leading to high electricity usage. Recent work on efficient NeRFs [23, 57, 15] may alleviate this concern.
+
+# Acknowledgements
+
+We thank Tsukasa Takagi, Toshiki Nakanishi, Hiroharu Kato, and Masaaki Fukuda for their helpful feedback.
+
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+
+# Checklist
+
+1. For all authors...
+
+(a) Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope? [Yes] The claims are empirically demonstrated in Section 5, and described in the whole paper.   
+(b) Did you describe the limitations of your work? [Yes] See mainly Section 5, and 6.   
+(c) Did you discuss any potential negative societal impacts of your work? [Yes] See Section 6.   
+(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]
+
+2. If you are including theoretical results...
+
+(a) Did you state the full set of assumptions of all theoretical results? [N/A] Mathematical formulations of the models are written in Section 3 and 4.   
+(b) Did you include complete proofs of all theoretical results? [N/A] Mathematical formulations of the models are written in Section 3 and 4.
+
+3. If you ran experiments...
+
+(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] The complete code for the reproduction of all the experimental results is not publicly available. Because the code is a modification from a public code by Zhi et al. [112], reproduction is also easier than from scratch. We will make our scene dataset publicly available.   
+(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] Some of them are further described in the supplementary material.   
+(c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? [No] Error bars are not reported because it would be computationally expensive and results are expected to be stable. Note that most existing studies on NeRF have not reported the bars too.   
+(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] It is difficult to completely track and sum the total amount of computing in the experiments. Instead, we reported the setup of the main experiments.
+
+4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
+
+(a) If your work uses existing assets, did you cite the creators? [Yes] Codebase and datasets are appropriately cited, mainly in Section 5.   
+(b) Did you mention the license of the assets? [No] We refer the readers to the original source instead of mentioning them in this paper.   
+(c) Did you include any new assets either in the supplemental material or as a URL? [Yes] We include generated video by models in the supplemental material.   
+(d) Did you discuss whether and how consent was obtained from people whose data you’re using/curating? [N/A] No data about people.   
+(e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content? [N/A] No such data.
+
+5. If you used crowdsourcing or conducted research with human subjects...
+
+(a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A] No such data or experiment.   
+(b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A] No such data or experiment.   
+(c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? [N/A] No such data or experiment.

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+# EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought
+
+Yao ${ { \bf { M } } { \bf { u } } ^ { 1 } }$ , Qinglong Zhang2, Mengkang $\mathbf { H } \mathbf { u } ^ { 1 }$ , Wenhai $\mathbf { W a n g ^ { 3 } }$ , Mingyu Ding∗,1, $\mathbf { J u n \mathbf { \mathbf { J i n } } ^ { 4 } }$ , Bin Wang4, Jifeng Dai2,5, Yu Qiao2, Ping Luo∗,1,2 1The University of Hong Kong, 2Shanghai AI Laboratory, 3The Chinese University of Hong Kong, 4Noah’s Ark Laboratory 5Tsinghua University
+
+# Abstract
+
+Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understanding and execution capabilities. To achieve this, we have made the following efforts: (i) We craft a large-scale embodied planning dataset, termed EgoCOT. The dataset consists of carefully selected videos from the Ego4D dataset, along with corresponding high-quality language instructions. Specifically, we generate a sequence of sub-goals with the "Chain of Thoughts" mode for effective embodied planning. (ii) We introduce an efficient training approach to EmbodiedGPT for high-quality plan generation, by adapting a 7B large language model (LLM) to the EgoCOT dataset via prefix tuning. (iii) We introduce a paradigm for extracting task-related features from LLM-generated planning queries to form a closed loop between high-level planning and low-level control. Extensive experiments show the effectiveness of EmbodiedGPT on embodied tasks, including embodied planning, embodied control, visual captioning, and visual question answering. Notably, EmbodiedGPT significantly enhances the success rate of the embodied control task by extracting more effective features. It has achieved a remarkable 1.6 times increase in success rate on the Franka Kitchen benchmark and a 1.3 times increase on the Meta-World benchmark, compared to the BLIP-2 baseline fine-tuned with the Ego4D dataset. More demos, code, and dataset information can be found at our homepage.
+
+# 1 Introduction
+
+Embodied AI tasks, e.g., embodied planning, embodied VQA, and embodied control, aim to imbue robots with the ability to perceive, reason, and act within their environment, enabling them to perform long-horizon plans and execute actions autonomously based on real-time observations. Recently, large language models (LLMs) such as GPT4 [1] and PaLM-E [2], have shown promising language understanding, reasoning, and "chain-of-thought" capabilities. Such advances may open new possibilities for developing robots capable of processing natural language instructions, performing multi-modal "chain-of-thought", and planning actions in physical environments.
+
+Large-scale datasets play important roles in training large language models. For example, OpenCLIP trains its ViT-G/14 model on the LAION-2B dataset [3], which contains 2B image-language pairs. Unlike general-purpose visual language tasks that can get a huge amount of weakly labeled imagecaption pairs from the Internet, embodied AI tasks require egocentric data in robotics domains. Also,
+
+Human: Describe the video in detail.
+
+![](images/ca3f7fcc311c8a80c4594e6f910429514912da588e1d632c4ab30ce842514c3f.jpg)
+
+Human: Can you write a detailed plan for the task the robot is performing?
+
+![](images/dae261c153cc867c75ffc25196665abd373dcf2f5bb9265bb822c5af97974793.jpg)  
+Figure 1: EmbodiedGPT’s capabilities for video captioning, multi-turn question answering, embodied planning, and low-level control. The plans given by EmbodiedGPT are highly executable and incorporate task-specific features, leading to a significant improvement in the success rate of embodied control tasks, outperforming both R3M [12] (a video-language contrastive learned model) and BLIP2 [13] (a multi-modal foundation model) on Franka Kitchen [14] and Meta-World [15] environments.
+
+structured language instructions are needed for precise planning, which usually requires huge manual efforts and costs. This poses a challenging problem in collecting high-quality embodied multi-modal data. Some researchers [4, 5, 6, 7] explore creating large-scale embodied datasets with simulators, but a significant gap remains between simulation and the real world. Recent works [8, 9, 10] also explore adapting the pre-trained LLMs to a new domain by efficient tuning strategies like LoRA [11]. However, several open questions still remain: how to apply LLMs to the field of robotics which may face large domain gaps; how to leverage the "chain-of-thought" capability for structured planning; and how to use the output language plan for downstream manipulation tasks in an end-to-end manner.
+
+To solve the above challenges, in this work, we first build a large-scale embodied planning dataset, termed EgoCOT, which features chain-of-thought planning instructions. It contains carefully selected egocentric videos from the Ego4D dataset [16] and corresponding high-quality step-by-step language instructions, which are machine-generated, then semantics-based filtered, and finally human-verified. Additionally, we also create the EgoVQA dataset as an extension of the Ego4D dataset, focusing on egocentric human-object interaction video question answering tasks, which aims to offer a wider range of egocentric multi-modal data.
+
+Based on our EgoCOT and EgoVQA, we present an end-to-end multi-modal embodied foundation model called EmbodiedGPT, which can interact with the physical world in a more natural and intuitive manner, and perform many embodied tasks, as shown in Figure 1, such as embodied planning, embodied VQA, and embodied control. EmbodiedGPT comprises four integrated modules that work together, including i) a frozen vision model for encoding visual features of current observations, ii) a frozen language model used to execute natural language for question answering, captioning, and embodied planning tasks, iii) an embodied-former with a language mapping layer for aligning the visual and embodied instructions and extracting task-relevant instance-level features with the generated planning for low-level control, and iv) a policy network, which is responsible for producing low-level actions based on the task-relevant features, enabling the agent to effectively interact with the environment. To further enhance EmbodiedGPT ’s performance in generating reliable planning containing sub-goal sequences, we implement prefix tuning on the frozen language model to encourage the generation of more executable planning.
+
+Our method possesses the following core advantages: i) the generated planning exhibits strong executability and granularity at the object part level, such as the gripper of a robotic arm or the handle of a door, manifested in sub-goal sequences. ii) the proposed EgoCOT dataset is built based on an open-source large-scale dataset, which offers greater scalability compared to the PaLM-E [2] model trained on proprietary robot data. And both the EgoCOT dataset, and the EmbodiedGPT model will be open-sourced. iii) EmbodiedGPT forms a closed-loop from high-level planning to low-level control, which enables seamless integration of high-level planning and low-level control, providing efficient task performance and adaptability to a wide range of tasks. To achieve this, we utilize the embodied-former to query task-relevant instance-level features through cross-attention between visual observations and generated embodied planning. This enables the policy network to complete low-level control tasks with fewer than 25 demonstrations.
+
+The contributions can be summarized as follows: (i) We build an end-to-end multi-modal foundation model EmbodiedGPT for embodied AI, which is featured with "chain-of-thought" capability, empowering embodied agents to interact with the physical world in a more natural and intuitive manner. (ii) We develop two datasets, EgoCOT and EgoVQA, consisting of 200M annotated videos from the Ego4D dataset with corresponding detailed planning instructions and VQA data. The datasets are first machine-generated, then semantics-based filtered, and finally human-verified for quality control. (iii) We introduce EmbodiedGPT a cost-effective training approach and a paradigm for extracting task-relevant features from LLM-generated planning queries, thereby forming a closed loop between high-level planning and low-level control. We demonstrate our approach’s effectiveness by achieving state-of-the-art or comparable performance on multiple embodied tasks, including embodied control, embodied planning, video captioning, and video QA. Notably, in comparison to BLIP-2 [17] fine-tuned on the Ego4D dataset and R3M [12] specifically designed for manipulation tasks, EmbodiedGPT outperforms both models on the Franka Kitchen [14] benchmark with a margin of $2 2 . 1 \%$ and $5 . 5 \%$ , respectively. Similarly, on the Meta-World [14] benchmark, EmbodiedGPT surpasses both models with margins of $2 2 . 5 \%$ and $4 . 2 \%$ , respectively.
+
+# 2 Related Work
+
+# 2.1 Vision Language Pre-training with large scale foundation model
+
+Vision-Language Pre-training focuses on strengthening the link between visual observation and natural language. The goal is to develop models that can better understand and process visual content, such as recognizing objects and actions, and generating descriptive text. As models become larger, the computational expense for end-to-end pre-training rises, leading to the need for modular vision-language pre-training methods. These methods smartly use pre-trained models, keeping them ‘frozen’ during vision language pre-training to save on computational costs. For example, models like Uniter [18], Oscar [19], VinVL [20], and LiT [21] freeze the image encoder, while Frozen [22] and VGPT [23] freeze the language model. Furthermore, Flamingo [24] and BLIP-2 [17] use both frozen image encoders and language models, providing a balance between performance and computational efficiency. Due to the lack of open-source data for multi-modal embodied planning, previous works struggled to perform detailed task decomposition and lacked the ability to generate precise and executable plans. To tackle this issue, we create the EgoCOT dataset and develop an embodied chain-of-thought vision language pre-training framework to enhance the capacity of multi-modal models for embodied reasoning and planning.
+
+# 2.2 Egocentric Video Datasets.
+
+Egocentric videos, which are captured using wearable cameras, provide a natural perspective of daily activities and pose several challenging research questions [25, 26, 27]. Several egocentric video datasets have been created over the years, including [28, 29, 30]. However, the collection of egocentric videos is expensive, and previous datasets tend to be small-scale and domain-specific. Recently, a massive egocentric video dataset, Ego4D [16], has been released and has been used for embodied representation learning. The dataset comprises 3,670 hours of videos collected by 931 people from 74 locations across 9 countries, with videos accompanied by narrations. For embodied AI tasks, learning from large and diverse egocentric human videos has emerged as a promising approach to acquiring a generally useful visual representation for controlling such tasks. For example, R3M [12] developed a sparse and compact visual representation using the Ego4D human video dataset through a combination of time-contrastive learning and video-language alignment. VIP [31], learns general-purpose reward functions for goal-conditioned robotic manipulation using the Ego4D dataset.
+
+![](images/d3004a6413492a3e7d506cbfb1f7e8c59fedcfea501214e5e543ca4f7073d656.jpg)  
+Figure 2: Overall framework of EmbodiedGPT. The black arrow shows the vision-language planning process, while the red arrow represents that we leverage the queried language plans for better policy learning in low-level control tasks.
+
+# 2.3 Large Foundation Model Assistant System
+
+Recent advancements in large-scale multi-modal language models (LLMs), such as GPT-3 [32] and GPT-4 [1], have resulted in the creation of various models that can understand multiple modes of information. Two main approaches are used in this field: systematic collaboration and end-to-end trained models. Systematic collaboration approaches involve coordinating multiple vision models or tools with language models to combine visual information with textual descriptions. Examples include models like Visual ChatGPT [33], MM-REACT [34], and HuggingGPT [35]. However, this approach is limited by the accuracy and capacity of fixed modular models, which can lead to an accumulation of errors. On the other hand, end-to-end models aim to provide unified models for multi-modal tasks. For example, Flamingo [24] combines vision and language by freezing pre-trained vision encoders and language models. BLIP-2 [13] introduces Q-Former to align visual features from frozen visual encoders with large language models. Recently, models such as MiniGPT-4 [36] and LLaVA [37] align instruction-tuned language models with visual features from frozen visual backbones. VideoChat[38], mPLUG-Owl [39] and X-LLM [40], further expand support for video input. PaLM-E [41] is the first large embodied multi-modal model, which directly incorporates features from sensor modalities to improve real-world performance and is trained with their largescale everyday robot data [42]. Compared to PaLM-E, EmbodiedGPT is more compact, with a size of only 10B and offers additional support for video captioning, video QA and making planning according to a demonstration video. Furthermore, we form a closed-loop system that spans from high-level planning to low-level control.
+
+# 3 Method
+
+The goal of the embodied foundation model is to imitate human-like perception and interaction with the environment by accurately perceiving the environment, identifying relevant objects, analyzing their spatial relationships, and formulating a detailed task plan. To achieve this, the EmbodiedGPT employs a pre-trained vision transformer as the visual encoder and a pre-trained LLaMA [43] model as the language model. As shown in Figure 2, the embodied-former acts as a bridge between the visual and language domains, it first extracts compact visual features from the output of the vision model through attention-based interaction involving visual tokens, text queries, and learnable embodied queries and then maps it to the language modality through a language mapping layer. These embeddings are sent to the frozen LLaMA [43] language model for visual caption, visual QA, and embodied planning. The generated planning is then used to query highly relevant features from the general visual tokens encoded by the visual model via the embodied-former. These features are utilized to generate low-level control commands for task execution through the downstream policy network. To enhance performance across a range of embodied tasks, we introduce a novel video-language pre-training paradigm that leverages a cognitive chain of thought to produce embodied planning from egocentric video inputs. We formulate this task as a standard VQA (Visual Question Answering) task, using "how to do the task that $^ +$ original caption" as the question and embodied planning as the answer. This framework enriches the data of embodied planning and standard visual QA tasks, encouraging the embodied-former to capture task-specific features that are more suitable for embodied control tasks.
+
+# 3.1 Framework
+
+The training process consists of three stages, each designed to incrementally develop reasoning and planning capabilities. The first two stages focus on pre-training in basic cognitive and responsive skills, while the third stage involves training the embodied AI task with egocentric video-text data on EgoCOT. In the first stage, we focus on image-text conversation alignment pre-training, which involves using three datasets: COCO Caption [44], 595 thousand finely filtered image-text pairs from CC3M [45], and 491 thousand filtered image-text pairs obtained by re-captioning LAION-400M using BLIP-2 [17]. The primary goal of this stage is to pre-train the Embodied-former and language projection while keeping the vision and language model parameters frozen to save computational resources. In the second stage, our goal is to enhance the model’s ability to comprehend and generate more complex sentences and improve its reasoning skills. We achieve this by updating the language projection and prefix language adapter and utilizing the "Complex_Reasoning_ $. 7 7 \mathrm { k } "$ and multi-turn conversation datasets provided by "LLaVA_Instruct_150K" [46].
+
+Embodied "chain-of-thought" training with EgoCOT: During the third stage, we employ Conv3D [47] to adapt the pre-trained vision model from stage 2 for video encoding, using a total of eight evenly distributed keyframes from each video. Each keyframe is partitioned into threedimensional (3D) patches, which can be visualized as spatio-temporal cubes, adeptly capturing both the visual content and the sequence of events within the video. These 3D patches are subsequently encoded into visual tokens via the Conv3D module with a time offset of 2 and are then integrated into the internal vision transformer. Then, we introduce the ’chain-of-thought’ vision language pre-training paradigm where the model takes 8 keyframes of the video as input, along with the task description, embodied planning, and structured verb-noun pairs summary to reason with a prompt, such as Listing 1. To avoid overfitting, we provide a prompt set that has different instructions with the same meaning. In this stage, we fine-tune the patch embedding, the language projection layer, and the prefix language adapter to better capture temporal information.
+
+Watch this video , identify the actions and devise a plan using chain -of - thought . Extract detailed actions using this schema :   
+Task : {" task description " }   
+Plan : {" plan with chain -of - thought "} Actions : {{ " number " }: { ’ verb ’ }({ ’ noun ’}) }.
+
+Listing 1: Prompt we used for chain-of-thought pre-training.
+
+# 3.2 Model Architecture
+
+The Embodied-former, denoted as $\mathcal { E } ( \cdot )$ , serves as a bridge between visual input $x _ { \mathrm { v i s } }$ and the frozen language model, acting as an information bottleneck that delivers the most relevant visual data to the language model. The Embodied-former consists of two sub-modules: one for extracting features from the image input, denoted as $\mathcal { E } _ { \mathrm { v i s } } : x _ { \mathrm { v i s } } \to y _ { \mathrm { v i s } }$ , and another for extracting features from the text input, denoted as $\mathcal { E } _ { \mathrm { t x t } } : x _ { \mathrm { t x t } } \to y _ { \mathrm { t x t } } .$ . We employ $N$ learnable embodied query embeddings $y _ { \mathrm { { q u e r y } } }$ as the input of $\mathcal { E }$ to interact with $x _ { \mathrm { v i s } }$ through cross-attention layers and with $x _ { \mathrm { t x t } }$ through self-attention layers. We denote the output query representation as $z \in \mathbb { R } ^ { N \times D }$ , where $D$ is the dimensionality of the embeddings. The dimension of $z$ is significantly smaller than that of the visual features. The output query embeddings are then transformed to $z ^ { ' } \in \mathbb { R } ^ { N \times D ^ { ' } }$ , which have the same dimensionality $D ^ { ' }$ as the LLM’s text embedding in the language modality. This transformation is performed by a mapping function denoted as $M : z \to z ^ { ' }$ , which is accomplished by a linear projection via a fully-connected (FC) layer. The projected embeddings, $z ^ { \prime }$ , serve as "soft visual prompts for the language model," decoupling the whole interaction into visual-query interaction and query-text interaction. The final embodied planning is inferred by the language model with $z ^ { \prime }$ and text prompt(shown as Listing 1) as input. For low-level control which aims to generate actions to interact with the environment, the embodied plan $x _ { \mathrm { p l a n } }$ is used as input text for embodied-former to query the task-relevant instance level features ${ z _ { \mathrm { i n s t a n c e } } = \mathcal { E } ( x _ { \mathrm { v i s } } , x _ { \mathrm { p l a n } } , y _ { \mathrm { q u e r y } } ) }$ . Subsequently, the agent is capable of generating control commands, such as the turning angle of the servo, represented as $a = g ( z _ { \mathrm { i n s t a n c e } } , z _ { \mathrm { g l o b a l } } )$ . This function combines both the instance-specific information zinstance and the global context $z _ { \mathrm { g l o b a l } }$ . The global context is inferred using a ResNet50 model [48] that has been pre-trained on ImageNet [49], employing global average pooling. Here, $g ( \cdot )$ represents the policy network, which is a Multi-Layer Perceptron (MLP) [50] mapping function. The output of the policy network consists of specific executable actions, such as positions and velocities in the Cartesian coordinate system. More implementation details can be found in Appendix A.
+
+# 3.3 Training Settings
+
+We employ the same pre-trained image encoder as BLIP-2[17]. Specifically, we utilize the ViT-G/14 model from EVA-CLIP [51] and remove its last layer, using the output features of the second last layer instead. For the frozen language model, we adopt a pre-trained LLaMA-7B [43] model and fine-tune it using the ShareGPT dataset and a GPT-4 generated 52K English instruction-following dataset [52]. We then utilize the well-fine-tuned language model as the frozen language model for vision-language pre-training. Additionally, we convert the data type of parameters of the frozen ViT [53] and language model to FP16 during pre-training to increase efficiency.
+
+# 3.4 Creating EgoCOT and EgoVQA Dataset
+
+For our EgoCOT dataset, we obtain basic data from the Ego4D dataset [16], which includes 9, 645 untrimmed videos of various durations ranging from 5 seconds to 7 hours. To prepare the data for our purposes, we conducted two stages of data cleaning to prepare our data. In the first stage, we filtered out videos with missing or very short narrations (which made up $7 . 4 \%$ and $0 . 9 \%$ of the text, respectively), as well as those with unsure tags (which accounted for $4 . 0 \%$ of the text). We also excluded videos without human-object interaction, such as watching TV or walking. After this stage, we were left with 2.9 thousand hours of video, containing 3.85 million narrations, from 129 different scenarios covering 2927 hours of video.
+
+To generate pairs of captions, embodied plannings, and corresponding video segments with time intervals, we utilized the EgoVLP framework [54] to segment the video. The narrations are organized as a sequence of sentences $\mathcal { T } _ { 0 } , \cdots , \mathcal { T } _ { n }$ with precise timestamps $t _ { 0 } , \cdots , t _ { n }$ that indicate when a described event occurred. For each narration $\mathcal { T } _ { i }$ with timestamp $t _ { i }$ , we paired it with a clip $\nu _ { i }$ by determining its start and end time points:
+
+$$
+[ t _ { i } ^ { s t a r t } , t _ { i } ^ { e n d } ] = [ t _ { i } - \beta _ { i } / 2 \alpha , t _ { i } + \beta _ { i } / 2 \alpha ] ,
+$$
+
+where $\textstyle \beta _ { i } = \sum _ { j = 0 } ^ { n - 1 } \left( t _ { j + 1 } - t _ { j } \right) / n$ is an adjustable parameter equal to the average temporal distance between consecutive narrations in a given video. Conversely, $\alpha$ is a scale factor computed as the average of all $\beta _ { i }$ across all videos in the EgoCOT dataset $\alpha = 4 . 9$ seconds). For each video segment, we provide prompts and corresponding captions for ChatGPT [55] to generate a reasonable and detailed embodied planning. The caption is typically a brief introduction such as $" C$ opens a drawer." We use the ChatGPT to generate a chain of thought according to the caption and organize it into a list of verb-noun pairs, such as "plans: grasp the handle with the gripper and pull the handle; actions: 1. grasp(handle, gripper) 2. pull(handle)." The prompt we used to generate EgoCOT dataset is shown in Listing 2. To enhance the diversity of generated chain of thoughts, we employ a temperature parameter of 0.9 and a top-p parameter of 0.95. For each prompt, we perform five sampling iterations.
+
+Post-procedure. To ensure the quality of the generated planning instructions, we perform the second stage of data cleaning. We used the CLIP model [56] to assess the similarities between the video and text pairs. For each video, we compared it against five potential embodied plans and selected the one with the highest similarity as the corresponding label for the embodied plan. We then took our data-cleaning process a step further by filtering out any video-caption-planning pairs with similarities lower than the threshold. We eliminated both data with the low similarity between the video and caption and between the video and planning to ensure the highest quality data for our EgoCOT dataset. For each keyframe of the video segment, we use the CLIP model to encode both the text data $T$ and the image data $I$ into a shared embedding space. The similarity is calculated using the cosine similarity function as $\begin{array} { r } { S ( y _ { T } , y _ { I } ) = \frac { y _ { T } \cdot y _ { I } } { \| y _ { T } \| \| y _ { I } \| } } \end{array}$ where $S ( y _ { T } , y _ { I } )$ denotes the similarity between the text and image, and $y _ { T }$ and $y _ { I }$ are the respective embeddings. Given that each video contains multiple keyframes, an ensemble of similarity scores is obtained for each video. This ensemble strategy helps to alleviate the problem of variability among individual frames and ensures a more robust and representative measure of overall similarity. The ensemble similarity score between a video $V$ with $n$ keyframes and text data $T$ is given by:
+
+![](images/f88e48ea60437f53f6cdd80db9cd910e7d47473db826536163efbe6b3d820d80.jpg)  
+Listing 2: Prompt we used for creating EgoCOT dataset.
+
+$$
+E ( V , T ) = \frac { 1 } { n } \sum _ { i = 1 } ^ { n } S ( y _ { T { i } } , y _ { I { i } } )
+$$
+
+where $E ( V , T )$ is the ensemble similarity score, $S ( y _ { T _ { i } } , y _ { I _ { i } } )$ is the similarity score for the $i \cdot$ -th keyframe, and $n$ is the total number of keyframes. We also created the EgoVQA dataset specifically for egocentric human-object interaction video question answering tasks to enrich the training data. For each caption in the Ego4D dataset, we used ChatGPT to generate five QA pairs. To ensure relevance, we guided ChatGPT to focus on core key verbs and nouns by designing prompts as shown in Listing 3. The sampling schema when crafting EgoVQA is the same to that as EgoCOT.
+
+![](images/53ca864cb8d21af9637f28cc41bccf3d613fb213ab59c51a0ff3a9766fdf6e90.jpg)
+
+Listing 3: Prompt used for creating EgoVQA dataset.
+
+# 4 Experiments
+
+In this section, we present a comprehensive evaluation of multi-modal foundation models and EmbodiedGPT, across various tasks including visual captioning, embodied planning, and control.
+
+Evaluation on image input tasks. In order to evaluate the quality of generated captions and planning with the given image, we conducted a user study with 30 participants. The study included 10 cases of image caption tasks from MS-COCO dataset [44], 5 embodied planning scenarios in different embodied AI simulators, and 5 real-world scenes with accompanying embodied planning tasks. Participants were asked to rate the generated captions from different end-to-end models on five dimensions using a scoring system ranging from 1 to 10: object recognition accuracy, spatial
+
+<table><tr><td>Model</td><td>Object(↑)</td><td>Spatial(↑)</td><td>Redundancy(↓)</td><td>Plan Reasonable(↑)</td><td>Plan Executable(↑)</td></tr><tr><td>Minigpt4</td><td>5.6</td><td>4.8</td><td>4.4</td><td>4.5</td><td>4.8</td></tr><tr><td>LLaVA-7B</td><td>7.3</td><td>7.4</td><td>3.9</td><td>7.5</td><td>6.6</td></tr><tr><td>LLaVA-13B</td><td>8.5</td><td>8.6</td><td>3.4</td><td>8.4</td><td>7.6</td></tr><tr><td>EmbodiedGPT</td><td>8.4</td><td>8.8</td><td>2.6</td><td>8.8</td><td>8.4</td></tr></table>
+
+Table 1: Generate Quality Evaluation on image input tasks.
+
+Human: Give a detailed explanation of the image, describe the scene and the observed objects, and spatial relationships. Where should I go if I wish to find a coat hanger?
+
+# EmbodiedGPT:
+
+1. The image depicts a doorway leading into a bedroom, with a clock hanging on the wall beside it and a picture of a woman further to the right. 2. There is a white closet suitable for storing clothes on the right side of the bedroom. 3. If one needs to hang a coat, the white closet on the left seem to be the logical choice.
+
+![](images/001decb7a01902b8d965d202104431b85dcdce1f214c2d589a9ac68a632cee71.jpg)  
+Figure 3: Comparison between EmbodiedGPT and VisualGPT in the question-answering task.
+
+# Visual ChatGPT:
+
+The image shows a bathroom with a clock on the wall and a toilet in the comer. The clock is white and has a black face with two hands owalls are tiled and there is a window in the room. The image shows a bathroom with a clock and a toilet. There is no coat hanger visible in the image.
+
+![](images/05c50bb1bd98096d39a333098a8c0b4f020d9a1715c54b4f5306ea6898926f3d.jpg)  
+Figure 4: Example of video input embodied AI tasks on Meta-World benchmark. EmbodiedGPT accurately analyzes embodied control tasks in demonstration videos and provides precise planning.
+
+relationship understanding, level of redundancy in the answer, and reasonability of the planning and the executability of the planning. The average scores among all the participants for different models are shown in Table 1. The results demonstrate that EmbodiedGPT achieves a comparable level of object recognition and spatial relationship understanding as the LLaVA-13B model, despite having only 7B parameters in the language model. Furthermore, EmbodiedGPT generates less redundant content in relation to the given embodied AI task, and produces the most reasonable and executable planning outputs. We also compared the performance of EmbodiedGPT with Visual ChatGPT [33], which adopts a hierarchical approach by combining several pre-trained vision models and language models to answer questions. In the Virtual-Home [57] benchmark, Visual ChatGPT uses a visual caption model to generate dense captions that are subsequently passed into ChatGPT for deriving a solution. As shown in Figure 3, Visual ChatGPT failed to find a coat hanger due to its limitations of relying solely on the caption model for extracting visual information, resulting in poor performance when compared to the end-to-end model like EmbodiedGPT. These findings highlight the advantages of adopting a unified, end-to-end model over hierarchical approaches that rely on multiple stages.
+
+Evaluation on video input embodied AI tasks. We evaluate the recognition ability of videos and planning abilities of our model for embodied control tasks on standard embodied AI benchmarks, Franka Kitchen [14] and Meta-World [15]. Meta-World provides a challenging set of tasks that require complex object manipulation skills, including assembling a ring on a peg, picking and placing a block between bins, pushing a button, opening a drawer, and hammering a nail. Franka Kitchen benchmark focuses on tasks like sliding open the right door, opening the cabinet, turning on the light, turning the stovetop knob, and opening the microwave. As shown in Figure 4, given a demonstration video, EmbodiedGPT can accurately interpret the embodied control task and provide step-by-step planning. The output planning is fed into the Embodied-former module of EmbodiedGPT to query highly relevant features for use as inputs in the policy network and the low-level actions are generated by the policy network to interact with the environment (see more visualizations in Appendix B).
+
+![](images/0160d27591b3d09e7eaedb127cc3b00d03db165b943d53184b3305576735ae9b.jpg)  
+Figure 5: Performance of EmbodiedGPT in low-level control tasks with 10 demonstration demos.
+
+![](images/df491cd02c0f0c38fb666ff242825a0d5bbc6a55f784d5de5d5359816a4fd65a.jpg)  
+Figure 6: Performance of EmbodiedGPT in low-level control tasks with 25 demonstration demos.
+
+Evaluation on embodied control tasks. For embodied control tasks, we compare our model with R3M[12], which is the state-of-the-art method in these two benchmarks, and an ablation version called ’BLIP-2[Ego4D]’, which has the same structure and same amount of parameters as EmbodiedGPT, and is only fine-tuned on the video caption task using the Ego4D dataset without incorporating EgoCOT. In all experiments, the policy network is learned using few-shot learning on a small amount of demonstration data. There are two settings, one of which utilizes 10 demonstrations, and the other utilizes 25 demonstrations. We report the success rate in 100 random evaluations with only visual observations in 5 tasks per benchmark over 5 seeds and 2 different camera views for each setting, respectively. As shown in Figure 5 and Figure 6, EmbodiedGPT outperforms the baseline methods, demonstrating the effectiveness of learning with EgoCOT.
+
+<table><tr><td>Model</td><td></td><td></td><td></td><td>Franka(10 demos） Franka(25 demos）Meta-World(10 demos）Meta-World(25 demos)</td></tr><tr><td>EmbodiedGPT</td><td>50.8% ±2.8</td><td>58.5% ±2.7</td><td>76.4% ±2.2</td><td>81.2%±2.0</td></tr><tr><td> - Close-loop</td><td>38.6% ±2.9</td><td>47.3% ±2.5</td><td>62.7% ±2.2</td><td>64.9% ±2.0</td></tr><tr><td>- COT</td><td>26.2% ±3.2</td><td>36.4% ±2.7</td><td>55.2% ±2.4</td><td>58.7% ±2.0</td></tr></table>
+
+Table 2: Ablation on the closed-loop spans from planning to low-level control, and "chain-of-thought" (COT) training with 25 and 10 demonstrations("-" symbol indicates "removing"). We report the average success rate over 5 tasks and 2 camera views per benchmark.
+
+Human: You are the controller of the robot shown in this image, now your task is put all the objects into the brown bag next to the robot, please write a step by step plan:
+
+# EmbodiedGPT:
+
+Step1. Move the robotic arm to the top of the gray bottle.   
+Step 2. Close the gripper and grasp the gray bottle.   
+Step 3. Move the arm to the brown bag.   
+Step 4. Release the bottle.   
+Step 5. Move the arm to the top of the cereal bag.   
+Step 6. Close the gripper and grasp the cereal bag.   
+Step 7. Move the arm to the brown bag.   
+Step 8. Finally, release the cereal bag.
+
+![](images/3316f62c7675dd171b331170085e6e260ebf69d698cc8cc7e888baf69cfee6f3.jpg)  
+Franka Emika Panda arm with front facing camera
+
+![](images/76cad0554ff107c57262e8ed75c33179d625a0e754eb6101b691c7f73c573fbe.jpg)  
+Figure 7: Generated plan by EmbodiedGPT for rearrangement task.   
+Figure 8: Real-world experiment on the Franka Emika robot arm.
+
+Ablation study. We perform ablation studies to analyze the effectiveness of the "Chain-of-Thought" training mode and the importance of a closed-loop design for embodied control. The results, as shown in Table 2, demonstrate a significant improvement in success rate when using the EgoCOT approach compared to training solely with the EGO4D caption task. Moreover, the closed-loop design is necessary as the generated plans contained specific and relevant sub-goal information, which proved crucial for control tasks. In summary, EmbodiedGPT exhibits a strong ability to generate reasonable planning, accurately extract task-relevant features from visual inputs, as well as execute low-level actions to interact with the environment. The ablation experiments demonstrate that both the training paradigm based on EgoCOT and the closed-loop design from embodied planning to low-level control significantly contribute to the performance improvement of EmbodiedGPT.
+
+Real robot experiment. We also conducted a real-world experiment using the Franka Emika robot arm, emphasizing a rearrangement task where the objective was to pack scattered bottles from a table into a box. As depicted in Figures 7 and 8, model is capable of generating intricate plans and executing of low-level actions based on 50 demonstration.
+
+# 5 Conclusion
+
+In this paper, we present EmbodiedGPT, an end-to-end multi-modal foundational model for embodied AI that enables agents to perform step-by-step planning and execute low-level commands. To achieve this, we create a large-scale embodied planning dataset called EgoCOT and develop an efficient training approach that utilizes prefix tuning to generate high-quality plans with a "chain-of-thought". Furthermore, our embodied control paradigm seamlessly coordinates high-level planning and lowlevel control. Extensive experiments demonstrate the effectiveness of EmbodiedGPT on various embodied tasks, achieving state-of-the-art or comparable performance. We believe that EmbodiedGPT represents a significant step towards developing more intelligent embodied AI agents.
+
+Future works and limitations: EmbodiedGPT freezes the parameters of the vision and language model due to limited computational resources. Joint training with all modules and exploring other modalities, such as speech, could be future works. We do not foresee obvious undesirable ethical or social impacts at this moment.
+
+Acknowledgement. This paper is partially supported by the National Key R&D Program of China No.2022ZD0161000 and the General Research Fund of Hong Kong No.17200622. We would like to express our appreciation to Dr. René Zurbrügg from ETH AI Center for his support on robot experimental platform.
+
+References   
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+        "text": "EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought ",
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+        "text": "Yao ${ { \\bf { M } } { \\bf { u } } ^ { 1 } }$ , Qinglong Zhang2, Mengkang $\\mathbf { H } \\mathbf { u } ^ { 1 }$ , Wenhai $\\mathbf { W a n g ^ { 3 } }$ , Mingyu Ding∗,1, $\\mathbf { J u n \\mathbf { \\mathbf { J i n } } ^ { 4 } }$ , Bin Wang4, Jifeng Dai2,5, Yu Qiao2, Ping Luo∗,1,2 1The University of Hong Kong, 2Shanghai AI Laboratory, 3The Chinese University of Hong Kong, 4Noah’s Ark Laboratory 5Tsinghua University ",
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+        "text": "Abstract ",
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+        "text": "Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understanding and execution capabilities. To achieve this, we have made the following efforts: (i) We craft a large-scale embodied planning dataset, termed EgoCOT. The dataset consists of carefully selected videos from the Ego4D dataset, along with corresponding high-quality language instructions. Specifically, we generate a sequence of sub-goals with the \"Chain of Thoughts\" mode for effective embodied planning. (ii) We introduce an efficient training approach to EmbodiedGPT for high-quality plan generation, by adapting a 7B large language model (LLM) to the EgoCOT dataset via prefix tuning. (iii) We introduce a paradigm for extracting task-related features from LLM-generated planning queries to form a closed loop between high-level planning and low-level control. Extensive experiments show the effectiveness of EmbodiedGPT on embodied tasks, including embodied planning, embodied control, visual captioning, and visual question answering. Notably, EmbodiedGPT significantly enhances the success rate of the embodied control task by extracting more effective features. It has achieved a remarkable 1.6 times increase in success rate on the Franka Kitchen benchmark and a 1.3 times increase on the Meta-World benchmark, compared to the BLIP-2 baseline fine-tuned with the Ego4D dataset. More demos, code, and dataset information can be found at our homepage. ",
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+        "text": "1 Introduction ",
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+        "text": "Embodied AI tasks, e.g., embodied planning, embodied VQA, and embodied control, aim to imbue robots with the ability to perceive, reason, and act within their environment, enabling them to perform long-horizon plans and execute actions autonomously based on real-time observations. Recently, large language models (LLMs) such as GPT4 [1] and PaLM-E [2], have shown promising language understanding, reasoning, and \"chain-of-thought\" capabilities. Such advances may open new possibilities for developing robots capable of processing natural language instructions, performing multi-modal \"chain-of-thought\", and planning actions in physical environments. ",
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+        "text": "Large-scale datasets play important roles in training large language models. For example, OpenCLIP trains its ViT-G/14 model on the LAION-2B dataset [3], which contains 2B image-language pairs. Unlike general-purpose visual language tasks that can get a huge amount of weakly labeled imagecaption pairs from the Internet, embodied AI tasks require egocentric data in robotics domains. Also, ",
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+        "text": "Human: Can you write a detailed plan for the task the robot is performing? ",
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+            "Figure 1: EmbodiedGPT’s capabilities for video captioning, multi-turn question answering, embodied planning, and low-level control. The plans given by EmbodiedGPT are highly executable and incorporate task-specific features, leading to a significant improvement in the success rate of embodied control tasks, outperforming both R3M [12] (a video-language contrastive learned model) and BLIP2 [13] (a multi-modal foundation model) on Franka Kitchen [14] and Meta-World [15] environments. "
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+        "text": "structured language instructions are needed for precise planning, which usually requires huge manual efforts and costs. This poses a challenging problem in collecting high-quality embodied multi-modal data. Some researchers [4, 5, 6, 7] explore creating large-scale embodied datasets with simulators, but a significant gap remains between simulation and the real world. Recent works [8, 9, 10] also explore adapting the pre-trained LLMs to a new domain by efficient tuning strategies like LoRA [11]. However, several open questions still remain: how to apply LLMs to the field of robotics which may face large domain gaps; how to leverage the \"chain-of-thought\" capability for structured planning; and how to use the output language plan for downstream manipulation tasks in an end-to-end manner. ",
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+        "text": "To solve the above challenges, in this work, we first build a large-scale embodied planning dataset, termed EgoCOT, which features chain-of-thought planning instructions. It contains carefully selected egocentric videos from the Ego4D dataset [16] and corresponding high-quality step-by-step language instructions, which are machine-generated, then semantics-based filtered, and finally human-verified. Additionally, we also create the EgoVQA dataset as an extension of the Ego4D dataset, focusing on egocentric human-object interaction video question answering tasks, which aims to offer a wider range of egocentric multi-modal data. ",
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+        "text": "Based on our EgoCOT and EgoVQA, we present an end-to-end multi-modal embodied foundation model called EmbodiedGPT, which can interact with the physical world in a more natural and intuitive manner, and perform many embodied tasks, as shown in Figure 1, such as embodied planning, embodied VQA, and embodied control. EmbodiedGPT comprises four integrated modules that work together, including i) a frozen vision model for encoding visual features of current observations, ii) a frozen language model used to execute natural language for question answering, captioning, and embodied planning tasks, iii) an embodied-former with a language mapping layer for aligning the visual and embodied instructions and extracting task-relevant instance-level features with the generated planning for low-level control, and iv) a policy network, which is responsible for producing low-level actions based on the task-relevant features, enabling the agent to effectively interact with the environment. To further enhance EmbodiedGPT ’s performance in generating reliable planning containing sub-goal sequences, we implement prefix tuning on the frozen language model to encourage the generation of more executable planning. ",
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+        "text": "Our method possesses the following core advantages: i) the generated planning exhibits strong executability and granularity at the object part level, such as the gripper of a robotic arm or the handle of a door, manifested in sub-goal sequences. ii) the proposed EgoCOT dataset is built based on an open-source large-scale dataset, which offers greater scalability compared to the PaLM-E [2] model trained on proprietary robot data. And both the EgoCOT dataset, and the EmbodiedGPT model will be open-sourced. iii) EmbodiedGPT forms a closed-loop from high-level planning to low-level control, which enables seamless integration of high-level planning and low-level control, providing efficient task performance and adaptability to a wide range of tasks. To achieve this, we utilize the embodied-former to query task-relevant instance-level features through cross-attention between visual observations and generated embodied planning. This enables the policy network to complete low-level control tasks with fewer than 25 demonstrations. ",
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+        "text": "The contributions can be summarized as follows: (i) We build an end-to-end multi-modal foundation model EmbodiedGPT for embodied AI, which is featured with \"chain-of-thought\" capability, empowering embodied agents to interact with the physical world in a more natural and intuitive manner. (ii) We develop two datasets, EgoCOT and EgoVQA, consisting of 200M annotated videos from the Ego4D dataset with corresponding detailed planning instructions and VQA data. The datasets are first machine-generated, then semantics-based filtered, and finally human-verified for quality control. (iii) We introduce EmbodiedGPT a cost-effective training approach and a paradigm for extracting task-relevant features from LLM-generated planning queries, thereby forming a closed loop between high-level planning and low-level control. We demonstrate our approach’s effectiveness by achieving state-of-the-art or comparable performance on multiple embodied tasks, including embodied control, embodied planning, video captioning, and video QA. Notably, in comparison to BLIP-2 [17] fine-tuned on the Ego4D dataset and R3M [12] specifically designed for manipulation tasks, EmbodiedGPT outperforms both models on the Franka Kitchen [14] benchmark with a margin of $2 2 . 1 \\%$ and $5 . 5 \\%$ , respectively. Similarly, on the Meta-World [14] benchmark, EmbodiedGPT surpasses both models with margins of $2 2 . 5 \\%$ and $4 . 2 \\%$ , respectively. ",
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+        "text": "2 Related Work ",
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+        "text": "2.1 Vision Language Pre-training with large scale foundation model ",
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+        "text": "Vision-Language Pre-training focuses on strengthening the link between visual observation and natural language. The goal is to develop models that can better understand and process visual content, such as recognizing objects and actions, and generating descriptive text. As models become larger, the computational expense for end-to-end pre-training rises, leading to the need for modular vision-language pre-training methods. These methods smartly use pre-trained models, keeping them ‘frozen’ during vision language pre-training to save on computational costs. For example, models like Uniter [18], Oscar [19], VinVL [20], and LiT [21] freeze the image encoder, while Frozen [22] and VGPT [23] freeze the language model. Furthermore, Flamingo [24] and BLIP-2 [17] use both frozen image encoders and language models, providing a balance between performance and computational efficiency. Due to the lack of open-source data for multi-modal embodied planning, previous works struggled to perform detailed task decomposition and lacked the ability to generate precise and executable plans. To tackle this issue, we create the EgoCOT dataset and develop an embodied chain-of-thought vision language pre-training framework to enhance the capacity of multi-modal models for embodied reasoning and planning. ",
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+        "type": "text",
+        "text": "2.2 Egocentric Video Datasets. ",
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+        "type": "text",
+        "text": "Egocentric videos, which are captured using wearable cameras, provide a natural perspective of daily activities and pose several challenging research questions [25, 26, 27]. Several egocentric video datasets have been created over the years, including [28, 29, 30]. However, the collection of egocentric videos is expensive, and previous datasets tend to be small-scale and domain-specific. Recently, a massive egocentric video dataset, Ego4D [16], has been released and has been used for embodied representation learning. The dataset comprises 3,670 hours of videos collected by 931 people from 74 locations across 9 countries, with videos accompanied by narrations. For embodied AI tasks, learning from large and diverse egocentric human videos has emerged as a promising approach to acquiring a generally useful visual representation for controlling such tasks. For example, R3M [12] developed a sparse and compact visual representation using the Ego4D human video dataset through a combination of time-contrastive learning and video-language alignment. VIP [31], learns general-purpose reward functions for goal-conditioned robotic manipulation using the Ego4D dataset. ",
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+    {
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+        "img_path": "images/d3004a6413492a3e7d506cbfb1f7e8c59fedcfea501214e5e543ca4f7073d656.jpg",
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+            "Figure 2: Overall framework of EmbodiedGPT. The black arrow shows the vision-language planning process, while the red arrow represents that we leverage the queried language plans for better policy learning in low-level control tasks. "
+        ],
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+        "type": "text",
+        "text": "2.3 Large Foundation Model Assistant System ",
+        "text_level": 1,
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+        "text": "Recent advancements in large-scale multi-modal language models (LLMs), such as GPT-3 [32] and GPT-4 [1], have resulted in the creation of various models that can understand multiple modes of information. Two main approaches are used in this field: systematic collaboration and end-to-end trained models. Systematic collaboration approaches involve coordinating multiple vision models or tools with language models to combine visual information with textual descriptions. Examples include models like Visual ChatGPT [33], MM-REACT [34], and HuggingGPT [35]. However, this approach is limited by the accuracy and capacity of fixed modular models, which can lead to an accumulation of errors. On the other hand, end-to-end models aim to provide unified models for multi-modal tasks. For example, Flamingo [24] combines vision and language by freezing pre-trained vision encoders and language models. BLIP-2 [13] introduces Q-Former to align visual features from frozen visual encoders with large language models. Recently, models such as MiniGPT-4 [36] and LLaVA [37] align instruction-tuned language models with visual features from frozen visual backbones. VideoChat[38], mPLUG-Owl [39] and X-LLM [40], further expand support for video input. PaLM-E [41] is the first large embodied multi-modal model, which directly incorporates features from sensor modalities to improve real-world performance and is trained with their largescale everyday robot data [42]. Compared to PaLM-E, EmbodiedGPT is more compact, with a size of only 10B and offers additional support for video captioning, video QA and making planning according to a demonstration video. Furthermore, we form a closed-loop system that spans from high-level planning to low-level control. ",
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+        "type": "text",
+        "text": "3 Method ",
+        "text_level": 1,
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+        "text": "The goal of the embodied foundation model is to imitate human-like perception and interaction with the environment by accurately perceiving the environment, identifying relevant objects, analyzing their spatial relationships, and formulating a detailed task plan. To achieve this, the EmbodiedGPT employs a pre-trained vision transformer as the visual encoder and a pre-trained LLaMA [43] model as the language model. As shown in Figure 2, the embodied-former acts as a bridge between the visual and language domains, it first extracts compact visual features from the output of the vision model through attention-based interaction involving visual tokens, text queries, and learnable embodied queries and then maps it to the language modality through a language mapping layer. These embeddings are sent to the frozen LLaMA [43] language model for visual caption, visual QA, and embodied planning. The generated planning is then used to query highly relevant features from the general visual tokens encoded by the visual model via the embodied-former. These features are utilized to generate low-level control commands for task execution through the downstream policy network. To enhance performance across a range of embodied tasks, we introduce a novel video-language pre-training paradigm that leverages a cognitive chain of thought to produce embodied planning from egocentric video inputs. We formulate this task as a standard VQA (Visual Question Answering) task, using \"how to do the task that $^ +$ original caption\" as the question and embodied planning as the answer. This framework enriches the data of embodied planning and standard visual QA tasks, encouraging the embodied-former to capture task-specific features that are more suitable for embodied control tasks. ",
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+        "type": "text",
+        "text": "3.1 Framework ",
+        "text_level": 1,
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+        "text": "The training process consists of three stages, each designed to incrementally develop reasoning and planning capabilities. The first two stages focus on pre-training in basic cognitive and responsive skills, while the third stage involves training the embodied AI task with egocentric video-text data on EgoCOT. In the first stage, we focus on image-text conversation alignment pre-training, which involves using three datasets: COCO Caption [44], 595 thousand finely filtered image-text pairs from CC3M [45], and 491 thousand filtered image-text pairs obtained by re-captioning LAION-400M using BLIP-2 [17]. The primary goal of this stage is to pre-train the Embodied-former and language projection while keeping the vision and language model parameters frozen to save computational resources. In the second stage, our goal is to enhance the model’s ability to comprehend and generate more complex sentences and improve its reasoning skills. We achieve this by updating the language projection and prefix language adapter and utilizing the \"Complex_Reasoning_ $. 7 7 \\mathrm { k } \"$ and multi-turn conversation datasets provided by \"LLaVA_Instruct_150K\" [46]. ",
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+        "text": "Embodied \"chain-of-thought\" training with EgoCOT: During the third stage, we employ Conv3D [47] to adapt the pre-trained vision model from stage 2 for video encoding, using a total of eight evenly distributed keyframes from each video. Each keyframe is partitioned into threedimensional (3D) patches, which can be visualized as spatio-temporal cubes, adeptly capturing both the visual content and the sequence of events within the video. These 3D patches are subsequently encoded into visual tokens via the Conv3D module with a time offset of 2 and are then integrated into the internal vision transformer. Then, we introduce the ’chain-of-thought’ vision language pre-training paradigm where the model takes 8 keyframes of the video as input, along with the task description, embodied planning, and structured verb-noun pairs summary to reason with a prompt, such as Listing 1. To avoid overfitting, we provide a prompt set that has different instructions with the same meaning. In this stage, we fine-tune the patch embedding, the language projection layer, and the prefix language adapter to better capture temporal information. ",
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+        "text": "Watch this video , identify the actions and devise a plan using chain -of - thought . Extract detailed actions using this schema :   \nTask : {\" task description \" }   \nPlan : {\" plan with chain -of - thought \"} Actions : {{ \" number \" }: { ’ verb ’ }({ ’ noun ’}) }. ",
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+        "type": "text",
+        "text": "Listing 1: Prompt we used for chain-of-thought pre-training. ",
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+        "text": "3.2 Model Architecture ",
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+        "text": "The Embodied-former, denoted as $\\mathcal { E } ( \\cdot )$ , serves as a bridge between visual input $x _ { \\mathrm { v i s } }$ and the frozen language model, acting as an information bottleneck that delivers the most relevant visual data to the language model. The Embodied-former consists of two sub-modules: one for extracting features from the image input, denoted as $\\mathcal { E } _ { \\mathrm { v i s } } : x _ { \\mathrm { v i s } } \\to y _ { \\mathrm { v i s } }$ , and another for extracting features from the text input, denoted as $\\mathcal { E } _ { \\mathrm { t x t } } : x _ { \\mathrm { t x t } } \\to y _ { \\mathrm { t x t } } .$ . We employ $N$ learnable embodied query embeddings $y _ { \\mathrm { { q u e r y } } }$ as the input of $\\mathcal { E }$ to interact with $x _ { \\mathrm { v i s } }$ through cross-attention layers and with $x _ { \\mathrm { t x t } }$ through self-attention layers. We denote the output query representation as $z \\in \\mathbb { R } ^ { N \\times D }$ , where $D$ is the dimensionality of the embeddings. The dimension of $z$ is significantly smaller than that of the visual features. The output query embeddings are then transformed to $z ^ { ' } \\in \\mathbb { R } ^ { N \\times D ^ { ' } }$ , which have the same dimensionality $D ^ { ' }$ as the LLM’s text embedding in the language modality. This transformation is performed by a mapping function denoted as $M : z \\to z ^ { ' }$ , which is accomplished by a linear projection via a fully-connected (FC) layer. The projected embeddings, $z ^ { \\prime }$ , serve as \"soft visual prompts for the language model,\" decoupling the whole interaction into visual-query interaction and query-text interaction. The final embodied planning is inferred by the language model with $z ^ { \\prime }$ and text prompt(shown as Listing 1) as input. For low-level control which aims to generate actions to interact with the environment, the embodied plan $x _ { \\mathrm { p l a n } }$ is used as input text for embodied-former to query the task-relevant instance level features ${ z _ { \\mathrm { i n s t a n c e } } = \\mathcal { E } ( x _ { \\mathrm { v i s } } , x _ { \\mathrm { p l a n } } , y _ { \\mathrm { q u e r y } } ) }$ . Subsequently, the agent is capable of generating control commands, such as the turning angle of the servo, represented as $a = g ( z _ { \\mathrm { i n s t a n c e } } , z _ { \\mathrm { g l o b a l } } )$ . This function combines both the instance-specific information zinstance and the global context $z _ { \\mathrm { g l o b a l } }$ . The global context is inferred using a ResNet50 model [48] that has been pre-trained on ImageNet [49], employing global average pooling. Here, $g ( \\cdot )$ represents the policy network, which is a Multi-Layer Perceptron (MLP) [50] mapping function. The output of the policy network consists of specific executable actions, such as positions and velocities in the Cartesian coordinate system. More implementation details can be found in Appendix A. ",
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+        "text": "3.3 Training Settings ",
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+        "text": "We employ the same pre-trained image encoder as BLIP-2[17]. Specifically, we utilize the ViT-G/14 model from EVA-CLIP [51] and remove its last layer, using the output features of the second last layer instead. For the frozen language model, we adopt a pre-trained LLaMA-7B [43] model and fine-tune it using the ShareGPT dataset and a GPT-4 generated 52K English instruction-following dataset [52]. We then utilize the well-fine-tuned language model as the frozen language model for vision-language pre-training. Additionally, we convert the data type of parameters of the frozen ViT [53] and language model to FP16 during pre-training to increase efficiency. ",
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+    {
+        "type": "text",
+        "text": "3.4 Creating EgoCOT and EgoVQA Dataset ",
+        "text_level": 1,
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+        "type": "text",
+        "text": "For our EgoCOT dataset, we obtain basic data from the Ego4D dataset [16], which includes 9, 645 untrimmed videos of various durations ranging from 5 seconds to 7 hours. To prepare the data for our purposes, we conducted two stages of data cleaning to prepare our data. In the first stage, we filtered out videos with missing or very short narrations (which made up $7 . 4 \\%$ and $0 . 9 \\%$ of the text, respectively), as well as those with unsure tags (which accounted for $4 . 0 \\%$ of the text). We also excluded videos without human-object interaction, such as watching TV or walking. After this stage, we were left with 2.9 thousand hours of video, containing 3.85 million narrations, from 129 different scenarios covering 2927 hours of video. ",
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+        "type": "text",
+        "text": "To generate pairs of captions, embodied plannings, and corresponding video segments with time intervals, we utilized the EgoVLP framework [54] to segment the video. The narrations are organized as a sequence of sentences $\\mathcal { T } _ { 0 } , \\cdots , \\mathcal { T } _ { n }$ with precise timestamps $t _ { 0 } , \\cdots , t _ { n }$ that indicate when a described event occurred. For each narration $\\mathcal { T } _ { i }$ with timestamp $t _ { i }$ , we paired it with a clip $\\nu _ { i }$ by determining its start and end time points: ",
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+    {
+        "type": "equation",
+        "img_path": "images/f6e40729880b876402b988c1b027dfc6723888330f856add9d8f87ccca5d336e.jpg",
+        "text": "$$\n[ t _ { i } ^ { s t a r t } , t _ { i } ^ { e n d } ] = [ t _ { i } - \\beta _ { i } / 2 \\alpha , t _ { i } + \\beta _ { i } / 2 \\alpha ] ,\n$$",
+        "text_format": "latex",
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+    {
+        "type": "text",
+        "text": "where $\\textstyle \\beta _ { i } = \\sum _ { j = 0 } ^ { n - 1 } \\left( t _ { j + 1 } - t _ { j } \\right) / n$ is an adjustable parameter equal to the average temporal distance between consecutive narrations in a given video. Conversely, $\\alpha$ is a scale factor computed as the average of all $\\beta _ { i }$ across all videos in the EgoCOT dataset $\\alpha = 4 . 9$ seconds). For each video segment, we provide prompts and corresponding captions for ChatGPT [55] to generate a reasonable and detailed embodied planning. The caption is typically a brief introduction such as $\" C$ opens a drawer.\" We use the ChatGPT to generate a chain of thought according to the caption and organize it into a list of verb-noun pairs, such as \"plans: grasp the handle with the gripper and pull the handle; actions: 1. grasp(handle, gripper) 2. pull(handle).\" The prompt we used to generate EgoCOT dataset is shown in Listing 2. To enhance the diversity of generated chain of thoughts, we employ a temperature parameter of 0.9 and a top-p parameter of 0.95. For each prompt, we perform five sampling iterations. ",
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+        "type": "text",
+        "text": "Post-procedure. To ensure the quality of the generated planning instructions, we perform the second stage of data cleaning. We used the CLIP model [56] to assess the similarities between the video and text pairs. For each video, we compared it against five potential embodied plans and selected the one with the highest similarity as the corresponding label for the embodied plan. We then took our data-cleaning process a step further by filtering out any video-caption-planning pairs with similarities lower than the threshold. We eliminated both data with the low similarity between the video and caption and between the video and planning to ensure the highest quality data for our EgoCOT dataset. For each keyframe of the video segment, we use the CLIP model to encode both the text data $T$ and the image data $I$ into a shared embedding space. The similarity is calculated using the cosine similarity function as $\\begin{array} { r } { S ( y _ { T } , y _ { I } ) = \\frac { y _ { T } \\cdot y _ { I } } { \\| y _ { T } \\| \\| y _ { I } \\| } } \\end{array}$ where $S ( y _ { T } , y _ { I } )$ denotes the similarity between the text and image, and $y _ { T }$ and $y _ { I }$ are the respective embeddings. Given that each video contains multiple keyframes, an ensemble of similarity scores is obtained for each video. This ensemble strategy helps to alleviate the problem of variability among individual frames and ensures a more robust and representative measure of overall similarity. The ensemble similarity score between a video $V$ with $n$ keyframes and text data $T$ is given by: ",
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+        "img_path": "images/f88e48ea60437f53f6cdd80db9cd910e7d47473db826536163efbe6b3d820d80.jpg",
+        "image_caption": [
+            "Listing 2: Prompt we used for creating EgoCOT dataset. "
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+        "text": "",
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+    {
+        "type": "equation",
+        "img_path": "images/d57eea2936c62d1751f662c85f55bc46f407e1cf79a190b8e0c91815618b06ff.jpg",
+        "text": "$$\nE ( V , T ) = \\frac { 1 } { n } \\sum _ { i = 1 } ^ { n } S ( y _ { T { i } } , y _ { I { i } } )\n$$",
+        "text_format": "latex",
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+    {
+        "type": "text",
+        "text": "where $E ( V , T )$ is the ensemble similarity score, $S ( y _ { T _ { i } } , y _ { I _ { i } } )$ is the similarity score for the $i \\cdot$ -th keyframe, and $n$ is the total number of keyframes. We also created the EgoVQA dataset specifically for egocentric human-object interaction video question answering tasks to enrich the training data. For each caption in the Ego4D dataset, we used ChatGPT to generate five QA pairs. To ensure relevance, we guided ChatGPT to focus on core key verbs and nouns by designing prompts as shown in Listing 3. The sampling schema when crafting EgoVQA is the same to that as EgoCOT. ",
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+    {
+        "type": "image",
+        "img_path": "images/53ca864cb8d21af9637f28cc41bccf3d613fb213ab59c51a0ff3a9766fdf6e90.jpg",
+        "image_caption": [],
+        "image_footnote": [],
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+    {
+        "type": "text",
+        "text": "Listing 3: Prompt used for creating EgoVQA dataset. ",
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+        "type": "text",
+        "text": "4 Experiments ",
+        "text_level": 1,
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+        "text": "In this section, we present a comprehensive evaluation of multi-modal foundation models and EmbodiedGPT, across various tasks including visual captioning, embodied planning, and control. ",
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+        "text": "Evaluation on image input tasks. In order to evaluate the quality of generated captions and planning with the given image, we conducted a user study with 30 participants. The study included 10 cases of image caption tasks from MS-COCO dataset [44], 5 embodied planning scenarios in different embodied AI simulators, and 5 real-world scenes with accompanying embodied planning tasks. Participants were asked to rate the generated captions from different end-to-end models on five dimensions using a scoring system ranging from 1 to 10: object recognition accuracy, spatial ",
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+        "type": "table",
+        "img_path": "images/b86cd66f3b065ff4708e85ab8931b95a23a0969d4e0780c57739c6c1deac5722.jpg",
+        "table_caption": [],
+        "table_footnote": [
+            "Table 1: Generate Quality Evaluation on image input tasks. "
+        ],
+        "table_body": "<table><tr><td>Model</td><td>Object(↑)</td><td>Spatial(↑)</td><td>Redundancy(↓)</td><td>Plan Reasonable(↑)</td><td>Plan Executable(↑)</td></tr><tr><td>Minigpt4</td><td>5.6</td><td>4.8</td><td>4.4</td><td>4.5</td><td>4.8</td></tr><tr><td>LLaVA-7B</td><td>7.3</td><td>7.4</td><td>3.9</td><td>7.5</td><td>6.6</td></tr><tr><td>LLaVA-13B</td><td>8.5</td><td>8.6</td><td>3.4</td><td>8.4</td><td>7.6</td></tr><tr><td>EmbodiedGPT</td><td>8.4</td><td>8.8</td><td>2.6</td><td>8.8</td><td>8.4</td></tr></table>",
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "Human: Give a detailed explanation of the image, describe the scene and the observed objects, and spatial relationships. Where should I go if I wish to find a coat hanger? ",
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+    {
+        "type": "text",
+        "text": "EmbodiedGPT: ",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "1. The image depicts a doorway leading into a bedroom, with a clock hanging on the wall beside it and a picture of a woman further to the right. 2. There is a white closet suitable for storing clothes on the right side of the bedroom. 3. If one needs to hang a coat, the white closet on the left seem to be the logical choice. ",
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+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/001decb7a01902b8d965d202104431b85dcdce1f214c2d589a9ac68a632cee71.jpg",
+        "image_caption": [
+            "Figure 3: Comparison between EmbodiedGPT and VisualGPT in the question-answering task. "
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Visual ChatGPT: ",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "The image shows a bathroom with a clock on the wall and a toilet in the comer. The clock is white and has a black face with two hands owalls are tiled and there is a window in the room. The image shows a bathroom with a clock and a toilet. There is no coat hanger visible in the image. ",
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+        "page_idx": 7
+    },
+    {
+        "type": "image",
+        "img_path": "images/05c50bb1bd98096d39a333098a8c0b4f020d9a1715c54b4f5306ea6898926f3d.jpg",
+        "image_caption": [
+            "Figure 4: Example of video input embodied AI tasks on Meta-World benchmark. EmbodiedGPT accurately analyzes embodied control tasks in demonstration videos and provides precise planning. "
+        ],
+        "image_footnote": [],
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+    {
+        "type": "text",
+        "text": "relationship understanding, level of redundancy in the answer, and reasonability of the planning and the executability of the planning. The average scores among all the participants for different models are shown in Table 1. The results demonstrate that EmbodiedGPT achieves a comparable level of object recognition and spatial relationship understanding as the LLaVA-13B model, despite having only 7B parameters in the language model. Furthermore, EmbodiedGPT generates less redundant content in relation to the given embodied AI task, and produces the most reasonable and executable planning outputs. We also compared the performance of EmbodiedGPT with Visual ChatGPT [33], which adopts a hierarchical approach by combining several pre-trained vision models and language models to answer questions. In the Virtual-Home [57] benchmark, Visual ChatGPT uses a visual caption model to generate dense captions that are subsequently passed into ChatGPT for deriving a solution. As shown in Figure 3, Visual ChatGPT failed to find a coat hanger due to its limitations of relying solely on the caption model for extracting visual information, resulting in poor performance when compared to the end-to-end model like EmbodiedGPT. These findings highlight the advantages of adopting a unified, end-to-end model over hierarchical approaches that rely on multiple stages. ",
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+    {
+        "type": "text",
+        "text": "Evaluation on video input embodied AI tasks. We evaluate the recognition ability of videos and planning abilities of our model for embodied control tasks on standard embodied AI benchmarks, Franka Kitchen [14] and Meta-World [15]. Meta-World provides a challenging set of tasks that require complex object manipulation skills, including assembling a ring on a peg, picking and placing a block between bins, pushing a button, opening a drawer, and hammering a nail. Franka Kitchen benchmark focuses on tasks like sliding open the right door, opening the cabinet, turning on the light, turning the stovetop knob, and opening the microwave. As shown in Figure 4, given a demonstration video, EmbodiedGPT can accurately interpret the embodied control task and provide step-by-step planning. The output planning is fed into the Embodied-former module of EmbodiedGPT to query highly relevant features for use as inputs in the policy network and the low-level actions are generated by the policy network to interact with the environment (see more visualizations in Appendix B). ",
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+    {
+        "type": "image",
+        "img_path": "images/0160d27591b3d09e7eaedb127cc3b00d03db165b943d53184b3305576735ae9b.jpg",
+        "image_caption": [
+            "Figure 5: Performance of EmbodiedGPT in low-level control tasks with 10 demonstration demos. "
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 8
+    },
+    {
+        "type": "image",
+        "img_path": "images/df491cd02c0f0c38fb666ff242825a0d5bbc6a55f784d5de5d5359816a4fd65a.jpg",
+        "image_caption": [
+            "Figure 6: Performance of EmbodiedGPT in low-level control tasks with 25 demonstration demos. "
+        ],
+        "image_footnote": [],
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "Evaluation on embodied control tasks. For embodied control tasks, we compare our model with R3M[12], which is the state-of-the-art method in these two benchmarks, and an ablation version called ’BLIP-2[Ego4D]’, which has the same structure and same amount of parameters as EmbodiedGPT, and is only fine-tuned on the video caption task using the Ego4D dataset without incorporating EgoCOT. In all experiments, the policy network is learned using few-shot learning on a small amount of demonstration data. There are two settings, one of which utilizes 10 demonstrations, and the other utilizes 25 demonstrations. We report the success rate in 100 random evaluations with only visual observations in 5 tasks per benchmark over 5 seeds and 2 different camera views for each setting, respectively. As shown in Figure 5 and Figure 6, EmbodiedGPT outperforms the baseline methods, demonstrating the effectiveness of learning with EgoCOT. ",
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+        "page_idx": 8
+    },
+    {
+        "type": "table",
+        "img_path": "images/72d882cb4191745fc39ae1d6a3878be1752df8b5087c2ec997180cbf6c149526.jpg",
+        "table_caption": [],
+        "table_footnote": [
+            "Table 2: Ablation on the closed-loop spans from planning to low-level control, and \"chain-of-thought\" (COT) training with 25 and 10 demonstrations(\"-\" symbol indicates \"removing\"). We report the average success rate over 5 tasks and 2 camera views per benchmark. "
+        ],
+        "table_body": "<table><tr><td>Model</td><td></td><td></td><td></td><td>Franka(10 demos） Franka(25 demos）Meta-World(10 demos）Meta-World(25 demos)</td></tr><tr><td>EmbodiedGPT</td><td>50.8% ±2.8</td><td>58.5% ±2.7</td><td>76.4% ±2.2</td><td>81.2%±2.0</td></tr><tr><td> - Close-loop</td><td>38.6% ±2.9</td><td>47.3% ±2.5</td><td>62.7% ±2.2</td><td>64.9% ±2.0</td></tr><tr><td>- COT</td><td>26.2% ±3.2</td><td>36.4% ±2.7</td><td>55.2% ±2.4</td><td>58.7% ±2.0</td></tr></table>",
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+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Human: You are the controller of the robot shown in this image, now your task is put all the objects into the brown bag next to the robot, please write a step by step plan: ",
+        "bbox": [
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+    {
+        "type": "text",
+        "text": "EmbodiedGPT: ",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Step1. Move the robotic arm to the top of the gray bottle.   \nStep 2. Close the gripper and grasp the gray bottle.   \nStep 3. Move the arm to the brown bag.   \nStep 4. Release the bottle.   \nStep 5. Move the arm to the top of the cereal bag.   \nStep 6. Close the gripper and grasp the cereal bag.   \nStep 7. Move the arm to the brown bag.   \nStep 8. Finally, release the cereal bag. ",
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+        "page_idx": 9
+    },
+    {
+        "type": "image",
+        "img_path": "images/3316f62c7675dd171b331170085e6e260ebf69d698cc8cc7e888baf69cfee6f3.jpg",
+        "image_caption": [
+            "Franka Emika Panda arm with front facing camera "
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "image",
+        "img_path": "images/76cad0554ff107c57262e8ed75c33179d625a0e754eb6101b691c7f73c573fbe.jpg",
+        "image_caption": [
+            "Figure 7: Generated plan by EmbodiedGPT for rearrangement task. ",
+            "Figure 8: Real-world experiment on the Franka Emika robot arm. "
+        ],
+        "image_footnote": [],
+        "bbox": [
+            176,
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+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Ablation study. We perform ablation studies to analyze the effectiveness of the \"Chain-of-Thought\" training mode and the importance of a closed-loop design for embodied control. The results, as shown in Table 2, demonstrate a significant improvement in success rate when using the EgoCOT approach compared to training solely with the EGO4D caption task. Moreover, the closed-loop design is necessary as the generated plans contained specific and relevant sub-goal information, which proved crucial for control tasks. In summary, EmbodiedGPT exhibits a strong ability to generate reasonable planning, accurately extract task-relevant features from visual inputs, as well as execute low-level actions to interact with the environment. The ablation experiments demonstrate that both the training paradigm based on EgoCOT and the closed-loop design from embodied planning to low-level control significantly contribute to the performance improvement of EmbodiedGPT. ",
+        "bbox": [
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+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Real robot experiment. We also conducted a real-world experiment using the Franka Emika robot arm, emphasizing a rearrangement task where the objective was to pack scattered bottles from a table into a box. As depicted in Figures 7 and 8, model is capable of generating intricate plans and executing of low-level actions based on 50 demonstration. ",
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+    },
+    {
+        "type": "text",
+        "text": "5 Conclusion ",
+        "text_level": 1,
+        "bbox": [
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+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "In this paper, we present EmbodiedGPT, an end-to-end multi-modal foundational model for embodied AI that enables agents to perform step-by-step planning and execute low-level commands. To achieve this, we create a large-scale embodied planning dataset called EgoCOT and develop an efficient training approach that utilizes prefix tuning to generate high-quality plans with a \"chain-of-thought\". Furthermore, our embodied control paradigm seamlessly coordinates high-level planning and lowlevel control. Extensive experiments demonstrate the effectiveness of EmbodiedGPT on various embodied tasks, achieving state-of-the-art or comparable performance. We believe that EmbodiedGPT represents a significant step towards developing more intelligent embodied AI agents. ",
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+    },
+    {
+        "type": "text",
+        "text": "Future works and limitations: EmbodiedGPT freezes the parameters of the vision and language model due to limited computational resources. Joint training with all modules and exploring other modalities, such as speech, could be future works. We do not foresee obvious undesirable ethical or social impacts at this moment. ",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Acknowledgement. This paper is partially supported by the National Key R&D Program of China No.2022ZD0161000 and the General Research Fund of Hong Kong No.17200622. We would like to express our appreciation to Dr. René Zurbrügg from ETH AI Center for his support on robot experimental platform. ",
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+    {
+        "type": "text",
+        "text": "References   \n[1] OpenAI. Gpt-4 technical report, 2023.   \n[2] Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, and Pete Florence. Palm-e: An embodied multimodal language model. In arXiv preprint arXiv:2303.03378, 2023.   \n[3] Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt. Openclip, July 2021.   \n[4] Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, and Linxi Fan. 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