LeRobot:  An Open-Source Library for End-to-End Robot Learning
Authors: Remi Cadene
Simon Alibert
Francesco Capuano
Michel Aractingi
Adil Zouitine
Pepijn Kooijmans
Jade Choghari
Martino Russi
Caroline Pascal
Steven Palma
Dana Aubakirova
Mustafa Shukor
Jess Moss
Alexander Soare
Quentin Lhoest
Quentin Gallouédec
Thomas Wolf
Venue: ICLR 2026 Poster (2026)
Keywords: robot learning
open source
robotics
Primary area: infrastructure, software libraries, hardware, systems, etc.
TL;DR: An open-source, scalabe and accessible library for end-to-end robot learning with real-world robots support

Abstract: Robotics is undergoing a significant transformation powered by advances in high-level control techniques based on machine learning, giving rise to the field of robot learning.
Recent progress in robot learning has been accelerated by the increasing availability of affordable teleoperation systems, large-scale openly available datasets, and scalable learning-based methods.
However, development in the field of robot learning is often slowed by fragmented, closed-source tools designed to only address specific sub-components within the robotics stack.
In this paper, we present lerobot, an open-source library that integrates across the entire robotics stack, from low-level middleware communication for motor controls to large-scale dataset collection, storage and streaming.
The library is designed with a strong focus on real-world robotics, supporting accessible hardware platforms while remaining extensible to new embodiments.
It also supports efficient implementations for various state-of-the-art robot learning algorithms from multiple prominent paradigms, as well as a generalized asynchronous inference stack.
Unlike traditional pipelines which heavily rely on hand-crafted techniques, lerobot emphasizes scalable learning approaches that improve directly with more data and compute.
Designed for accessibility, scalability, and openness, lerobot lowers the barrier to entry for researchers and practitioners to robotics while providing a platform for reproducible, state-of-the-art robot learning.

Published as a conference paper at ICLR 2026
LEROBOT: AN OPEN-SOURCE LIBRARY FOR END - TO- END ROBOT LEARNING
Remi Cadene" Hugging Face
Simon Aliberts
Francesco Capuano" University of Oxford
Michel Aractingi" Hugging Face
Hugging Face
Adil Zouitine" Hugging Face
Pepijn Kooijmans'
Jade Choghari Hugging Face
Martino Russi"
Hugging Face
Hugging Face
Caroline Pascal"
Steven Palma'
Mustafa Shukor
Jess Moss' Hugging Face
Alexander Soare"
Hugging Face
Hugging Face
Hugging Face
Hugging Face
Dana Aubakirova" * Hugging Face
Quentin Lhoest
Quentin Gallouédec
Thomas Wolf
Hugging Face
Hugging Face
Hugging Face
ABSTRACT
Robotics is undergoing significant transformation powered by advances in high- level control techniques based on machine learning, giving rise to the field of robot learning. Recent progress in robot learning has been accelerated by the increas- ing availability of affordable teleoperation systems, large-scale openly available datasets, and scalable learning-based methods. However, development in the field of robot learning is often slowed by fragmented, closed-source tools designed only address specific sub-components within the robotics stack. In this pa- per, present e oboo,, an open-source library that integrates across the entire robot learning stack, from low-level middleware communication for motor con- trols to large-scale dataset collection, storage and streaming. The library is de- signed with strong focus on real-world roboties, supporting accessible hardware platforms while remaining extensible to new embodiments. It also supports effi- cient implementations for various state-of-the-an robot learning algorithms from multiple prominent paradigms, as well as generalized asynchronous inference stack. Unlike traditional pipelines which heavily rely on hand-crafted techniques, lerobot emphasizes scalable learning approaches that improve directly with more data and compute. Designed for accessibility, scalability, and openness, 1 lerobot lowers the barrier entry for researchers and practitioners robotics while providing a platform for reproducible, state-of-the-art robot learning.
INTRODUCTION
Early successes in robotics relied the precise description of robot-environment interactions, typi- cally consisting in analytical descriptions of rigid-body kinematics, contact modeling, and planning under uncertainty (explicil models). While effective in controlled settings, deriving accurate models for diverse deployment scenarios is difficult and error-prone, often requiring substantial expert effort and thus has limited scalability. Recent advances in Machine Learning (ML) have catalyzed a shift toward tackling robotics problems with implicit models, typically learned rather than formulated.
key advantage of learning- -bsed methods (implicit models) is their scalability: performance em- pirically improves with larger datasets, and more compute. In turn, the shift from explicit to implicit models promises to address many the challenges holding robotics back: rather than hand-tuning the different components of typical robotics pipeline, robot learning algorithms learn monolithic
Cooe team. Work done while Hugging Face.

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Published as a conference paper at ICLR 2026
Shared middleware
Dataset handling
SOTA Policies
HopeJfeeam
Read
Teltobot nxmt.
openty
Pre trained SOTA palicies for real world roboties in Pytorch
To HIL-SERL
LeRobot
and many more!
HopoJR
Optimized training recips
Easily
Ready-to-use scripts
extensible
ALOHA
Write
Asymi Interenoe engine
Essily customizaible
Real-world robots
Inference optimization
Experiments tracking
lerobot is an open-source library for end-to-end robot learning. It covers the entire stack, from middleware motor interfaces large-scale data collection and dataset streaming, sup- porting an optimized inference stack, scalable implementations of SOTA robot learning algorithms, and providing support for training custom models well as easily reusing pre-trained ones.
Figure 1:
control policies end-to-end, adapting to different input modalities and typically improve with in- creasing quantities of data, echoing broader trends in vision, language, and multimodal learning.
Despite this momentum, the robot learning ecosystem is fragmented as (1) high-to-low level control interfaces (middleware) are often tailored to specific robots and difficult to adapt, and (2) datasets lack common formats and tooling, resulting in robot and task-specific contributions that are difficult reproduce and use in practice. lerobot is an open-source library providing unified, end-to-end stack for robot learning, and it is vertically integrated across the stack featuring:
Unified robot integration. A consistent, Python-based middleware API for real-world motor control across diverse platforms, bridging typical ML frameworks and real-world robotics across a variety of robots, ranging from low-end manipulators to humanoid arms and hands.
Standardized datasets. An efficient, multimodal format for recording, storing, and streaming high frame-rate sensory and image data via Databet,, a custom dataset format built for scale. With seamless integration into the open-source ecosystem, LeRobot encourages openness and research reproducibility.
Optimized inference. An optimized inference stack that decouples action planning from control execution both (1) physically and (2) logically, enabling policies to (1) run sep- arate machines with increased computational resources compared to those onboard robots, and (2) in parallel with low-level control loops, for robust deployment and dynamic adapt- ability at runtime.
Efficient, reusable algorithms. Clean, Py PyTorch-bar implementations of state-of the-ahr art (SOTA) robot learning methods, optimized for (1) training custom models from scratch and (2) using openly-availan pre-trained models.
Together, these components address fragmentation issues in the field, reducing the barrier entry for robotics by providing vertical integration across the entire robot learning stack, with a clear emphasis on accessibility and scalability, aiming at accelerating progress in the field.
2 BACKGROUND
2.1 EXPLICIT AND IMPLICIT MODELS
Autonomous motion leverages either explicit implicit models (Bekris et al. 2024). Classical robotics historically uses explicit models, implemented as modular pipelines for perception, plan- ning, and control (Siciliano & Khatib 2016). This approach suffers from compounding errors, poor scalability to diverse deployment scenarios, and undermodeling issues due to simplified analytical models of physical interactions, limiting its effectiveness to unstructured, dynamic environments (e.g., house versus a factory line). In contrast, robot learning relies on implicit models to develop monolithie, data-driven policies directly mapping observations to action. Robot learning also priori-
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Published as a conference paper at ICLR 2026
Classical Robotics
Robot
Friction
Dlale Esiiration
SYTO PM
Planingg
Contol
Ang. velocity Undermodeling issues
Integration challenges
Multimodality challenges
Robot Learning
Robot
learned pipaline
Mo Billtti Policy
Testuree
Robot
Environment
Monolithic policies
Automated feature extraction
Interactions modeling
Figure 3: Classical robotics uses modular, model-based pipelines with hand-crafted features, while robot learning employs monolithic, data-driven policies that learn directly from interaction data.
(A);
Number Trajectories
LeRobol (B)
25
ALOHA/ALOHA-2
(1:5M) Open-X
Accessibiity
Dataset
SO-10X
1.0M
Franka Emika
Panda
(130K) RT1
Datagat
201.00
402-011
2025 5g
203509
2025077
2025-09
Cost
Figure 4: (A) Low-cost, open-source robots like SO-10X and ALOHA cost fraction of proprictary industrial arms, using consumer-grade parts and 3D-printable designs. (B) Decentralized efforts to collect expert demonstrations in the form of trajectories surpassed centralized efforts for the collec- tion of large amounts of real-world robotics data.
tizes interaction data over rigid assumptions, and replaces hand-engineered components with learned representations, offering a more robust and adaptable solution for unstructured environments.
Autonomooe Mation
The adaptability of these learned, implicit models stems directly from their scalability with data a primary advantage over classical ap- proaches. In this context, real-world robotics data is often collected in form of expert demonstrations via teleoperation, process where "cognitive decisions are made by [a] human user, while the robot is responsible for their mechanical implementation" " (Siciliano Khatib 2016 Ch.43, $1). In recent years, teleoperation hardware has become in- creasingly affordable, making more and more relevant for robot learn- ing, either by teleoperating in virtual reality (VR) in the real world. Consumer-grade VR teleoperation headsets for robotics have been used collect robot data both on real-world and simulated robots (Bjorck et al. 2025), and low-cost teleoperated robotic arms (Zhao et al. 2023 Aldaco al. 2024 (u al. 2024: Knight et al. (2024) are increasingly empowering researchers and practitioners to collect real-world robotics multiplication of centralized (Brohan al.
Explicit Madillng
Implicit Modwing
Forward Kinemaiia
Re nforcement
Learring
Inverse Kinematics
Imitaion Learning
Figure Some of the explicit and implicit models for autonomous motion.
data. In turn, this results
2023 Collaboration et al., 2025 Khazatsky et al.) (025) and de-centralized (Section 3.2 efforts to collect robot data. Figure ssshows how fully accessible teleoperated platforms such as the SO- 100, SO-101 (jointly referred to S0-10X, (Knight al. 2024)) and ALOHA-2 (Aldaco et al. 2024) can cost down to a fraction closed-soune source, industrial -gadde robots such as the Franka Emika Panda Consequently, these low end robot platforms be used to collect large amounts of data in a decentralized effort powered by the very accessibility- low-cost open designs, Driprintable parts-of of these low-end robot platforms.
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Published as a conference paper at ICLR 2026
2.2 ROBOT LEARNING
Reinforcement Learning Reinforcement learning (RL) (Sutton & Barto 2018) has been ex- tensively applied to roboties (Kober al., 2013), for the inherently sequential nature of control problems and Deep RL's capability to learn strategies for return maximization max, (xx
max, Eysr T rr directly from highly-dimensional,, unstructured observations such as 2013). Off-policy, entropy-regularized methods such as Soft Actor Critic (Haarnoja et al. 2018) can be adapted to exploit teleoperation data and safely train in the real-world, thereby sidestepping concerns related to operative safety and simulation-inducet dis- crepancies. Reinforcement Learning with Prior Data (RIPD) (Ball et al., 2023) mixes offline and online buffers without pretraining to speed up convergence, and in conjuction with (1) learned re- ward classifiers overcoming the need to define brittle hand-crafted rewards (Luo et al. 2025) and (2) targeted human interventions during training, can yield near-perfect success rates in challenging manipulation tasks within 1-2 hours of real-world training (HIL-SERL, Luo et al. (2024).
images (Mnih et al.,
Imitation Learning Imitation Learning via Behavioral Cloning (BC) offers a pragmatic alterna- live real-world by learning control directly from human demonstrations, eliminating the need for reward design and reducing exploration risk by learning to reproduce the behavior of an expert demonstrator (Pomerleau 1988). Collected via teleoperation on increasingly affordable hardware, large corpora of robotics data also enable training at a scale across tasks and embodiments (Collab-) oration et al. 2025) Khazatsky et al. (025). BC relies on learning generative models of the joint (or conditional) distribution over sate-action pairs S x A + [0, 1J, p(a, s) (or p(a[s) (s) to learn from data distributions exhibiting multiple modes, such as teleoperation data (Florence al. 2022). Recent works in BC thus employ powerful generative models to learn the conditional distribution p(a)s) s), learning from multimodal demonstrations and produce coherent action sequences: Zhao et al.
(2023) leverages (conditional) Variational Auto-Ein auto-Encoders (Kingma & Welling 2022 Sohn et al.
2013). Chi et al. (2024) relies on Diffusion Models (Ho et (0220) whereas Black et al. (2024);
Shukor et al. (2025) both rely on Flow Matching (Lipman al. 2023). Inspired by successes in developing foundation models for vision (Dosovitskiy et al. 2020) and language (OpenAl 2024). BC is also increasingly being used in efforts aiming to develop rabot foundation models (Jang al.,
2022 Brohan et al. 2023 Black et al. 2024), scaling up both data and compute used to learn visuo- motor policies suitable for real-world deployment across tasks and even robot embodiments.
Robot learning algorithms are often implemented as standalone components and their integration with the rest of the robotics stack remains challenging.
2.3 PRACTICAL CHALLENGES FOR ROBOT LEARNING RESEARCH
Despite scientific advances, the robot learning ecosystem remains fragmented, impeding repro- ducibility and raising the barrier to entry for research.
Disaggregated Middleware: While middleware abstractions are available, it is common to encounter middleware components tailored to specific platforms in practice. This het- erogeneity often forces teams to develop bespoke adaptations, siloing efforts.
Datasets and Formats: Large-scale datasets are typically shared in a different formats. Data is often released in varied formats like TensorFlow Datasets, ROS bags, bespoke JSON layouts. The absence of a universal, modality-rich schema prevents the seamless aggregation of disparate datasets into larger mixtures.
The deep learning literature has consistently demonstrated that minor implementation differences in algorithms, data handling, and evaluation pipelines can lead to significant variance in results (Henderson et al. (0118). In robotics, these issues are compounded by hardware variability, further hindering reproducibility.
Learning Frameworks:
This ecosystem-wide fragmentation imposes significant incidental complexity researchers, di- verting resources from core scientific inquiry to systems integration. derobotaddress these limitations by providing end-to-end, open, and scalable library designed to unify hardware in- terfacing, collecting and streaming data, and training and deploying advanced policies with minimal engineering overhead.
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Published as a conference paper at ICLR 2026
Robot
Downloads
Datasets
Episodes
Robot Type
Cost (E)
Panda
1'878"39 395
588
926'776
S0-100/101
Manipulator (Bimanual)
225 (550)
XArm
1'107'329 329
450'329
Koch-v1.1
Manipulator (Bimanual)
670 (1346)
WidowX
832'177
100
114
ALOIIA
Bimanual Manipulator
21k
KUKA
662'
419784
Hom Humanoid Arm and hand ~ 500
SO-101
319'586
3'965
58'299
LeKiwi
Mobile Manipulator
230
SO-100
278'697
5'161
78'510
(a) Cost for all robot platforms supported by
Koch-v1.1
43'
849
20'959
lerobot and with an openly-available Bill Of Mate- rials (BOM).
(b) All Top-4 robots for number of downloads, datasets and episodes openly shared, listed decreasing order by the total number of downloads.
3 FEATURES
1erobot is designed for accessibility, scalability, and reproducibility in robot learning. The library natively integrates (1) entirely open-source hardware platforms costing fraction closed-souree devices, (2) unified middleware shared across low-level robot interfaces (3) data collection, storage and streaming tools, (4) an optimized inference engine and (5) many ready-to-use implementations of SOTA methods in robot learning, useful to both train models from scratch and re-use openly available pre-trained models. lerobot is entirely open-source, and highly accessible due to its reliance on low-cost teleoperation kits, focus on empowering large-scale datasets via streaming, and simple interface to adopt models in fully reproducible pipelines.
3.1 ACCESSIBLE REAL-WORLL ROBOTS
lerobot currently supports multiple real-world robot platforms for both static and mobile manipu- lation. The library fully integrates the SO-100 and SO-101 arms (Knight et al. (2024). both in single and bimanual setup. The library also supports the Koch-v1.1 (Moss 2025) and ALOHA-2 (Aldaco et al. 2024) manipulators, the Hope-JR humanoid arm (TheRobotStudio 2025), , the Stretch-3 (Hello Robot (025) and LeKiwi (SIGRobotics- UIUC 2025) mobile manipulation platforms, and lastly the Reachy-2 humanoid (Mick al., 2019). lerobot is designed interface multiple open devices with shared middleware that can be used to (1) read the configuration a leader robot and write on follower robot for teleoperation and (2) directly control the follower with learned policy.
Table Ta shows the cost for all the robot platforms currently supported by lerobot with openly- available Bill of Materials (BOM), reported for completeness in Appendix lerobot can support multiple robot platforms thanks to a shared middleware embedded in higher-level abstractions for the different robots supported, and engineered to interface directly with the low-level SDKs of major low-cost actuator producers (FeeTTech and Dynamixel). Crucially, the middleware is designed to be easily extensible and highly composable. We refer to Appendix B for an example of teleoperation using lerobot.
3.2 DATASETS
To address the fragmented nature of data in robotics research, introduce LeRobotDataset, lerobot's unified multimodal dataset schema. This standardized format is engineered to pro- vide convenient and standardized access roboties data spanning diverse modalities. including high-frequency sensorimotor readings, multiple camera feeds, and teleoperation status signals. The schema is designed be self-contained, accommodating general metadata such as textual descrip- tions of the demonstrated tasks for filtering and dunguage-condition embodiment considered, and relevant experimental parameters such as frames-per-sectt data capture and the types sensors used. As of September 2025, 16K+ datasets from 2.2K+ indi- vidual contributors are openly shared via the LeRobot Dataset format, featuring robots directly integrated in the library such the S0-10X arm and unsupported robots (Franka Emika Panda, xArm, R R1Pro), ported to the teRobotDatases format by the open-source community. We argue the support of different robot configuration underscores the flexibility of our dataset format, and that the coexistence of both large-scale academic benchmarks and small-scale data collection efforts exemplifies the breadth of use-cases that dataset format accomodate.
policies, specifics of the robot
(FPS) of
5

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Published as a conference paper at ICLR 2026
Downloads over Time by Robot Type (Top Other)
Share Downloads
by Robot Type per Month (Top 4 + Other)
Robot Type
Thand
965
29.0%
30 1.1%
13
Number of Downlowds
80
29.00
55.5%
52.55660 0%
kuka
1.066-
1.566
panda
xarm
widowx
663)
40
60
100
100 000
100.0%
100.0%
100 0%000
99 .%
28.9%
.%
90.
45 195 8%
68 0%
87 67.6%
92. 92.2%
92 %%0%
41.1%
$421527k 156
00
2024
2024
2026--4
10
2006-11
2024
2012--0 ai
20125-02
2025-03
2023 drd
2013 og
2025 d6
2003
2015-
2025-a0
2024-08
2024-09
2024-10
2024-11
2024-12
2025-01
2025.00
2025
0025.03
2025
2025-08
04
2025.08
2025.07
2025-08
2025-08
(a) Downloads over time by robot type
(b) Share of downloads by robot type
Datasets by Robot Type Other)
Share Dataset
Type
100
by Robot Type per Month (Top + Other)
Number of Dataeees
0.0e6
Other
Trend
80
41.9%
56.4%
9%.
58.1%
61. 11.3%
42.7%
32 5%
42.7%
35 8%.8%
0.0c6
0.0e6
so100
panda
D69
60
100 0%
100 0%
100 0%
95. 35.5%
9%
31.4%
0.0e6
942
310 208
41 41.6%
56.8%
45.0 0%
572
951
703
20
30 4%
30 3%
6%
25 9%
34.8%
33 73.%%
209
0.0e6
2024-08
2024-09
2014-10
2026-11
2024-14
2025-077
2025 02
2025-0
2023 tM
2023 rts
2023 2025.00 06
2025-08
2025-09
2024-08
2024-09
2028-10
2024-11
2024-12
2025-00
2025-00 025-02
2025 dp
2025-0 DO
2025-0 Ss
2025-01 025-06
2025-0 2025 07
2025-00
8008
09
(c) Datasets over time by robot type
(d) Share datasets by robot type
Episodas over Time by Robot Type (Top Other)
Share of Episodes
Robot
100
by Robot Type per Month (Top Other)
23.5%
Number of poddes
Other
80
41.3%
35.4 35.4%
54.6%
38. 7%
kuka
widdwx
panda
100.00 0%
100.00 0%
100 0%
98.9%
54.1%
19.7%
78.
78.7%
81.8%
0.5e6
Xarm
2122
40
60 9%
76.45 .%
47 1..9%
45 0%
10k
2104
234k
240
20
0%
2%
29
112k
94g
2016-10
2024 11
2024-12
2025
2023-02
2025-03
2023-04
2035-05
2025
2025-0
2025-66
2025-09
2024-10
2024-11
2024-12
2025-01
2025 02
2025-03
2025-04
2025-05
2025.0 06
2025-07
2025 08
2025-09
(c) Episades time by rohot type
(f) Share of episodes by robot type
Figure 5: Numbers and trends of downloads, datasets, and episodes by robot type over time. The number of episodes in each dataset has been explicitly tracked starting in October 2024 only. For completeness, we report the top-5 robots grouped in Other, for each of the metrics considered, in Table 4
Open datasets are available for downloads, and Figure 5a shows the evolution of the number of downloads over time, with a breakdown of the share of downloads per robot type (Table [bb and per robot type over time (Figure 5b) see Appendix [ for further details). Despite lerobot only supporting a limited number of robots (grouped under the Other tag in Figure 5a and Figure [e). datasets collected for other platforms such as the Franka Emika Panda and xArm lead in the num- ber of downloads and size of the datasets collected (Figure 5ep. We argue this follows from these platforms being often featured in research-orientee centralized data collection efforts (Collaboration al. 2025 Khazatsky al., 2025). Conversely, platforms such as the S0-10X are increasingly fea- tured small-scale decentralized community efforts powered by the accessibility of (1) the hard- ware platforms used and (2) LeRobot DeoobotDatae aset format, with 50%+ of the datasets contributed being collected directly on the SO-10X platforms (Figure 5d).
A primary design principle of the dataset format is scalability. The dataset architecture is opti- mized handle large-scale repositories potentially containing millions of expert trajectories. This unified interface for multi-modal, sequential data is designed for seamless integration with the Py- Torch ecosystem, further promoting standardized and repeatable research workflows. This design

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Published as a conference paper at ICLR 2026
Peak Memory
# Params
CPU
MPS
RTX 4090
A100
ACT
52M
817.4MB
462MB
211.24 MB
211.24 MB
Diffusion Policy
263M
1.22GB
224MB
1.12 GB
1.12 GB
To SmolVLA
3.5B
4.13GB
97MB
13.32 GB
13.32 GB
450M
1.69GB
555MB
1.75 GB
1.75 GB
Table 2: Peak memory consumption of policy models currently supported by lerobot. All models are run in full precision (fp32). Diffusion and Flow Models are run with 10 denoising steps at inference. All models maintain their original outputs shapes.
complemented by a native streaming capability designed to enhance accessibility: users can pro- cess remotely-hostee large-scale datasets without the prerequisite of downloading the entire corpus, thereby lowering barriers to entry for the broader community and improving on the accessibility of robot learning research. See Appendix C for more details streaming.
From Terobot datasets. poooot_dattset.t.. import LeRobotDattsset
from lerebot datasets st stramming dataset import StreamingLeRooottDatsss
repo id " "Lerobot/svla so101 1aa101piccplacee
Downloads the whole dataset and leads it in memory (allowing for random access)
dataset LeRobotDataset (repo_id)
# Streams frames the fly without downloading
(access frames sequentially, ()) I1 dataset StreamdnggeRooottaattet (repo_id)
3.3 MODELS
Robot Learning
lerobot supports reference implementation for multiple SOTA robot learning algorithms, providing useful baselines for ex- perimentation and accessible models across RL. such as HIL-
Behavioral
Reiffrcement Learring (AL)
Cloring (BC)
SERI. (Luo et al. 2024) and TD-MPC (Hansen et al.) (022) and BC, both for single-task ACT (Zhao et al. 2023). Diftusion Pol- icy (Chi et al. 2024) and VQ-BET (Lee et al. 2024). and multi-task
HIL-SERL
polidies
TD-MPO
ACT
models such as To (Black et al. 2024) and SmoIVLA (Shukor et al. 2025
Diffusion Policy
(F. 6
VQ-BET
lerobot offers support for custom models too, grouped together under the Other tag in Figure 7 All the control policies imple- mented in lercbot are written in pure Pytorch (Paszke et al. 2019), and integrated with the library allow (1) training models from scratch on datasets collected via real-world demonstrations, and (2) inference using openly available pre-trained models. The library is designed to for high accessibility, providing a composable of recipes which can be used train model from scratch less than 100 lines-of-code (LOC), and serve models in less than 40 LOC (Appendix D).
Generallet polisies
myg
SmoVLLA
Figure 6: The different robot learning algorithms currently supported by lerobot.
In its effort to foster accessibillity, lerobot supports multiple models with different computational constraints, ranging from lightweight single-task models to larger, multi-task models. ACT ZZhao al. 2023) particularly popular model dominating the number of uploads (Figure 7a) con-
sistenly ranking one of the most popular policies trained (Figure 77b) and used (Figure 7c). We
ascribe the popularity of ACT to (I) its small size and fast inference speed and (2) straightforward, ap- plication to limited amount of real-world demonstrations, allowing users to obtain well-performing policies with as little as 50 real-world trajectories. As a single-task model, however, ACT necessi- tates retraining whenever changes in the experimental conditions occur. SmolVLA (Shukor et al.) 2025) is powerful. small-scale Lanoon-Lnngage--- robots via language conditioning, resulting overall wider applicability practical scenarios.
model which allows to control real-world
7

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Published as a conference paper at ICLR 2026
Avg Inference Latency (ms)
Params
CPU
MPS
RTX 4090
A100
182.313
42.667
5.013
13.77
ACT
52M
+ 40..2
= 10.088
+ 0.006
+ +0.455
3453.838
369.788
613.893
Diffusion Policy
263M
(100%)
399.271
+ 0.193
+ 10.173
209.381
568.978
70
3.5B
(100%)
(100%)
2.762
2.937
2028.461
721.826
99.244
278.833
SmoIVLA
450M
302.55 (2%)
57.748
+ 1.195
1.888
Table 3: Average and standard deviation inference latency over 100 forward passes for policy models currently supported by lerobot. Diffusion and Flow Models are run with 10 denoising steps at inference time. (x%) indicates the percentage of samples that timed-out before the 5000ms hard stop (0% omitted).
Table 2 and Table 3 the peak memory footprint and the average inference latency, measured over 100 test samples, for the most widely used policies supported by lerobot. Evaluations were conducted on four platforms: (1) MacBook Pro MI (2021, 16GB, CPU only), (2) the same Mac- Book Pro with the MPS backend, (3) an NVIDIA RTX 4090, and (4) an NVIDIA A100. All models were executed in full fp32 precision at runtime, with inference timed-out after seconds. Over- all, peak memory footprints largely align with theoretical estimates obtained from the combination of model parameter count and numerical precision. The main exceptions are the CPU and MPS backends, where unified memory and frequent offloading to swap introduce variability, obscuring direct performance comparisons and increasing latency. Latency measurements are averaged across all non-timed-out trials, with both mean and standard deviation reported in Table 3] Smaller, task- specific models such as ACT exhibit high efficiency on accelerated backends like MPS and achieve inference rates of ~ 100-200Hz larger models such as то require substantially longer per each forward passes on average on all plat- forms, and even fail to complete inference within the 5s limit on lower-tier devices, underscoring the challenges in deploying robotics foundation models in practice.
high-end GPUs such as the RTX 4090 and A100. Crucially,
3.4 INFERENCE
lerobot defines a custom inference stack which designed to decouple action prediction (in- ference) from action execution (control), at both the physical and logical level (Figure S). This optimized stack is designed for modern robot learning policies, increasingly predicting sequences of actions (action chunks, 01-+---11 (Zhao al. (023)) rather than single controls. All the BC policies supported by lerobot predict action chunks.
remote machine connected the network to the robot's low-level controller. This design enables the use of higher-end computational resources than those typically available aboard robot for inference, while control is maintained at the de- sired control frequency stepping through the multiple actions received. Further, logical decoupling implements inference via an asynchronous producer-consumet scheme: the inference process pre- diets action sequences with look-ahead horizon H in parallel with environment control, which consumes actions at a fixed control rate. Overlapping predictions are merged via a generalized ag- gregation function f. which users can easily specify for their own use cases, ensuring a non-empty action queue and preventing idleness of the robot by overlaying action prediction and action execu- tion. We refer to Appendix E for more details on the performance of decoupled inference.
Physical decoupling allows inference
4 SIMULATION
While the core focus of lerobot is to lower the barrier to entry to enable real-world robotics appli- cations, lerobot does also support different simulation environments for benchmarking purposes. In practice, simulation proves challenging for the kind of contact-rich, complex tasks lerobot tar- gets. This justifies the library's 's choice to train as much as possible on real-world data, relying on simulation primarily for the systematic evaluation of robot learning algorithms. To enable this,
8

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Uploaded over Palicy Type
Share Models Uploaded
by Policy Type per Month
1000
Robot Type
Thend
250
28.2%
24.3%
37.6%
27. 1%
36.0%
0%
Number of Morels
800
ect
80
34
41.5%
diffusion
201
60
75.7%
70.1%
91.4%
40
66. 7%
70. 3%
36.0%
52.5%
52 .%
47.1%
43.4%
9.5%
59.
20224-1
2024-12
2023-00 oz
2025-422
2022--0
20225 ma
2025 oi
20125 d7
2025-a0 ap
2024-11
2024-12
2025-01
2025-02
2025-03
2025-04
2025.05
2025.06
2025-07
2025-00
2025-09
2025
(a) Models uploaded over time by policy type.
(b) Share of the models uploaded over time by policy
type.
Downlcaded over by Palicy Type
Share Models Downloaded
50000
by Palicy Type per Month
Robot Type
Thend
7.15
%%
39.0%
Number of Motels
30000
40000
#ct
diffus
Other
60
80
94.59 .9.9%
90.2%
8%
46.2
99.3%
99. 5%
85 8%
31.5%
23
91. 2%
60. 2%
20000
40
40.0 0%
45
10000
BOK
20
7%
4%
OK
2006-11
2006-12
20125-0 01
3022-02
2023-03
2023 di
2023-00 45
2015-06
2025-00
2023 de
2022-09
2024-11
2024-12
2025-01
2025-02
2025-03
2025-04
2025-00
2025
2025-07
2025-08
2025-09
(c) Models downloaded over time by policy type.
(d) Share of models downloaded over time by policy type.
Figure 7: Numbers and trends uploads and downloads of robot learning models by policy type over time. TD-MPC (Hansen et al. 2022), HIL-SERL (Luo al. (2024) and VQ-BET (Lee et al. 2024) are absent from all visualizations as they are not typically uploaded by users.
Runs the policy to obtain chunk new actions, given 0g
PolicyServer
Pg
IT (u)
G2
i ((,))
3
RobotClient
Starting state, On)
Performs n actions
Receive actions from s(og) Computes aggregate queue, (r((0),
then sends new observation, og
Receive actions from (Oe)
Starts acting with the updated queue
Figure 8: Overview of the generalized inference schema supported by lerobot. whereby a remote policies for inference, while the robot client receives stream of the actions chunks enact. The schema provides scalability and flexibility through the possibility to fully customize the function / used to aggregate overlapping chunks.
server can be used to host compute-expense
provide evaluation support via the lerobot API for both LIBERO (Liu et al. 2023) and Meta- World Yuu et al. 2020), two popular simulation environments that are often used as benchmarks for robot learning research (Black al. 2024 Shukor al. 2025).
LIBERO
While developed to specifically assess the life-long learning capabilities of generic au- tonomous agents, LIBERO is also used in robot learning research as benchmark demonstrate

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Published as a conference paper at ICLR 2026
the adaptability and performance of novel methods, e.g. in manipulation settings (Black al. 2024 Shukor et al.] (025). While accomodating for procedural task generation, LIBERO proposes four fixed task suites with 10 tasks each researchers can benchmark on. Task suits developed to quantify the amount of information that is shared under different conditions in terms of spatial ar- rangement (LIBERO- LIBERO-SPAAIAL) object considerd TLIBERO-OBJECTT and overall task variations (LIBERO-GOAL) LIBERO does also provide a benchmark for continuing, short (LIBERO-90) and long-horizon (LIBERO-LONG) tasks requiring the transfer of entangled knowledge between the aforementioned sources of variation. Typical LIBERO evaluation protocols report the success rate over a number of test episodes, and lerobot natively integrates LIBERO.
Meta- World Similarly to LIBERO, Mera-World was first developed as a benchmark for assess- ing the performance of generic autonomous systems, with particular focus on fast adaptation to novel scenarios via meta-learning. Adapting quickly to novel scenarios is a particularly promis- ing area of research in the field of robotics, as it holds the premise of enabling the development of systems that can effectively generalize to unseen tasks leveraging previously acquired information. The Meta-World benchmark consists of 50 distinct robotic manipulation tasks that can be combined into different benchmark suites. The benchmark is structured to quantify performance under dif- ferent learning regimes: multi-task learning (MT10, MT50) where the agent learns multiple tasks simultaneously with access to task identifier, and meta-learning (ML1, ML100 ML45) which as- sesses the agent's ability to adapt to new tasks using minimal data. All 50 different tasks require the same robotic arm in the same setup to interact with multiple objects with different shapes and diverse uses. Critically, all the high-level tasks presented in Meta- Meta-World require the robot to execute combination of fixed, more fundamental skills such reaching for an object manipulating it. Such a common task conceptual structure proves instrumental in providing a shared interface for autonomous agents to use and learn how to transfer knowledge across different tasks: key property of the adaptability that is required of modern robot policies (Black et al. 2024 Shukor et al. 2025).
CONCLUSIONS
In this work we introduced Lerobot, a unified, open-source stack for end-to-end robot learning that bridges low-level control, large-scale data tooling, and scalable learning algorithms. We showed how accessible teleoperation of multiple real-world robot through a shared middleware can be used to collect real-world data across a variety of robot platforms. Further, we illustrated how standardized datasets can be exploited to collect and reuse data at scale, powering advancements in robot learning thanks to the thousands of datasets collected, resulting in hundreds of thousands of episodic data, and hundreds of models openly contributed by the robot learning community.
Limitations We identify several limitations remaining in contribution. First, robots coverage is currently far from exhaustive, as we support a practical but incomplete set of arms, grippers, sen- sors, and controllers. Over the course of 2025, lerobot went from supporting 3 manipulation setups (Koch-vl.1, SO-100, ALOHA) to the 8 regular, humanoid and mobile manipulators cur- rently supported, and we highlight that keeping a similar rate of progress is paramount to properly serve the robot learning community. Second, the coverage in terms of robot learning algorithms is also noneexhaustive. We provide strong, reproducible implementations across key paradigms, while extending the library with additional algorithms remains future work. Third, achieving strong prac- tical inference performance still requires low-level optimization (quantization, graph compilation, etc) that are currently disregarded by the library. We view these limitations as concrete, tractable avenues for community contributions and future development, and in the very spirit of open-source, invite the broader robot learning community to address them. However, despite these limitations, our work takes a significant step toward an end-to-end stack for robot learning, providing a useful tool for researchers and practioners in the field.
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A
OPENLY-AVILABLE ROBOTS
S0-10X Guide
from
Knight
et al.
2024):
https://github.com
TheRobot.ttudio/50--A ARM1 00
Koch-y1.1 Guide from Moss
2025
https://githut: comabbcs////ssmmss
Coch-vi-1
ALOHA Guide from Zhao et
2023 here
Hope.JR-Arm Guide from T Sobootottt (2025):
A/Arm/ RRM. https://github.
LeKiwi Guide from SIGRobotic-UUIUC (2025):
https://github.co
BOM md
B REAL-WORLD ROBOTS API
from
Lerobot borbbot.teleccee wtw.epeeeepese.s..ooooooo so100 leader. Leader import
50100Leader
from lerobot teleoperators. sollooooeee..o.oooooooooeeeeeeee so100 otlp__aoooooooeeeoooooooooooooe import
50100Follower
teleop S0100Leader
(provided teleop matches) can alsc be Reachy-2, LeKiwi, etc. robot s0100Fallower
10
()
robot. ()
13 action saleop.get_action
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Robot Datasets
Robot
Downloads
Robot
# Episodes
unknown
2370
unknown
711729
google_rober robot
213852
lekiwi
535
google robot
438560
unknown
170706
arx5
371
so101
319586
so 10100
78510
aloha
334
so100
278697
so101
58299
aiworker
202
aloha
45219
easo
45652
(a) Top-5 robot platforms in (b) Top-5 robot platforms the
(c) Top-5 robot platforms in "Other" category for number of episodes.
"Other" category for number of "Other" category for number datasets.
downloads.
Table Breakdown of the Other category by top-5 robot platforms across datasets, downloads, and episodes.
yaak-ai/L2D
Robot Type
behevior-112225-calleeeeg
xarm
jesbul/bridge_V sa_______eeeee Jerobot
panda
Qu3tzal/bridgev2
widowx
PPC-COMMUNIYYrrccaa2220077 data Jerobot
IPEC-COMMUNIYY/UJa_aa. Jerobot
google robot
|PEC.COMMUNNT//orige. leio_lt
r1pra
KIA Nira EV 2023
cadene/droid 1.0.1
IPEC-COMMKUNIT//oodd.... lerobot
PEC.COMMUUNTYYlarggagg.ldddllddd
10a
Number of downloads
Figure 9: Openly-available datasets with the largest number of downloads using the
LeRobotDataset format. The most downloaded datasets are academic benchmarks released by the research community (Collaboration et al., 2025 Khazatsky et al. 2025).
14 print (action)
15
{ sswwhhher_ppanaeceeeeeeeeee
pac.er_apppppsss 84.74,
16 Schoulderliftt.tee
4.95,
#elbow_flex.pos': 70.6. 6,
18 "wrist_flex.pos" -88. -1,
19 'wrist_roll.pos' 57.89,
20 gripper.pos'' 4.13)
robot act (action)
send_acttinnn
moves robot according ng 'action"
C DATASETS
Table further breaks down the Other category for the number of downloads, datasets and episodes, and it shows how faulty dataset that do not explicitly record the robot platform used (tagged as unknown) dominate in the Other category.
Figure 9 shows the most downloaded datasets by robot type. Crucially, the largest number of down- loads is not achieved for platform natively integrated in lerobot, further undescoring the adop- tion of the LeRobot Databett format in the robotics community.
C.I STREAMING DATASETS
The development of St reaaiiinnggi rew.mingn.eooootaattaaae. addresses several fundamental chal- lenges associated with the efficient utilization of large-scale robotic datasets in robot learning pipelines. Traditional approaches to dataset handling rely on pre-loading data into local mem- ory, which becomes increasingly impractical as datasets grow to the million-episodes scale. wrreemminggLRRoottDaaee
supports a streaming paradigm, whereby frames- crames-defined defined as in- dividual items in dataset-an are fetched on-demand from remote storage rather than preloaded in their entirety. This architectural shift required addressing three core challenges: (1) efficient data access under strict memory constraints, (2) ensuring sufficient randomness during iteration sup-
15

---

Published as a conference paper at ICLR 2026
With Deita Timestamps Screaminn vs Local
2314
0.12
2.30
2230
3334
332
033
(a) Timing performance of stepping through single (b) Timing performance of stepping through frames of a StreammingeRRoootDaat com- pared pre-loaded attse.
dataset retrieving multiple frames of
GtreaningLeRobooDatasst compared
pre-loaded LeRobotDataset.
Figure 10:
Timing performance of streaminggeeRobbbbattaet versus regular LeRobotDataset.
port robust learning, and (3) enabling multi-frame retrieval in a setting that inherently sequential and non-indexable.
partitions robotic data into tabular records (. (.paquuet files) and compressed videos (.mp4 files), alongside lightweight metadata. Metadata files are downloaded fully due to their negligible size relative to the dataset, but all high-volume video and control streams are processed on demand. This achieved through two principal design choices: (1) adoption of an IterableDat aset interface, and (2) integration with torchcodec for on-the-fly video decoding. These components together enable data consumption through simple iterative calls, while maintaining memory usage bounded irrespective of dataset size. Provided good network connectivity, Figure shows timing performance is comparable between the two formats in the steady-state regime (after initialization).
Efficient Streaming of Lcage-Scale Data. The LeRobotDataset
C.2 EXAMPLE: USE A DATASET
import torch
from lerobot datassts. 1erobot_datasee l import LeRobotiDataset
delta timestamps
0.2, and 0.1 seconds *before* each frame
"observation. images. wrist_camerar [-0.2, -0.1, 0.0]
Optionally, use StreamingLeRobbttatteet to avoid downloading the dataset 10 dataset LeRobotDataset
11 #Terobot/svla_sollll ottp://rle__ll000lpppppppleeee
delta_timestampss ta_timstatms
14
15 Get frame in the dataset by their index
16 sample dataset
print (sample)
18 {
ietervattin.stttee... tensor ((...)), 20 'action': tensor tensor([...]) ([...)),
19
# extra dimension due to delta timesteps
ooservvttion imwgaesiiiiiiiiii..mmmmm images............ wrist_camera! tensor (13, C, W])
23
24
25
batch_si ze-16
wrap the dataset batalicader for training/inferrnce purposes
16

---

Published as a conference paper at ICLR 2026
Uploads by Policy Type
Downloads by Policy Type
pio
diffusion
diffusion
pio
Other
smolvia
Other
act
smolvla
500
1000
1500
10000 20000 30000 40000 50000
(a) Models uploaded policy type. Policies present have been publicly uploaded.
(b) Models downloaded by policy type. Policies
present have been publicly downloaded.
28
torch. waw.lls.ddaa..ataloc....
29
dataset,
32
33 Iterate over the Dataīoader in a training doop
34 num epochs 1
device "cuda" if torch. is availablee) else "cpu"
37
for epoch in range (num_epochs):
38
for batch in data_loader:
# Move data to the appropriate device (e.g., GPU)
observations batch batch(" (obbseovaiioonnoieiieee state") to (device)
41
actions batch ("actin"" to (device)
42
images batch ("obscrvation wmwges.wrist_cameet www.w_ "] to (device)
43
Next, process the data for training inference
C.3 EXAMPLE: USE A STREAMING DATASET
from Lerobot dat lereeaiing_dttaaee import Streamn.nggeRoobtDDtastt
Streams frames the fly without downloading the dataset
dataset @treamiinglattoooootaaaaaae s delta_timeste delta_tiimsttan---eelttiiiiieeee
D MODELS
D.1 EXAMPLE: TRAIN MODEL.
import torch
from lerobot. configs.ocioo....c.coo........se types import FeatureType from lerobot datasets. lerobot_datasee import ( LeRobotDataset, LelobotDatasstMMataaae
From lerobot datasets utils import dataset coaitt__tt_oolic_ffeetuee
from lerobot www.pies..eeeeoooooo........oo.e factory import pake_pre_post_prccesor
10 Users can use many plug-in policies from the library
from 1erobot. palicips.ciiieeee ffusion wtw.sst..........oouuooeo.... confi confiigraioondii ffusico import
Di ff
17

---

Published as a conference paper at ICLR 2026
From Lercbet. ottp://too...aiaaaaaaaaaaaaaaooa import DiffusionPolicy
14
15 output_directory =
16 device torch. device ( ("cuda")
training_steps 5000
log_freq
19
20 repo_ic "Lerobot/pusht"
Replace with your dataset
dataset_metadee = Latoootbatattteeaatataaeea
23 features dataset_to_pan dctset_toopooll__ffeerre otto::/eeeeeauureeccdceeeeeeeaee
24 output_features -
25
key: ft for key, ft in features. items ()
if ft.type is TeatureType.ACT ACTION
28
input features =1
29
key: ft for key, ft in features. items ()
30
if key not in futtut_
33
ofg Di DiffusicnConfig(
34
imput_featurest
35
ftt feattuurreteeeeeseseeeeeeeee features
37
38
policy DiffusicnPoolccy (cfg)
39 Dolicy.trainf.
tolicy.to (device)
preprocesscr, postprocessor make pre post, processors efg,
42
http:/ddttttttteetttttteeeee www.mmettttttteee stats
45
44
46
mella_timesttee image": [-0.1, 0.0],
47
"observation iesservationstaateer [-0.1, 0.0], "action": [
48
-0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4 1,
51
52
55
dataset LeRobotDataset (repo_id, detfaarcinetttmmesrroooottaaaaan
56
57
optimizer = torch. aotii..damm
1x=le-4)
dataloader torch.utile data Datallader
dataset,
60
num_workers=4,
61
batch_size=64,
62
shuifle=True,
pin_memory=device.tppp !- "cpu", drop_last-
65
66
step =
done False while not denc:
70
for batch in dataloador:
71
batch preprocessor (batch)
loss, policy. forward (batch)
loss. basssaacc
74
optimizer.com step Detimizer.zero_ger.
15
zero
16
it step log freq 0:
18

---

Published as a conference paper at ICLR 2026
print sf"step: (step) lcss: [loss. item ;.3E)")
79
step 1
80
if step >= training_stepe:
done = Truc break
84 Save policy checkpoint.
85 wolicy.save_preeraaiie http://peerrrretttoooooteeeeeeee directory)
86 preprocessor. save waw__pearaaaieeddddeeeeeeeaaaaed (
postprocessor.c save pretrained (output directory)
D.2 EXAMPLE: USE PRE-TRAINED MODEL
From typing import Any
From 1erobct. pel iciis...... smovs.s www.wma.cooni.iii.e.. wtt.aa.ooorrrraiiosssolllllooo
import \
from lerobot, cerbbtt.ooicciess smolvla wwwlvl..mmeelllll..mmmm model modelinggsmolll ing_smollla import SmolVIAPolicy
from lerobot datasets. lerobot dataset import
Lefccottaataseteetaaaa
from lerobot. www.bott.ppiiiiss... factory import make_pre_post__roccast
from lcrobot orto0p.ccccoppttooo.oo.oo...ooee
import
10
s0100Fellower
1
Take cataset which SmolVLA was trained, for normalization sttpaatt/vvll_slllpiieeeeeoee 14 dataset_metadas
13 repo_id id
Lefosttaataetheeedttaall (repoo (repo_id)
15
16 cig SmeeVoACon
policy Smol smel
18 preprocessor, postprocessor make_pre_pst_proceesco
cfg, 19
20
robot S0100Fallower ....)
25 raw raw_obs: dict [str, Any] rebot Beret.get__oaee get_observation ()
24
25 Freprocess the observation for inference
policy_input preprocessor (raw_cbs)
Select the action from the policy 28 policy_output palicy.select_ade select_acttennppliine
29 Postprocess the action for the robot
30 molicy_actions postprocessor (pulicy_output)
32 robot send action action)
E INFERENCE
Optimized inference accelerate cycle times across multiple tasks with comparable performance (Ta- ble () and provide a scalable path to higher model capacity without compromising on real-time control, provided access to network. In particular, the speedup presented Table derives from logical
computing the next chunk while the current one has not been exhausted vet-rather than physical decoupling, both the server and client run on the same ma- chine, though in principle the inference stack allows for communication between different machines.
E.1 EXAMPLE: HOST A REMOTE SERVER
this script start the policy server any machine from lerobot server..o import PollcyServerConfig from lerobot , www.stsriiissc.o...ooe. poc.
import serve
config Pol icyServerconfigl
host="1 oca host"",
19

---

Published as a conference paper at ICLR 2026
Inference
Success Rate (%)
Inference
Time (s)
Inference
# of Cubes
Pick-Place
Stacking
Sorting
Avg
Total
Avg
Std
Total
Avg.
Syne
75
90
70
78.3
Syne
137.5
13.75
2.42
Sync
9
1.8
0.45
Asyne
80
90
50
73.3
Asyne
97.0
9,70
2.95
Asyne
(a) Performance (success rates).
(b) Task completion time.
(c) Performance in fixed time
(60s per each episode).
Table 5: Comparison between regular (Sync) and optimized (Async) inference. We evaluate the SmoIVLA implementation provided in lerobot on three real-world performed using the SO-100 arm, consisting of (1) pick and place cubes (2) stacking cubes on top of each other and (3) sorting cubes. lerobot's 's decoupled inference schema achieves similar success rates (left) but results in significantly reduced cycle times (middle) and thus higher throughput (right), over the 10 test episodes (60s each) for the task considered.
port=8080,
serve (config)
E.2 EXAMPLE: STREAM ACTIONS TO ROBOT
Run this script to start the rebet client on the robot's s computer
2 import threading
from lerebot www.pttssseeee....t...... import RobotclientContig import RobotClient
from lerobot. www.bbbtssrripss.sccc..o
camera_cff robot_cfg ... # given robot supported by the library
cameras used by the vi visuomotor policy
10
3. Create client configuration
11
client_cff RobotClientConfiqe
12
robot-rebot_cfg,
attach to the server running the policy acvee_adree==locahhooe
14
15
use a higher-end device for inference
16
policy_device=="adadtt
policy_type- toliey_type="p""", pretrained name Battee_or_aahaaaaaeae path=" Lercbotpp"p"
19
20
21
4. Create and start client
client RobotClient (client_cfg)
23
24
task
Specify the task using natural language
25
if Client.start///
Start action receiver thread
28
action_roeiver_thredd threading. Thread
29
target=client..eeae reciive_attiiicn. daemon=True
30
33
try:
34
$ Run the control loop
client. control loop (task)
except KeyboardInterrupt:
client stop action_receiver_ttiee threaadd..eetrta..oo....
38
20


Reviews:
[{"review_id": "9HqELpQxVQ", "reviewer": "Reviewer_5MwV", "summary": "The paper presents a library for end-to-end robot learning. It addresses a very pervasive and relevant problem of a typically fragmented robotics stack, for which having simple and unified tooling would greatly enhance productivity and rampup time for beginners. It has various subcomponents that address different parts of the stack: hardware integration, local and streaming datasets in a shared format, async inference for action chunking based models, RL and IL constructs, simulation integrations etc. All of these together are an excellent starting point for someone looking to conduct research and development on low-cost readily available robots in a non-production setting.\n\nOn balance, I am slightly leaning towards accepting the paper as it would provide a reference to people using the tooling to conduct research and for publications. However, I do feel that the core functionality of the library has several avenues for improvement (some iterative and others fundamental).", "questions": "- Do you have any detailed benchmarks for the throughput of LeRobotDataset and StreamingLeRobotDataset as opposed to something like MCAP?\n\n- Do you plan to add GELLO teleop support?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- This is clearly a significant contribution that will help a lot of additional people easily take up robotics and experiment on well-integrated low cost robots. Someone who would previously have been blocked on a part of a complicated stack can use the abstracted tooling of the library to get started quicker and with lesser frustration.\n\n- The library supports a lot of popular models and algorithms that have been shown to work on real robots in a variety of settings.\n\n- The library contains support for asynchronous inference (i.e. that used in modern models that use action chunking and related methods). The functionality to use a hybrid cloud-robot inference is also very useful.\n\n- The dataset tooling seems very useful, especially the structure which helps handle larger scale datasets with millions of trajectories.\n\n- I find it positive that the paper explicitly lists the limitations of the library. This is absolutely understandable since it is in active development.", "weaknesses": "- While the overall library seems excellent as a starting point for someone learning about robotics or who wants to get started with minimal friction with a toy problem, it seems pretty raw for something like production usage. The parallel I would like to draw here is of pytorch, which was suited for both simplicity and production robustness.\n\n- Local porting of large datasets seems very slow compared to what should be possible. E.g. 7+ days of processing time for DROID should be able to be sped up significantly by better parallel processing and systems engineering.\n\n- I feel like LeRobot overindexes on what is ‘popular’ in the current robotics/ML types of models and algorithms (e.g. RL and imitation learning), but lacks some essential functionality that a user would likely need to resort to a third party library for. An example of this is optimized motion planners or IK solvers that could run in either real or simulation environments, which could give ground truth data to train particular e2e models.\n\n- Simulation is often a critical component of end-to-end robot learning. I would have liked to see additional support for additional simulation frameworks (e.g. IsaacSim or Genesis). This to me, would abstract away a lot of the friction a typical user needs to undergo to setup the simulation environment itself. Note: MetaWorld and Libero seem impractical for a lot of real world tasks compared to something more comprehensive.\n\n- A crucial element of deploying robotic systems in the real world is a robust safety layer. In my opinion, the deployment portion of the library should treat safety as the most important principle and contain abstractions that allow for this to be realized (e.g. add protective stops based on certain criteria). While these are typically also implemented in the firmware of the robot itself (e.g. as protective stops or estops), having an added layer on top that can focus on the specific logical safety considerations would be helpful when directly coupled with (for example) the RobotClient / PolicyServer\n\n- (not relevant to the assessment of the paper) It was impossible to not know where the work came from, given that the library is already very popular in the online robotics/ML community.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MGWbXoXz5K", "reviewer": "Reviewer_aYVW", "summary": "This paper introduces LeRobot, an open-source library for robot learning that provides an end-to-end stack for scalable robotics research. It features unified robot integration, standardized datasets, optimized inference, and efficient, reusable state-of-the-art robot learning algorithms. This effort addresses fragmentation in the field, where researchers typically develop tools for their own use with specific robot platforms, data formats, and learning algorithms. By providing a unified and accessible framework, the library reduces the entry barrier and accelerates progress in robot learning through improved accessibility, scalability, and reproducibility.", "questions": "- How does LeRobot compare to ROS? What factors might make LeRobot more successful than ROS as an open-source robotics platform?\n- How is the correctness of the implemented algorithms ensured, and how is reproducibility maintained?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "- The paper addresses the important problem of entry barriers in robotics and embodied AI research. A unified platform and interface for hardware, data collection, and learning would benefit many researchers in the field of robot learning.\n\n- The paper identifies key challenges and roadblocks to progress in robot learning, which motivates the proposed library.\n\n- Current downloads and usage of the platform already demonstrate the value and need for such a unified platform. The statistics on model and dataset downloads also provide insights into the community’s interests and needs over time.", "weaknesses": "- Lack of discussion of what tasks or scenarios may not be suitable for the current LeRobot library. Would all researchers in robot learning benefit from the current LeRobot library?\n- The paper would benefit from more discussion on the integration of simulated environments, as many researchers start with toy simulations. What is the procedure for adding custom environments to the ecosystem?\n- Broken citation on line 266.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FXadKX1n4M", "reviewer": "Reviewer_fnXV", "summary": "LeRobot is an open-source, end-to-end robot-learning library from a consistent Python middleware for diverse, low-cost and humanoid/mobile robots to standardized data tooling and scalable policy deployment. It introduces the multimodal LeRobotDataset to ease large-scale collection and reuse of teleoperation data, and an optimized, decoupled inference stack that separates action prediction from execution for robust real-time control. The library ships clean PyTorch implementations of state-of-the-art methods across RL and BC (e.g., ACT, Diffusion Policy, VQ-BET, π₀, SmolVLA) with emphasize on accessibility, reproducibility, and openness. I have no doubt that it presents a useful framework for the community.", "questions": "1. **Scope of the “entire robotics stack.”** The claim of covering the “entire robotics stack” is an overstatement. The library primarily targets robot learning for manipulation tasks and omits major subfields such as SLAM, sim-to-real transfer, and broader perception and control systems. The authors should moderate this claim to accurately reflect the covered scope.\n2. **Ambiguity in “explicit” vs. “implicit” models.** The terminology used to distinguish explicit and implicit models is confusing. The paper conflates learning-based methods with implicit models, whereas both explicit (e.g., flow models) and implicit (e.g., energy-based) formulations exist in machine learning. Clarifying what each abstraction encompasses, and specifying how methods that explicitly model the world or action distributions (with or without data) fit into this taxonomy, would improve clarity.\n3. **Clarity on compounding errors.** The paper states that classical, modular methods (labeled as explicit models) suffer from compounding errors, but so do monolithic, data-driven policies (labeled as implicit models). It is unclear how the monolithic networks mitigates this issue. The authors should clarify whether their choice genuinely addresses compounding errors or simply shifts where they occur.\n4. **Definition of middleware.** The role and scope of “middleware” are not clearly defined. A brief explanation in the main text of what components or functionalities are included under this term would improve clarity for readers.\n5. **Language and grammar issues.** The paper would benefit from a grammar and spell check. Some (nit-pick) examples include:\n    - “aboard robots” → “onboard robots” (more standard usage)\n    - “parallely to low-level control” → “in parallel with low-level control loops”\n    - “the the” → “the”\n    - “coexistance” → “coexistence”\n6. **Reproducibility of supported methods.** It is unclear whether the methods implemented in *LeRobot* reproduce the results reported in the original papers? If not, how large is the gap? Providing a clear mapping (e.g., method X on benchmark Y) and where to verify such reproduction within the repository would help assess reliability of the implemented algorithms.\n7. **Extensibility and community contribution.** Including brief documentation or a section describing how to add new robots, learning methods, and simulation environments would make the framework more approachable for community contributors.\n8. **Future directions.** A short discussion on planned extensions—such as supporting additional teleoperation devices, robot types, and learning methods—would help situate the library’s development trajectory. The README may be a suitable place for such a roadmap.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 1, "contribution": 4, "strengths": "1. Many policy learning algorithms, standardized end-to-end robot learning dataset schema, tooling, and policy deployment in one framework\n2. Real-robot coverage with low-cost focus for accessibility and democratization; resource transparency\n3. Strong evidence of community uptake", "weaknesses": "The paper’s writing currently falls short of top-tier conference standards, particularly in the precision and clarity of its claims. In addition, it lacks a discussion of reproduced results from the implemented algorithms, making it difficult to assess the reliability of the framework. My detailed concerns are outlined below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)