# Object-Centric Instruction Augmentation for Robotic Manipulation

Junjie Wen1,∗, Yichen Zhu2,∗, Minjie Zhu1, Jinming Li3, Zhiyuan Xu2, Zhengping
Che2,
Chaomin Shen1,†, Yaxin Peng3, Dong Liu2, Feifei Feng2, and Jian Tang2,†
1School of Computer Science, East China Normal University, China {51255901019,
<EMAIL_ADDRESS><EMAIL_ADDRESS>Group, China
{zhuyc25, xuzy70, chezp, liudong13, feifei.feng,
<EMAIL_ADDRESS>of Mathematics, School of Science, Shanghai
University, China {ljm2022<EMAIL_ADDRESS>contributions. This
work was done during Junjie Wen, Minjie Zhu, and Jinming Li’s internship at
Midea Group. †Corresponding authors: Chaomin Shen and Jian Tang.

###### Abstract

Humans interpret scenes by recognizing both the identities and positions of
objects in their observations. For a robot to perform tasks such as “pick and
place”, understanding both what the objects are and where they are located is
crucial. While the former has been extensively discussed in the literature
that uses the large language model to enrich the text descriptions, the latter
remains underexplored. In this work, we introduce the Object-Centric
Instruction Augmentation (OCI) framework to augment highly semantic and
information-dense language instruction with position cues. We utilize a Multi-
modal Large Language Model (MLLM) to weave knowledge of object locations into
natural language instruction, thus aiding the policy network in mastering
actions for versatile manipulation. Additionally, we present a feature reuse
mechanism to integrate the vision-language features from off-the-shelf pre-
trained MLLM into policy networks. Through a series of simulated and real-
world robotic tasks, we demonstrate that robotic manipulator imitation
policies trained with our enhanced instructions outperform those relying
solely on traditional language instructions. The project is available at
https://oci-robotics.github.io/.

##  I Introduction

A fundamental challenge in deciphering the intelligence of embodied agents
lies in the representation and interpretation of the rich, continuous sensory
data obtained from our environment. The “what-where” framework in cognitive
science [1, 2] posits that our brain employs separate neural channels to
encode two primary categories of information [3]. Specifically, the “what”
pathway [4] identifies detailed attributes of objects, like their identity and
characteristics (e.g., color and shape). In contrast, the “where” pathway [5]
discerns spatial details, such as position, trajectory, and closeness. Modern
advancements in the domain of object-centric representations in artificial
intelligence are probing these concepts [6], with a growing emphasis on
simultaneously training both the “what” and “where” facets within specific
contexts.

The recent advancement of the Large Language Model (LLM) has attracted
increasing attention, with many initiatives leveraging human-like language
comprehension for robotics. It revolutionized the pipeline for embodied agents
and resolved the challenge in the previous literature where human instruction
can be hard to interpret by robots [7, 8]. However, these methodologies
primarily focus on augmenting the text with more detailed instructions, i.e.,
task planning [9, 8, 10]. The presumption is that by learning from the input
observation, the policy networks should comprehend the objects’ position
internally and store this information as the form of parameters, and
subsequently generate a corresponding action trajectory. While this is a
prevalent approach, it often demands a vast number of demonstrations for the
policy network to genuinely understand an object’s position and produce a
valid trajectory.

Our work is built upon the recent development of Multi-modal Large Language
Models (MLLM) to augment language instruction with object-centric information.
The novelty of our framework is how we inform the robot with visual
information via transferred language instruction that allows us to infer high-
level features around affordances and intents.

To start, we aim to develop a position-aware MLLM capable of representing the
desired objects’ locations. Our investigation delves into two kinds of
positions: absolute and relative. The absolute position signifies an object’s
exact location. Taking the user instruction “pick up the apple and place it on
the plate” as an example, we can directly extract the bounding boxes of both
the apple and the plate, then supplement the instruction with these bounding
boxes. This provides the policy network with a clear understanding of object
locations based on a first-person perspective. Furthermore, any distractors,
i.e., objects not pertinent to the action, are excluded, sharpening the policy
network’s focus on the task at hand. On the other hand, relative positions
place the robot at the world’s center, and define the direction of target
objects relative to the robot. Using the previous example, our system might
indicate that the “apple is to the left” and the “plate is to the right”. This
mirrors the human-centric approach to perceiving object positions. These
language augmentations can be perceived as visual cues presented in text,
allowing the policy network to derive positional knowledge from natural
language. This circumvents the challenges associated with learning about
objects directly from images.

Moreover, considering the computational intensity of MLLM calculations, we
have devised a method that repurposes MLLM features, marrying its vision-
language insights with policy networks to enhance robotic manipulation
performance. By caching the MLLM’s pre-computed embedding at the onset of the
inference phase, we enable policy networks to operate seamlessly and
efficiently, leveraging MLLM knowledge without incurring high computational
costs.

Figure 1: Two examples of object-centric instruction augmentation for
simulation and real robots, respectively. Given an initial instruction from
the left figure, we augment them by providing the object’s absolute position
and relative position to the robotics and obtain the action eventually.

We assessed our proposed Object-Centric Instruction augmentation (OCI) on
unseen simulated and real-world robotic manipulation settings. Our findings
suggest that OCI not only facilitates superior policy learning in contrast to
flat instructions, but also enhances the understanding of the pivotal
influence of an object’s position on the success rate of manipulation. Through
detailed ablation studies, we emphasize the importance of using pretrained
models in “where” dimensions as vital for achieving remarkable enhancements.

To summarize, our contributions are the follows:

  * •

We’ve established a pipeline that equips MLLM with position-aware knowledge,
enabling it to augment incoming instructions with object-centric information.

  * •

We introduce a Feature Reuse Mechanism that incorporates feature embedding
from MLLM into the policy networks.

  * •

Empirical evaluations and ablations on both simulated and real robots validate
the superiority of our framework over existing baselines.

##  II Related Work

Natural Language Instruction for Robotic Manipulation. The rise of large
foundation models, specifically the Large Language Model (LLM) [11, 12, 13,
14, 15], has opened a new era for embodied agents to understand instruction
and generalize to unseen domains. These works include task planning [16, 17],
navigation [18], code generation for actions [19], multi-robot collaboration
[20], human-robot interaction [21], etc,. Another line of work utilize vision-
language models for robotics [22]. Some of them use pre-trained VLM as
instruction encoder [23, 24, 25, 26, 27, 28, 29] or visual encoder [30, 31,
32, 15], and others for visual state representations [33], object navigation
[34, 35], high-level planning [22, 9, 36], or providing supervision or success
detection [37, 10, 38, 39], and open-vocabulary object localization [35]. RT-2
[40] develop an end-to-end framework that outputs actions with images and
instructions directly.

Unlike prior applications of foundational models to downstream tasks like
planning and navigation, our goal is to enhance language instructions to boost
the generalizability of robotic manipulation, emphasizing positional cues for
objects in text structure.

Object-Centric Representation Learning. The fields of robotics and vision have
delved deeply into the use of object-centric representations, recognizing
their value in facilitating modular reasoning about visual scenes. Within the
robotics domain, it is common to use poses [41, 42] and bounding boxes [43,
44] to represent objects presented in a scene. However, these methods are
typically limited to predefined object categories or specific instances. The
development of the vision foundation model, i.e., SAM [45], motivates the
researcher to extend the object-centric learning [46, 47] to unseen objects.
Some works leverage unsupervised learning [48, 49] approaches to endow the
manipulation policies with object awareness [50, 51], but their works are
limited to simulation. Unlike prior works, we present a novel approach that
translates object positions into natural language descriptions, enhancing
visual representations for more effective policy learning in manipulation
tasks. This method aligns with recent trends where LLM are integrated with
vision systems [52, 53, 54], resulting in enriched scene comprehension through
a multi-modal approach.

Figure 2: An overview of the training process in OCI. We first fine-tune a
MLLM with general detection datasets and our collected datasets. The fine-
tuned MLLM enables automatically augmenting instruction object-centric
information. Subsequently, this is integrated with a policy network to develop
a model capable of generating specific actions.

##  III Methodology

This section discusses how to chain what and where into a unified and useful
instruction for manipulation policy learning. There are two key components to
our OCI framework: 1) a fine-tuned MLLM that is adept at comprehending
language and the environment, with the ability to correlate an object’s
location to its identity, and 2) a feature reuse mechanism that leverages the
features embedding from MLLM to improve policy learning. The following section
will address these two challenges in detail.

###  III-A Position-Aware Multi-modal Large Language Models

Finetune Position-Aware MLLM. We use the pretrained weights [52] that were
trained on a combined dataset of Conceptual Caption, SBU [55], and LAION [56].
The entire pre-training stage undergoes 20,000 training steps with a batch
size of 256, covering approximately 5 million image-text pairs. To enhance its
visual comprehension of text queries, we fine-tuned the pre-trained models
using the annotated LLaVA-Instruct-150K dataset [53]. While the VLM currently
demonstrates competent vision-language integration at the image level, this is
inadequate for our purposes. We aim for scene comprehension that transcends
mere image-level analysis, emphasizing object-centric representation from a
holistic standpoint.

To facilitate the MLLM with object-level understanding and comprehend objects
in terms of location, we use referring detection datasets such as RefCOCO,
RefCOCO+, and RefCOCOg [57] to fine-tune the model. Notice that the RefCOCO
datasets contain object’s bounding boxes, accompanied by questions and
answers. These datasets identify bounding boxes of mentioned objects within
given instructions. This closely aligns with our use-case, which requires
understanding the absolute position of objects.

Now, the MLLM can return the bounding boxes of referred objects but still
cannot re-write the given instruction. We further make an instructing tuning
dataset that allows the model to know how to rewrite the incoming command into
our desired format, which preserves the instruction, adds bounding boxes to
the target objects, and provides the relative position of the objects. In
particular, we formulate the format of prompt, input, and output. We use a set
of data that contains these three components, along with the image for the
current scene, to fine-tune the MLLM. We collected 200 number of high-quality
data for this fine-tuning step. Each image is collected with a random setup on
the table. The initial input language is manually set, and we use the
GPT-3.5-turbo [58] to enrich the instruction format. We use $o_{1}$ and
$o_{2}$ to represent two objects for simplicity. In practice, we use a
concrete object’s name. We ask the GPT to “Use three different expressions to
rewrite the instruction of ’pick up $o_{1}$ and place to $o_{2}$’.”, it
returns “Grab $o_{1}$ and set it on $o_{2}$.”, “Take $o_{1}$ and position it
on $o_{2}$.”, and “Lift $o_{1}$ and put it onto $o_{2}$”. In our experiment,
each instruction is augmented with ten more different expressions, which are
then used for the training.

Representation of Position. We represent the absolute position of objects
using bounding boxes. These positions are articulated with numerical values,
expressed in a manner that’s both intuitive and aligned with natural language
conventions. A bounding box is characterized by the format
$[x_{\min},y_{\min},x_{\max},y_{\max}]$. Here, both $x$ and $y$ are normalized
in relation to the image’s dimensions. By default, coordinates are presented
with a precision of three decimal places. Such coordinates can appear at any
point within the model’s input or output sequence.

While bounding boxes define the absolute position of objects, a description of
their relative positioning remains essential. To articulate the relative
position of an object, a reference point is necessary. In the context of
robotic manipulation, understanding movement in relation to the robot’s own
position is pivotal. As such, we designate the robot as the central reference
in the image and detail objects’ positions relative to it. For this purpose,
we provide eight directional options: (a) Left, (b) Right, (c) Top, (d)
Bottom, (e) Upper-left, (f) Upper-right, (g) Bottom-left, (h) Bottom-right.

Overall, when providing linguistic instructions to the robot, our pipeline
facilitates object-centric commands. These commands guide the policy network
with specific positional expressions. For instance, as shown in Figure 1, when
the user instructs the robot to “pick up the polar bear and place it in the
blue box”, the following instructions with coordinates are given: “Pick up the
polar bear [0.396, 0.682, 0.516, 0.786] to the blue box [0.641, 0.302, 1,
0.646]. The white polar bear is on the bottom of the robotic arm [0.052, 0.0,
0.552, 0.342]. The blue box is on the right of the robotic arm [0.052, 0.0,
0.552, 0.342].” While preserving the original language of the user’s request,
our system provides bounding boxes for identified objects in the scene.
Additionally, with the robot positioned at the center of the image, we detail
the relative positions of the referred objects, highlighting that the polar
bear is below the robot and the blue box is to its right. The overview can be
found at the top of Figure 2.

Architecture. We have selected the pre-trained ViT-L/14 [59] from CLIP [12] as
our visual encoder and adopted LLaMA-2-7B [60, 61] as our LLM. To ensure modal
alignment and provide the appropriate input dimension for the LLM, a fully
connected layer is implemented to transform the ViT’s $16\times 16$ output
embedding $V\in\mathbb{R}^{16\times 16\times 1024}$ to
$V^{{}^{\prime}}\in\mathbb{R}^{256\times 4096}$. We tap into the robust
vision-language capabilities inherent to the text-image alignment [52].
Additionally, we fine-tune the associated networks while keeping the language
and visual embeddings static, with only the alignment layers being adjustable.

Figure 3: This figure illustrates how we connect the policy network with MLLM.
During the fine-tuning phase, MLLM is only utilized at the initial time step
$t_{1}$, and its parameters are frozen.

###  III-B Feature Reuse Mechanism

This section gives a detailed description of how we reuse the features from
MLLM to improve the generalization of manipulation policy learning.

Policy networks. To formulate an efficient multi-task robot policy, we employ
policy networks designed with a multi-task decoder architecture. Concretely,
our objective is to learn a robot policy represented by $\pi(a_{t}|P,H)$,
where $H:=\\{o_{1},a_{1},o_{2},a_{2},\cdots,o_{t}\\}$ captures the historical
trajectory of past interactions. Within this framework, the
$o_{t}\in\mathbb{O}$ and $a_{t}\in\mathbb{A}$ respectively represent
observations and actions taken at each interaction step. These policy networks
ingest multi-modal tokens. For encoding, we deploy multi-modal prompts: the
image undergoes processing via a vision backbone and is subsequently combined
with the tokenized, augmented instruction using straightforward concatenation.
The next step for the policy networks is to delineate the action space. Our
policy network consists of three MLP layers with ReLU non-linear activation.

Figure 4: The experimental results on Franka Kitchen. On all sub-tasks, our
proposed OCI beats existing approaches, where our methods lead for a large
margin on some tasks. Figure 5: The example of Franka Kitchen for five tasks
on two camera views.

Feature Reuse Method. In our setup, we primarily retrieve the augmented
instruction at the first frame and then abandon the model. Yet, discarding the
computations made within the MLLM once a new instruction is acquired is
inefficient. This is because the MLLM provides not just vision-language
comprehension of the original instruction but also intuitively recognizes an
object’s identity and position. Finding a method to harness this wealth of
information is both challenging and rewarding.

To implement this idea, we architected a feature reuse mechanism. In specific,
we denote the feature embedding by the final layers of the MLLM as
$E_{mllm}\in\mathbb{R}^{D}$ and define the multi-modal token (fusion of image
tokens and text tokens) as $M\in\mathbb{R}^{D^{\prime}}$, where $D^{\prime}$
and $D$ are its corresponding dimensions. Herein, we add a sequence of
operations with a LayerNorm-MLP (LN-MLP) [62]
($\mathbb{R}^{D}\rightarrow\mathbb{R}^{D^{\prime}}$), such that
$E_{mllm}^{{}^{\prime}}=\text{LN-MLP}(E_{mllm}\in\mathbb{R}^{D^{\prime}})$.
The LN-MLP introduces nonlinearity and allows more flexible transformations
for the $E_{mllm}$. Here, the $E_{mllm}^{{}^{\prime}}$ carried prior knowledge
on vision-language understanding. Therefore, it is paramount to align this
prior vision-language information with the current instruction-observation
pair. We use cross-attention to serve as a bridge to connect these two types
of multi-modal embedding.

Particularly, $M$ is projected into a query (Q) and key (K), and
$E_{mllm}^{{}^{\prime}}$ is projected into value (V). The keys $K$ and values
$V$ are down-sampled to different sizes for different heads indexed by $i$.
Thus, we formulate our multi-scale cross-attention (MSC) as
$Q_{i}=E_{mllm}^{{}^{\prime}}W_{i}^{Q}$,
$K_{i}=MSC(M,r_{i})W_{i}^{K},V_{i}=MSC(M,r_{i})W_{i}^{V}$, and
$V_{i}=V_{i}+P(V_{i})$. The $MSC(\cdot,r_{i})$ is an MLP layer for aggregation
in the $i^{\text{th}}$ head with the down-sampling rate of $r_{i}$, and
$P(\cdot)$ is a depth-wise convolutional layer for projection. Compared with
the standard cross-attention, more fine-grained and low-level details that are
beneficial to manipulation tasks are preserved. Finally, we calculated the
attention tensor by:

$h_{i}=Softmax(\frac{Q_{i}K^{T}_{i}}{\sqrt{d_{h}}}V_{i})$ (1)

where $d_{h}$ is the dimension. Intuitively, the instruction-observation
queries the useful knowledge in vision-language knowledge embedding and passes
it into the policy network to improve the successful rate on unseen domains.

Note that the MLLM is frozen during the policy learning. At test time, we
extract both the augmented instruction and feature embedding at the task’s
initial frame. These instructions and feature embedding are then stored for
subsequent frame inferences, allowing the MLLM to be purged from the
processor’s memory. This optimizes computational speed by freeing up
resources.

Overview of the Framework. In our context, the text includes not only natural
language but also regions of interest that are represented by a set of
floating points. Inputting regions of interest into the model includes various
approaches, such as direct concatenation with cropped image patches [63, 64],
encoder-decoder structure for bounding boxes representation [65, 66, 67, 68],
and utilization of Gaussia map [69, 70, 71]. In our experiment, we find it is
sufficient to directly incorporate bounding boxes as a natural language
without additional pre-processing. Initially, a pre-trained image encoder
processes each image. This is then tokenized, similar to RT-1 [26]. The
language undergoes tokenization first, after which a T5-small model extracts
features. It is then concatenated with the image token. A policy network is
followed up to generate the action space. A comprehensive overview of this
framework can be found in Figure 3.

##  IV Experiments

Our experiments aim to answer the following questions: 1) Does our method
enable better policy learning than using naive language instruction? 2) Is our
method effective in real-world environments? We initiate our discussion by
outlining the simulation environments tailored to address these queries.
Subsequently, we present in-depth experimental results that positively affirm
answers to both questions.

TABLE I: Ablation on two types of augmented instruction. Experiments are conducted on Franka Kitchen. “w/o” indicates “without”. We report the average success rate over five tasks and two camera views per task. Model | 10 Demos | 25 Demos
---|---|---
OCI (Ours) | 61.7 | 69.4
w/o absolute position | 54.2 | 63.5
w/o relative position | 49.6 | 57.3
TABLE II: Ablation on reusable feature mechanism. Experiments are conducted on Franka Kitchen. “w/o” indicates “without”. We report the average success rate over five tasks and two camera views per task. Model | 10 Demos | 25 Demos
---|---|---
OCI (Ours) | 61.7 | 69.4
w/o reusable features | 53.9 | 62.3

###  IV-A Simulation Experiments

Experiments Setup. 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. The example of each task is present
in Figure 5. For Franka Kitchen tasks, the length of a demonstration is 50,
which contains 50 state-action pairs. For MLLM, we fixed the weights when
training the policy networks. There are two views in the Franka Kitchen
simulator; each is conducted three times, and the average success rate over
both views is reported. All Franka tasks include proprioceptive data of the
arm joint and gripper positions. The horizon for all Franka tasks is 50 steps,
and our imitation experiments use either 10 or 25 demos.

Baselines. We compare our model with R3M [72], which is the state-of-the-art
method and widely applicable method in Franka Kitchen. We also compare with
BLIP-2 [63], a SOTA vision-language model. We replace our MLLM with BLIP-2 in
our method and retain the FRM. 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 25 demonstrations, and the other
utilizes ten demonstrations. We report the success rate in five tasks per
benchmark and two different camera views for each set respectively. Each
experiment is run five times, and we report the average performance.

Main Experimental Results. We demonstrate the experimental results in Figure
4. It is obvious that for all tasks on two different camera views, our
approach achieves superior performance over both R3M and BLIP-2. On some
difficult tasks, such as opening the cabinet, opening the microwave, and
turning the stovetop knob, the performance gap between our proposed OCI is
even larger compared to R3M and BILP-2. Compared to BILP-2, which also uses a
large-scale pre-trained vision-language model to align the instruction and
observation, then map to the policy network, our OCI achieves stronger.
performance over five tasks, showing the effectiveness of the object-centric
instruction augmentation.

Figure 6: Left: The setup of our Franka real robot. Right: The example of some
tasks that we collected. Figure 7: Results from real-world experiments
indicate that both relative and absolute positions are crucial for the
successful execution of manipulation tasks.

###  IV-B Ablation Study

How Important for Absolute and Relative Position in Instruction? We evaluate
the utility of instruction augmentation and present our findings in Table I.
Our experiments indicate that both relative and absolute positions are
critical, confirming the efficacy of our framework. We conduct another
ablation study is conducted on real-world experiments.

The Effectiveness of Feature Reuse Mechanism. We explore the efficacy of our
proposed FRM. The experimental results are detailed in Table II. Observing the
data, we find that omitting the FRM results in an average drop of 7.1% in the
success rate, highlighting the importance of FRM.

###  IV-C Real-world Experiments

Given our encouraging simulation experiments, it is natural to ask whether our
algorithm can work on real-world robotic manipulation tasks, which present the
additional challenges of noisy image observations from imperfect camera
sensors and increased object quantity and diversity.

We use the Franka robot with a 7-DOF arm, which is equipped with a parallel
jaw gripper (see Figure 6, left). Our workspace uses two D435i RealSense RGBD
cameras. We only use the RGB information in our experiments. One egocentric
camera is attached to the robot’s hand, and one exocentric camera is
positioned at the robot’s front.

Experiment Setup. To realize the stated challenges above, we design a real-
world environment (referred to as RealRobot), in which a Franka robot is
tasked with 1) picking up a cube to either left or right box, 2) picking up a
cube based on the color, 3) open the top cover of a box, 4) pick up a toy and
place to the box, 5) pick up a cube based on its position (left, middle, or
right). We provide a sample of the data we gathered from real-world
trajectories in Figure 6 (right). The tasks are numerically represented for
simplicity; for example, Task 1 corresponds to “picking up a cube and placing
it in either the left or right box”.

Experimental Results. An evaluation of our approach was conducted using a real
robot setup, with each task repeated in ten trials. Additionally, we conducted
an ablation study, which is presented in Figure 7. In this study, we
progressively removed the relative position (denoted as “w/o rel-pos”) and the
absolute position (denoted as “w/o abs-pos”). Across all five tasks, our OCI
consistently outperformed the baselines, achieving the highest success rates.
Particularly notable were the results for Task 1 and Task 5. In these tasks,
the robot needed to discern direction, such as picking up the cube from the
left, right, or middle side or placing the cube on the left or right box.
Plain language instructions struggled with Task 5, failing entirely, and
achieved a mere 30% success rate for Task 1. However, introducing absolute
position information boosted success rates by 40% and 20% for the two tasks,
respectively. Incorporating relative position data further enhanced
performance by 20% and 30%, underscoring the effectiveness of our OCI.

##  V Conclusion

This work contributes a novel perspective on language instruction for robotics
manipulation. Motivated by the concept of visual understanding in human
intelligence, we augment language instruction by adding an object’s absolute
and relative position into the text format. Such augmented language alleviates
the burden of the visual encoder, which was previously responsible for
localizing objects on its own. We conduct experiments on both simulation and
real-world scenarios and show the superior performance of our methods over
conventional language instruction. We believe our approach presents a fresh
perspective on the kinds of instructions best suited for versatile robotic
manipulation.

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[a]Abhi Modak

# Measurements of inclusive photons at forward rapidities in p–Pb collisions
at $\sqrt{s\rm_{NN}}$ = 5.02 TeV with ALICE

for the ALICE Collaboration

###### Abstract

We present multiplicity and pseudorapidity distributions of inclusive photons
at forward rapidity in proton-lead (p–Pb) collisions at $\sqrt{s\rm_{NN}}$ =
5.02 TeV using the data obtained from Photon Multiplicity Detector (PMD) of
ALICE. The centrality dependence of pseudorapidity distributions of inclusive
photons is also studied. Results are compared with the previous ALICE
measurements of charged-particle production and with theoretical predictions
from Monte Carlo models, DPMJET and HIJING.

##  1 Introduction

The production of particles in high energy collisions achieved at the Large
Hadron Collider (LHC) is dominated by soft-QCD interactions. Soft particle
production ($p\rm^{particle}\rm_{T}$ $\lesssim$ 1-2 GeV) is described by non-
perturbative QCD and challenges existing phenomenological models. Global
observables such as multiplicity and rapidity dependence of particle
production are some of the most fundamental measurements for improving and
constraining these models. Measurements of these observables in p–Pb
collisions provide an important baseline to understand lead-lead (Pb–Pb)
results by disentangling cold nuclear matter effects from hot nuclear matter
effects [1]. The study of inclusive photon multiplicity aims at providing
complementary measurements to those of charged particles since the photon
production is dominated by the decay of neutral mesons [2].

In this article, the measurements of multiplicity and pseudorapidity
distributions of inclusive photons in p–Pb collisions at $\sqrt{s\rm_{NN}}$ =
5.02 TeV are reported. The pseudorapidity distribution
(d$N\rm_{\gamma}$/d$\eta\rm_{lab}$) is also measured for different centrality
classes from 0–5% (most central) to 80–100% (most peripheral). Results are
compared with the predictions from DPMJET [5] and HIJING [6] and with the
previous measurements of charged-particle production by ALICE [4, 3].

##  2 Data sample, analysis method, and systematic uncertainties

This analysis is performed using the ALICE data collected in 2013 by colliding
a proton beam of $E=4~{}\mathrm{TeV}$ (circulating in the negative z-direction
with respect to the ALICE laboratory system [7]) with a lead beam of
$E=1.58~{}\mathrm{TeV}$ per nucleon (circulating in the positive z-direction).
The detailed description and performance of the ALICE detector system are
discussed in Ref. [8]. ALICE has unique coverage to measure both charged
particles and photons at forward rapidity. Charged particles can be measured
over a wide pseudorapidity range ($\sim$ 8 units) of
$-3.4~{}\textless~{}\eta\rm_{lab}~{}\textless~{}5.0$ using the Forward
Multiplicity Detector (FMD) and the Silicon Pixel Detector (SPD) [9, 10]. On
the other hand, photon production can be studied over a kinematic range of
$2.3~{}\textless~{}\eta\rm_{lab}~{}\textless~{}3.9$ using the Photon
Multiplicity Detector (PMD) [2].

The analysis of charged-particle production using FMD and SPD was performed
and studied in detail in Ref. [3]. In this article, we focus on the new
measurements of inclusive photon production using a preshower technique with
the PMD. The PMD is a gaseous detector placed at a distance of 367 cm from the
interaction point and it consists of two finely granular planes with a lead
converter of thickness 3$X_{0}$ ($X_{0}$ = radiation length) sandwiched in
between them [11, 12].

In the present work, minimum bias (MB) events requiring a signal to be
detected in both arrays of forward scintillator detectors (V0A and V0C) [13]
are analysed. These events correspond to non-single diffractive (NSD) events.
Events with vertex position |z| $\textless$ 10 cm from the nominal interaction
point are selected. Various centrality classes are determined by measuring the
amplitude of the V0A detector [13, 14]. We apply suitable photon-hadron
discrimination thresholds based on the number of affected cells and the energy
deposition in a cluster produced in the preshower plane of the PMD to obtain
the photon rich sample, known as $\gamma$-like clusters [2].

For MB events, the Bayesian unfolding technique [15] is used to correct the
distributions of $N\rm_{\gamma-like}$ clusters which include several detector
effects (detection inefficiency, limited acceptance, finite resolution etc.)
and contamination from hadron clusters. In this method, all these effects are
taken into account by a response matrix ($R$) which is constructed in terms of
the true photon ($N\rm_{\gamma-true}$) multiplicity vs $N\rm_{\gamma-like}$
clusters (measured multiplicity) with the help of Monte Carlo (MC) simulation.
Each element ($R_{mt}$) of matrix $R$ corresponds to the probability that an
event with true multiplicity t is measured as an event with the measured
multiplicity m. A detailed description of the unfolding procedure can be found
in Refs. [2, 9, 16]. The correction of d$N\rm_{\gamma}$/d$\eta\rm_{lab}$ for
various centrality classes is performed using the Efficiency-Purity method as
described in Ref. [17].

The MB results are also corrected for trigger and vertex reconstruction
efficiencies in a similar way that was followed in Ref. [2]. The trigger and
vertex reconstruction efficiencies are estimated to be 98.47% and 99.8%
respectively using HIJING.

Systematic uncertainties from various sources (Material budget in front of
PMD, discrimination thresholds, unfolding methods, unfolding parameters) are
evaluated using the same technique as described in Ref. [2]. The total
systematic uncertainties are obtained by adding uncertainties from various
sources in quadrature and found to vary from 4.4 to 57% and from 7.37 to 7.4%
for multiplicity and pseudorapidity distributions respectively. The systematic
uncertainty on d$N\rm_{\gamma}$/d$\eta\rm_{lab}$ due to the centrality
classification amounts to 7.38% and 7.17% in centrality ranges (0–5%) and
(80–100%) respectively.

##  3 Results and discussion

Figure 2 (Top panel) shows the inclusive photon multiplicity distributions
measured for NSD events at forward rapidity (
$2.3~{}\textless~{}\eta\rm_{lab}~{}\textless~{}3.9$) in p–Pb collisions at
$\sqrt{s\rm_{NN}}$ = 5.02 TeV in comparison to the predictions from MC models
HIJING and DPMJET. The ratios between the data and MC results are shown in the
bottom panel of Figure 2. It is found that both models underpredict the data
at low multiplicities ($N\rm_{\gamma}$ $\textless$ 10) and agree with the same
in higher multiplicity bins within uncertainties.

Figure 2 describes the pseudorapidity distribution of inclusive photons
(filled blue circles) as a function of $\eta\rm_{lab}$ within
$2.3~{}\textless~{}\eta\rm_{lab}~{}\textless~{}3.9$ together with previous
ALICE results of charged-particle production at midrapidity [4] (filled
magenta circles). Results are compared with the predictions from HIJING and
DPMJET. It is observed that d$N\rm_{ch}$/d$\eta\rm_{lab}$ is well described by
both MC models whereas d$N\rm_{\gamma}$/d$\eta\rm_{lab}$ is slightly
overestimated by DPMJET.

Centrality evolution of particle production in p–Pb collisions at
$\sqrt{s\rm_{NN}}$ = 5.02 TeV is presented in Figure 3. It is seen that
inclusive photon (mostly produced from the decay of neutral mesons [2]) (solid
circles) and charged-particle production (open circles) [3] have a similar
dependence on centrality. A clear asymmetric shape of
d$N\rm_{ch}$/d$\eta\rm_{lab}$ is observed for the events with centrality
classes from 0–5% to 20–40%, with an increasing number of particles produced
in the Pb-going direction compared to that in the proton beam direction.
Figure 3 shows the comparison of the obtained results with the predictions
from DPMJET (Left) and HIJING (Right). Both models describe the data for
peripheral events (80%–100%) however underpredict the same for central events.

Figure 1: Top panel: Inclusive photon multiplicity distribution for NSD events
at forward rapidity, $2.3~{}\textless~{}\eta\rm_{lab}~{}\textless~{}3.9$, in
p–Pb collisions at $\sqrt{s\rm_{NN}}$ = 5.02 TeV. The lines show the
predictions from HIJING (Red) and DPMJET (Green). Bottom panel: The ratios
between the data and MC results.

Figure 2: Pseudorapidity distribution of inclusive photons (filled blue
circles) for NSD events at forward rapidity,
$2.3~{}\textless~{}\eta\rm_{lab}~{}\textless~{}3.9$, in p–Pb collisions at
$\sqrt{s\rm_{NN}}$ = 5.02 TeV and a comparison with previous ALICE
measurements of charged-particle production (filled magenta circles) at
midrapidity [4]. Predictions from HIJING (Red line) and DPMJET (Green line)
are superimposed.

Figure 3: Pseudorapidity distribution of inclusive photons (solid circles) for
various centrality classes at forward rapidity,
$2.3~{}\textless~{}\eta\rm_{lab}~{}\textless~{}3.9$, in p–Pb collisions at
$\sqrt{s\rm_{NN}}$ = 5.02 TeV and a comparison with previous ALICE
measurements of charged-particle production (open circles) [3]. Predictions
from DPMJET (Left plot) and HIJING (Right plot) are superimposed.

##  4 Summary

We have presented multiplicity and pseudorapidity distributions of inclusive
photons for NSD events in p–Pb collisions at $\sqrt{s\rm_{NN}}$ = 5.02 TeV at
forward rapidity ($2.3~{}\textless~{}\eta\rm_{lab}~{}\textless~{}3.9$) using
the data obtained from PMD of ALICE. Results are compared with two theoretical
MC predictions, DPMJET and HIJING. We observed that both models underpredict
the multiplicity distribution for $N\rm_{\gamma}$ $\textless$ 10 and describe
the same in higher multiplicity regions within uncertainties. Pseudorapidty
distribution of photons is found to be explained by HIJING within
uncertainties but slightly overestimated by DPMJET towards midrapidity.

We have also presented photon production for various centrality classes in
comparison with previous measurements of charged-particle production by the
ALICE [3]. A similar dependence of both photon and charged-particle production
on centrality classes is observed. Both HIJING and DPMJET are unable to
explain the data for all centrality classes except for the most peripheral
class (80%–100%). These results will help to constrain model parameters to
understand the photon and charged-particle productions in p–Pb collisions.

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