RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots
Authors: Soroush Nasiriany
Sepehr Nasiriany
Abhiram Maddukuri
Yuke Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: Robot Datasets and Benchmarking
Vision-Language-Action Models
Robot Simulation
Primary area: datasets and benchmarks
TL;DR: RoboCasa365 is a large-scale benchmark of 365 everyday tasks that advances the study and evaluation of generalist robots across diverse environments and data.

Abstract: Recent advances in robot learning have accelerated progress toward generalist robots that can perform everyday tasks in human environments. Yet it remains difficult to gauge how close we are to this vision. The field lacks a reproducible, large-scale benchmark for systematic evaluation. To fill this gap, we present RoboCasa365, a comprehensive simulation benchmark for household mobile manipulation. Built on the RoboCasa platform, RoboCasa365 introduces 365 everyday tasks across 2,500 diverse kitchen environments, with over 600 hours of human demonstration data and over 1600 hours of synthetically generated demonstration data---making it one of the most diverse and large-scale resources for studying generalist policies. RoboCasa365 is designed to support systematic evaluations for different problem settings, including multi-task learning, robot foundation model training, and lifelong learning. We conduct extensive experiments on this benchmark with state-of-the-art methods and analyze the impacts of task diversity, dataset scale, and environment variation on generalization. Our results provide new insights into what factors most strongly affect the performance of generalist robots and inform strategies for future progress in the field.

Published as a conference paper at ICLR 2026
ROBOCASA365: A LARGE-SCALE SIMULATION FRAMEWORK FOR TRAINING AND BENCHMARKING GENERALIST ROBOTS
Soroush Nasiriany '", Sepehr Nasiriany" "", Abhiram Maddukuri ' Yuke Zhu 1.2
'The University of Texas at Austin, ?NVIDIA Research; Equul contribution h http://oooccaaaaaooo
ABSTRACT
Recent advances robot learning have accelerated progress toward generalist robots that perform everyday tasks in human environments. Yet it remains difficult to gauge how close we are to this vision. The field lacks a reproducible, large-scale benchmark for systematic evaluation. To fill this gap, present Robo- Casa365, a comprehensive simulation benchmark for household mobile manipu- lation. Built the RoboCasa platform, RoboCasa365 introduces 365 everyday tasks across 2,500 diverse kitchen environments, with over 600 hours of human demonstration data and over 1600 hours of synthetically generated demonstra- tion data data-making it one of the most diverse and sarge-scale resources for study- ing generalist policies. RoboCasa365 is designed support systematic evalua- tions for different problem settings, including multi-task learning, robot founda- tion model training, and lifelong learning. We conduct extensive experiments on this benchmark with state-of-the-art methods and analyze the impacts of task di- versity, dataset scale, and environment variation on generalization. Our results provide new insights into what factors most strongly affect the performance of generalist robots and inform strategies for future progress in the field.
Diverse Scenes
Rich object Libeaxy
Interactive Fixtures
365 Everyday Tasks
2200. Hours bata Systwettel
ROB CASA363
2500 Kischen Scones
500k+ Trataccariis
3280+ Objects
Systeratic Benchearki
Wide- Banging Skills
Long Hozizon Mobile Munipulation
Senantic Reasoning
Nee Sarre
Somes Fwad)
Figure 1: Overview of RoboCasa365. RoboCasa365 is large-scale simulation framework for training and benchmarking generalist robots. RoboCasa365 includes 365 everyday tasks, 2500 diverse kitchen scenes, over 600 hours of human demonstration data, plus 1600 hours of synthetically generated demonstration data, and systematic benchmarks for training and evaluating generalist robot models.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Recent advances in robot learning have brought the field closer to generalist robots capable of per- forming broad range of tasks across diverse environments. A growing body of work has focused on collecting large-scale real-world robot datasets and training high-capacity robotic foundation models (Black et al.) 2024| Gemini Robotics Team et al. 2025 NVIDIA et al. 2025 Physical Intel- ligence et al., 2025). These models have demonstrated meaningful generalization to novel objects, environments, and tasks, showing great promise towards developing broadly capable policies.
Despite these advances, two major challenges remain. First, training generalist robots requires vast amounts of robot experience data. Although recent datasets have grown substantially in size, they remain limited in diversity and task coverage, which constrains the ability to train robust, gener- alist policies. Second, real-world evaluation and benchmarking are resource- inntrceeintensive and time- consuming. They are often affected by experimental noise, making it difficult to perform repro- ducible, systematic comparisons across methods.
Simulation provides practical avenue for addressing these challenges. With simulation, we can create large-scale interaction datasets, covering an effectively infinite variety tasks and environ- ments (Mandlekar et al.) 2023 Jiang al. 2025). Simulation also enables rapid experimentation, controlled evaluation, and reproducible benchmarking that would be infeasible in real-world robotics (Saxena et al., 2025). Together, these capabilities make it possible generate data, train policies, and systematically evaluate generalist robots at scale. However, existing simulation frameworks fall short of this potential. Most current tools support only limited tasks and environments, often focus- ing on simple object manipulation or single-room scenarios (Zhu et al.) 2020: James et al. 2020) Wang et al. 2023). The datasets they generate are small relative the diversity and complexity of real-world robotics challenges, and benchmarking is typically confined to these narrow condi- tions (Liu et al. 2023) Mandlekar et al.) [022]. Consequently, it remains difficult to study how task diversity, environment variation, and dataset scale affect policy generalization.
To address these gaps, we introduce RoboCasa365, comprehensive simulation benchmark for ev- eryday household robotics. RoboCasa365 is built on top of the RoboCasa simulation framework by |Nasiiiayy et al. (2024), and structured around four core components:
Comprehensive tasks: RoboCasa365 defines 365 tasks spanning 60 distinct kitchen activities, in- cluding manipulation, semantic reasoning, long-horizon planning, and memory nemory-dependent tasks. This task diversity allows evaluation across multiple dimensions of generalist robot capability.
Diverse environments:
The benchmark includes 2,500 unique kitchen scenes modeled from real kitchens across the United States. These scenes capture wide spectrum of layouts, object configu- rations, and visual variations, providing realistic contexts for a variety of everyday tasks.
Large-scale data: The benchmark provides over 2,000 hours of robot interaction data. This includes 612 hours of human demonstration data and an additional 1615 hours of synthetic demonstration data using the MimicGen data generation tool (Mandlekar al. 2023 to significantly expand the quantity of data.
Systematic benchmarking: RoboCasa365 supports rigorous evaluation across three learning set- tings: massively multi-task training, foundation model training, and lifelong learning. The bench- mark is designed facilitate reproducible, large-scale experiments and in-depth analysis of which data and environment factors most strongly influence generalization.
By integrating these elements, RoboCasa365 provides a large, diverse, and systematically structured resource for studying generalist robots in simulation. It enables researchers to explore algorithms, run reproducible evaluations, and analyze the impact of task and environment diversity on policy generalization. Using RoboCasa365, we conduct extensive experiments to compare state-of-ther tf-te-- methods, evaluate learning strategies. and investigate the factors that most strongly drive perfor- mance in generalist robot learning.
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RELATED WORK
Robot Simulation Frameworks. There is a long line of prior work on building robot simulation
frameworks (Zhu et al. 2020 Gu al. 2023 Mittal et al. 2023 Tao et al. 2025 Szot al. 2021
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Kolve et al. 2017: Li et al. 2023
2024 Liu et al.
2023
Deitke et al. 2024 James et al. 2020). while we focus
2022) Some focused on
tabletop settings (hu et al.
2020 Liu et al.
2023
Li et al.
on simulating entire room- scale scenes, sumilar to some other pror works (Li et al. 2023 Nasiriany
et al. 2024 Szot et al., 2021 Kolve et al., 2017). Our work unique in that it features hundreds of tasks across thousands of unique scenes, large-scale, high-quality demonstration datasets, and suite of benchmarks for training and evaluating generalist robot models. To our best knowledge, our work is the first simulation framework to satisfy all of these criteria.
Datasets and Benchmarks for Generalist Robots. There have been numerous efforts towards collecting large robot datasets in the real world (Brohan al.. 2022; Walke et al. [2033 Khazatsky et al.] 2024] X- X-Embodiment Collaboration et al. 20033. Evaluating and benchmarking policies trained on these datasets the real world is challenging due to the resources needed run large-
scale systematic evaluations, despite several recent approaches towards this goal (Atreya al. 2025
Zhou et al. 2023 Yenamandra et al., 2023 Zhou et al. 2025| Krotkov al.) 2016 Correll et al.
2018). Simulation enables running large-scale benchmarks. However, most simulation benchmarks
are confined to a very narrow distribution of tasks and environments (Mandlekar et al. 2021 Zhu
et al. 2020; Liu et al., 2023; TRILBM Team et al., 2025). Li et al. (2023) bring forth some of the largest diversity of environments and tasks to date, but lack accompanying large-scale datasets for all of these tasks. Nasiriany et al. (2024) include 100k demonstrations spanning 30 tasks and 100 scenes. In contrast, our datasets comprise over 500k demonstrations across over 300 tasks and 2500 unique scenes. While prior work focuses on benchmarking specific methods such as multi- task training TRI LBM Team et al. 2025; Nasiriany et al. (2244) and lifelong learning (Liu et al., 2023,, we provide comprehensive suite of benchmarks to systematically study multi-task training, foundation model training, and lifelong learning.
Training Generalist Robots. There long body of work on learning generalist robot policies from large, diverse robot datasets (Octo Model Team et al. 2024 Open X X-Emmbodment Collabora- tion et al. 2023 NVIDIA et al., 2025] Brohan et al. 2023 Kim et al., [024]| et al. 2025 Wen et al. 2025). In our work, we aim to be agnostic to the choice of model, and instead create bench- marks to systematically assess the capabilities of these models across distinct settings, including multi-task training, pretraining, and fine-tuning target data, and lifelong learning.
3 ROBOCASA365: LARGE-SCAM SIMULATION OF 365 EVERYDAY TASKS
We present RoboCasa365, large-scale simulation framework for training and benchmarking gen- cralist robots. We use the existing RoboCasa simulation framework (Nasiriany et al. 2024) as the starting ground for RoboCasa365 and make significant efforts scale up the assets, environments, tasks, and datasets. We also establish a rigorous benchmark to study sate-of-the-e art policy learning methods, which we outline in Section 4 In the following sections, we outline the components of this simulation framework: assets, scenes, tasks, and datasets.
3.1 EXPANDING THE SCOPE OF ASSETS
RoboCasa features a diverse array of objects, interactable fixtures, and appliances, with a focus on common tasks in kitchen environments. We use the existing library of 2,509 objects from Nasiriany ct al. (2024), spanning 153 object categories. In addition to these, we source an additional collection of high-quality 3D assets spanning 57 object categories. These are high-quality 3D assets sourced from artists and edited to preserve strict quality standards. We use these new objects to support new tasks and to populate various areas of kitchen scenes generally. We provide a complete inventory in Appendix C.1
In addition to the 3D assets, we significantly expand the repertoire of interactable fixtures and ap- pliances in the kitchen environment. RoboCasa (Nasiriany et al.) (024) includes total of 20 inter- actable fixtures and appliances across 4 categories: sinks, coffee machines, stoves, and microwaves. We significantly expand the scope of these assets to 456 instances spanning 12 categories. We in- clude new categories of appliances, such as toasters, toaster ovens, stand mixers, blenders, and elec- tric kettles. All of these appliances were not previously articulated under RoboCasa. We model these assets using the same format as RoboCasa, MJCF objects with annotations of the regions. For each category, we include between
articulated, including fridges, ovens, and dishwashers, which
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Pretraining
Target Scenes
Figure 2: Kitchen Scenes. Our simulation framework features 2,500 distinct kitchen scenes for pretraining (top, representative samples shown), 10 distinct target kitchen scenes (bottom, all scenes shown).
20 and 50 instances in order to ensure that there is sufficient diversity to support generalization to novel instances. We provide a complete inventory of our fixtures and appliances in Appendix C.2
3.2 DIVERSE KITCHEN SCENES
Achieving generalization in robot learning requires exposure to wide range of training environ- ments; we address this need by providing thousands of diverse kitchen scenes spanning a broad spectrum of household settings. We categorize these scenes into pretraining and target splits. Our goal is to use the pretraining kitchen scenes for large-scale data collection and synthetic data gen- eration pipelines; of our experiment evaluations. Using the terminology from Nasiriany et al. (2024), we define each kitchen scene combination of layout and style, where the layout defines the floor plan, and the style defines the specific selection of fixtures, appliances, and textures used in the kitchen. We can configure each kitchen scene use any combination of layout and scene.
use the target kitchen scenes for targeted data collection and for running most
For our target kitchens, we use the 10 layouts and 10 styles defined by Nasiriany et al. (2024) in RoboCasa, where each layout is matched with specific style, for total of 1O kitchen scenes. For our pretraining kitchen scenes, we create 50 distinct new layouts. In order to capture the distri- bution of diverse scenes, source our kitchens from 50 real-world homes with active listings on Zillow.com, real estate marketplace. These homes span diverse geographic locations across the United States. We build digital cousin (Dai et al.) (2024) replicas for each of these environments, making sure to match the floor plan closely as possible. In addition to these layouts, we create 50 distinct styles. We ensure that the pretraining and target styles do not overlap the selection of the fixtures, appliances, or environment textures used. Together, we have total combination of 50 layouts x 50 styles, for total of 2,500 pretraining kitchen scenes. We provide an overview of the pretraining and target kitchen scenes in Figure]
3.3 SUITE OF 365 EVERYDAY TASKS
We aim to provide a diverse set of tasks to support sharing knowledge across tasks and generalizing to new tasks. Nasiriany al. (2024) define two broad categories of tasks: atomic tasks, which represent the execution of a single skill, and composite tasks, which involve executing a sequence of skills. Nasiriany et al. (2024) define eight foundational skills: (1) pick-and-place, (2) opening and
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Instructiin:: Taie the tork und place to arturdd plste and place the spoce right plane.
Soot by Lattover
Figure 3: Composite Tasks. RoboCasa365 features 300 composite tasks that involve a sequence of skills. We use large language models to generate set of high-level activities, and for each activity, set of task blueprints. There are activity families (high-level categories) spanning 60 activities, which organize composite tasks based on shared functional and semantic structure. Representative tasks are shown selected activities.
closing doors, (3) opening and closing drawers, (4) turning levers, (5) turning knobs, (6) pressing buttons, (7) insertion, and (8) navigation. We adopt these skills as the basis for our atomic tasks. In addition to the 25 atomic tasks in RoboCasa, create additional set of 40 new atomic tasks to support various new appliances and new behaviors afforded by our simulator. We provide the entire list of 65 atomic tasks in Appendix E.
For our composite tasks, we follow the framework established by Nasiriany et al. (2024) where large language models to solicit task blueprints. The process follows two stages. First, prompt L.MSs to give a list of activities representing high-level groups of tasks in kitchen environ- ments. We retrieve list of the top 60 activities, such boiling water, toasting bread, brewing coffee, washing dishes, and storing leftovers, to name few. For each activity, we then prompt the LLM to provide task blueprints, which consist of the name of the task, a high-level description of the task, the objects and fixtures involved, and the sequences of skills needed to solve the task. We then proceed to write code for the tasks based on these blueprints. We use 83 of the existing composite tasks from RoboCasa and generate an additional set of 217 new composite tasks, for total of 300 composite tasks. We outline the full list of activities and representative composite tasks Figure In total, our benchmark includes 365 everyday tasks: 65 atomic tasks and 300 composite tasks. Out of these, 220 require mobile manipulation, while 145 can performed without mobility.
3.4 DATASETS
We provide a large collection of robot datasets covering all of our tasks. Broadly, our datasets are divided into two categories: pretraining datasets for data from the pretraining scenes, and target datasets from the target scenes.
3.4.1 PRETRAINING DATASETS
Out of the 365 total tasks outlined Section 3.3 our pretraining data covers 300 tasks, with 65 atomic tasks and 235 composite tasks. For each of these 300 tasks, we collect 100 human demon- strations per task via robot teleoperation. This results in 30k human demonstrations total for pre- training. For data collection,
the Franka Panda Emika robot, equipped with Omron mobile base (Haviland et al. 2022), and in principle, our simulation framework can support data collection with other mobile manipulators and humanoid platforms.
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We also the MimicGen generation system (Mandlekar et al. 2023 to generate laage-scale syn- thetic data across 60 atomic tasks. For each task, we use the 100 human demonstrations previously collected seed demonstrations, and generate 10k demonstrations, effectively scaling data x00
3.4.2 TARGET DATASETS
For our target data, we choose 50 representative ones of the 365 tasks, grouped into three splits:
Atomic (18 tasks): We include 18 representative tasks out 65 total atomic tasks in the benchmark.
Composite-Se (16 tasks): We choose 16 representative composite tasks spanning 16 activities. These include mix of short and long-horizon tasks, with some involving subtasks and the longest task involving 15 subtasks.
Composit Unseen (16 tasks): To test the effect of our pretraining data, we also choose 16 composite tasks that are unseen in the pretraining data. These tasks are of similar diffi- culty to the composite seen tasks, but focus on another distinct set of 16 activities.
We list the entire set of 50 target tasks in Appendix F.2 For each of these tasks, we collect 500 human demonstrations via robot teleportation, for a total of 25k demonstrations.
3.4.3 DATASET STATISTICS
We provide a high-level overview of our datasets in Appendix Our pretraining synthetic demon- stration dataset spans the highest amount of data, with 1615 total hours, followed by human pretrain- ing data (404 hours), and then human target data (208 hours). In Figure we report the distribution over the number of subtasks required for each of our 365 tasks. Most tasks require one or two sub- tasks, but there are a few tasks that require 15 or more subtasks to complete. In Figure (4b] we report the distribution of episode lengths across all pretraining and target human data (55k episodes), The majority of episodes range from 10 to 60 seconds, with long tail end for longer horizon episodes, some going beyond? 3 minutes.
Distribution of Required Subtasks per Task
Distribution of Demonstration Episode Lengths
Number of Demos
8000
Task Count
60
40
4000
20
6.0
10-30
10000
ooste
5
15 16
Number of Subtasks
Demo length (seconds)
(a) Distribution of required subtasks per task.
(b) Distribution demonstration lengths.
Figure 4: Distribution of task lengths (by number of subtasks) and dataset episode lengths (by number of seconds). We observe a long tail of tasks and representing long- horizon behaviors.
EXPERIMENTS
In our experiments, we conduct a systematic study understand the key factors that influence training generalist robot policies. To this end, we design a comprehensive suite of benchmarks aimed at answering the following questions:
How well do generalist robot models perform when trained on large multi-task datasets?
2. What role does pretraining data play, and to what extent can it improve learning of down- stream tasks?

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3. How effectively learn new tasks in lifelong learning settings?
4. How does the scope and composition of pretraining data impact learning downstream tasks?
4.1 MULTI-TASK TRAINING
We begin by investigating how state-of-the-art methods perform when trained on massively multi- task datasets. This evaluation is critical step toward developing generalist robots that can not only master a wide range of behaviors but also adapt to entirely novel tasks beyond their training data.
We train languugge-oonditioned datasets outlined in Section [.4.1] Each task has 100 human demonstrations, for a total of 30k demonstrations. Our experiments feature four state-of-the-art methods: Diffusion policy (Chi al.) 2023 To (Black et al. 2024), To.5 (Physical Intelligence et al. 2025). and GROOT N1.5 (NVIDIA al. 2025).
bason-based policies the mixture of 300 pretraining human
We train a multi-task language-conditioned policy for each method. We use the pretrained check- points released publicly for иo. Wo.5. and GROOT N1.5 as the base model for training our models. We provide details on the training protocols for each method in Appendix G
We evaluate on the 50 tasks outlined in Section 3.4.2 Atomic, Composite- Seen, and Composite-Unsee
Note that the Compos i ite Unseen tasks represent unseen tasks in the pretraining data; our evaluation for these tasks is zero-shot, aimed at understanding generalization to novel tasks. We evaluate in the pretraining kitchen scenes for each task and report average task completion success rates across methods. See Appendix for details on the evaluation protocol.
Task Split
Diffusion Policy
1T..5
GROUT N1.5
At otiic
15.7
36,3
39.6
43.0
Compos Composite-Seet iten Composite-Unseen
0.2
5.2
7.1
9.6
1.25
0.7
1.2
4.4
Average
6.1
15.0
16.9
20.0
Table 1: Multi-task Training Results. We compare state-of-tho-an policy learning approaches on our human pretraining data across 300 tasks, and report task success rates (%) across seen and unseen tasks. We see that learning composite tasks is more challenging, and that performance suffers when evaluating on unseen tasks.
We report results in Table T Overall, we see that across all methods, learning At omi e tasks is the easiest, followed by learning Compos i t -Seeen tasks and Compos i tu-unseen tasks. This is reasonable, as the Atomic tasks are shorter-horizon tasks that present fewer learning challenges for imitation learning (Ross al., 2011), and the lower performance on Composite seen tasks is due to the fact that the model has never been trained on these tasks. Overall, GROOT N1.5 performs the best among all methods, followed by To.s, No, and finally Diffusion Policy. They show non-zero success rates on Composite Unseen tasks, sign of stronger generalization abilities. Diffusion Policy performs the worst, highlighting how high-capacity vision-language-action models can better fit large, diverse multi-task robot datasets. While our multi-task learning experiments show that GROOT N1.5 outperforms other baselines, we do not claim that it is conclusively the superior method. Performance can be influenced by many factors, including the amount of compute used (e.g., batch size), data composition, and whether the visual or language backbones are fine- significant opportunity for future methods improve upon these results.
tuned. Overall,
4.2 FOUNDATION MODEL TRAINING
In our next experiment, we are interested in studying foundation model training, i.e., training with our pretraining datasets, followed by fine-tuning on our target datasets. This learning paradigm has been established by numerous prior works in robotics (Black et al.) 2024; VVIIIA et al., 2025), with evidence that pretraining can aid learning downstream tasks in more robust and data-efficient manner. In our experiments, our pretraining data includes human datasets across 300 tasks (411 hours), and synthetic data across 60 atomic tasks (1,615 hours), while our target data includes human datasets across 50 tasks (208 hours). Out of the 50 target tasks, 34 are also represented in the human pretraining data (At omic c and Compos i te e-Seen tasks), and the target data includes an additional 16 composite tasks that not seen in the pretraining data (Compos te (ee-nnsen). We

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Pretraining Only
Target Only
Pretraining + Target Post-Training
Task Type
10%
30%
100%
10%
30%
100%
Atemic
41.9
38.7
50.6
60.6
56.9
59.1
68.5
Composite- Seen
0.0
11.0
22.7
35.0
25.4
34.6
40.6
Composite-Unsent
0.2
11.2
27.5
33.3
22.7
30.8
42.1
Average
15.1
21.0
34.3
43.7
35.9
42.2
51.1
Table 2:
Foundation Model Training Results. Comparing the impact of training on pretraining and target
datasets on learning downstream tasks. The performances are measured by average task success rates (%).
followed by fine-tuning independently on three separate target split datasets (Atomic c, Composite -eenne,eeeen,,,
first train on all of our pretraining datasets (see Section 3.4.
Cemposste-Unnes Section 3.4.2). We compare learning on different amounts of target data, with 50, 150, and 500 demos per task, representing 10%, 30%, and 100% of the total target data.
Unless otherwise noted, we use GROOT N1.5 as the model for these experiments and all subse- quent experiments. We open source all models for the community to benchmark all methods. We compare pretraining only, target task learning only, and pretraining followed by post-training on tar- get data. After training, we evaluate the model across the 50 target tasks in the target kitchens. See Appendix G for a detailed discussion of the training and evaluation protocols. We report experiment results in Table
We see that with pretraining alone, the model per- forms over 40% on the atomic tasks but performs very poorly on the composite tasks. For target learn- ing only, we see more capable policies. However, they require high amount of data to be performant. Using pretraining yields significant improvements in model performance. These gains especially pro- nounced for the Composite -Unseen tasks (see
Foundation Model Training Results
50
Rate
45
30
Task
25
Table 2 We visualize the improvement in per- formance in Figure [5] visualizing the average task success rates from Table 2 We observe roughly 3x improvement in data efficiency, i.e., pretraining helps achieve roughly the same performance as tar- get learning only with 3x higher number of target task demonstrations. a rigorous robustness evaluation and analyze the ef- fects of different factors on performance.
20
Phitraning
Tarpet only
15-0
Phetraning Target Poab-training
à sori0s) Quantity of Target Demos per Task
190330%
5001100%)
Figure 5: Foundation Model Training Results. Pre-training enables more effective learning of downstream tasks with significant gains in data ef- ficiency.
Appendix H.2 we present
4.3 LIFELONG LEARNING
In contrast to the conventional two-stage paradigm of pretraining followed by post-training on target data, real-world robots must often acquire skills continuously. This setting, known as lifelong learning, involves learning tasks over a sequence of phases. The central challenge is leveraging prior knowledge learn new tasks while retaining previously acquired skills. We design a lifelong learning benchmark to assess these capabilities. In our experiments, we learn a series of tasks over four phases. Each phase involves learning progressively longer horizon tasks. Phase involves learning 65 atomic tasks, Phase 2 involves learning 20 new composite tasks with 2 or 3 stages, Phase 3 involves learning 20 new composite tasks with 4 or stages, and Phase 4 involves learning 20 new composite tasks with 6 or more stages. We define "stage" as the invocation of one of the robot skills defined by Nasiriany et al. (2024), such as aek--and-,,ace, turning knobs, and navigation. We use pretraining datasets for these phases; Phase includes all human and MimicGen datasets for atomic tasks, while Phases 2, 3, and feature human datasets.
For each phase N, we take the model previously trained from phase N - 1, and fine-tune it for data pertaining to the tasks in phase N. After training completes for phase N, we run evaluations for tasks from phase through phase N in the pretraining kitchens and report results. We report results in Table 3 We make two distinct observations. First, we see that the success rates steadily drop as learn progressively longer-horizon tasks in each new phase (see the diagonal entries in the table).
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Phase
Atomic Tasks
2-3 Stage Tasks
4-5 Stage Tasks
6+ Stage Tasks
Phase
41.5
Phase 13.9
24.5
Phase
13.9
4.8
11.3
Phase 10.6
1.7
2.7
4.3
Table 3: Lifelong Learning Results. We train across four phases with progressively longer horizon tasks. After each phase, we report task success rates (%) across all tasks seen in the current and previous phases.
This is intuitively the case, as learning longer-horizan tasks can demand higher data requirements. Second, we see that the performance on previously learned tasks steadily drops with each new phase. This highlights the catastrophic forgetting problem, i.e., performance degrades on prior tasks if the agent does not continue to train on them in subsequent phases. Overall, this experiment highlights the current challenges with lifelong learning and is useful testhed for improving upon these results.
4.4 PRETRAINING DATA COMPOSITION STUDY
In Section showed that pretraining brings forth significant improvements in data efficiency for learning downstream target tasks. In this section, we run experiments to further understand how the composition of pretraining data affects downstream performance. In foundation model training experiments, we used all of the available pretraining data, comprising human data from 300 tasks and MimicGen data across 60 tasks (Human300+ MG60). We compare variant that does not include MimicGen data and only includes the human data (0uman330). To better understand the role of task diversity in the pretraining data, we compare two variants that include human data from 50 tasks (Human50). These 50 tasks include the At Atomic c and Compos iopooitte teeossttt-eennn tasks, as well as an additional randomly selected set tasks. Finally, we compare with the case with no pretraining data. Our pretraining and target protocol are identical to the process in Section 4.2] We specifically run two separate sets of experiments, one for the low-data regime with 10% of the target data, and one for the high-data regime with 100% of the target data.
Pretraining Data
Target Data
No Pretruining
Human50
Human300
Human300 MG60
Atomic (108)
38.7
$2.0
57.0
56.9
Cumposite-Ssen (10&)
11.0
26.2
28.7
25.4
Composi te-Unseen (108)
11.2
Average (10%)
21.0
34.7
40.0
35.9
Atoomic (1008)
60.6
68.1
70.0
68.5
Camposite-Seen (1008)
35.0
41.0
41.2
40.6
Composite-Uneee Unseen (100%)
33.3
38.5
44.0
42.1
Average (100%)
43.7
52.5
Table 4: Pretraining Task Diversity Results. report task success rates (%) and compare the downstream
effects of training on different mixtures of pretraining data.
We report evaluations in target kitchens in Table Compared to training all pretraining data (Human300- + MG60), we find that training on just the human data (Human300) yields better down- stream learning results. Although MimicGen enables the large-scale generation of synthetic trajec- tories, we find that the resulting demonstrations vary in quality. Developing methods that can more effectively leverage such large, mixed-quality datasets is an important direction for future work. Comparing the Human50 and Human300 settings, we see that increasing the number of pretraining tasks can enable significant improvement downstream target tasks, especially for the low-data regime target data setting. Notably, the biggest gains are seen for the Compos te-Unseen tasks, suggesting that increasing the scope task diversity is especially beneficial for learning novel tasks.
In addition to task diversity, we study the effects of scene diversity in pretraining on downstream performance. We report these results in Appendix
4.5 REAL-WORLD EXPERIMENTS
We conduct additional set of experiments to examine the utility of our benchmark for downstream real-world applications. Our real-world setup uses the DROID Panda arm (Khazatsky et al.) 2024) with three cameras.

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We examine four tasks in real kitchen:
ClsseEleetricceettleliio close the electric kettle lid
PiciPlaaceTsstteevvveeoCCCossss place the item from the toaster oven to the
counter
TickelaccCoutteerooabiice. place the object from the counter to the cabinet
PlaceOnDi shRack: a longer horizon task, involving placing two items from the sink onto the dish rack.
We collect 30 demonstrations for each of the first three tasks, and 50 demonstrations of the last task, for a total of 140 real-world demon- strations. We compare the following settings:
Real Only: we train the GROOT N1.5 model on the real- world demonstrations (140 demonstrations):
Sim-and-Be Real (Ours): first mid-train the GROOT N1.5 model on our simulation tasks (we use data from the 150 highest performing tasks in simulation), and then co-fine- tune the model on the real-world demonstrations and cor- responding data for the four real-world tasks in simulation.
Following best practices for sim-and-real alignment from Mad- dukuri et al. (2025), re-render our simulation datasets to match the camera views of the real setting, facilitating improved transfer. After training, evaluate each model in the real world, where we conduct 20 trials per task. We report task success rates in Table
Figure Real-Robot Platform. Our real-world setup features a Panda robot arm a real kitchen.
CloseElectric KettleLid
PichPlaceConnter Toesterrenee ToCabinet
PlaceOn DishRack
ToCounter
Avg
Real Only
70
70
52
55
61.8
and (Qurs)
70
Table 5: Real-world evaluations. Across four real-world tasks, we compare training real-world data only training on mixture of simulation and real-world data. By additionally using simulation data, outperforms training on real-world data only by an average task success rate 18.1%.
Overall, the Real Only model achieves 61.8% average success rate, while Sim-and-Red Real training reaches 79.8%, substantial improvement. This highlights the value our simulation benchmark for both algorithm evaluation and real-world policy leariin..
CONCLUSION
We presented RoboCasa365, large-scale simulation framework for training and benchmarking generalist robot models. RoboCasa365 provides 2,500 realistic kitchen environments, 365 everyday tasks spanning over 50 activity categories, and over 2,000 hours of robot interaction data, making one of the most diverse simulation resources to date.
Using this benchmark, scale, foundation model learning, and lifelong learning. Our experiments show that (i) generalist policies trained on large multi-task datasets can acquire broad competence but still face challenges with long-horizon tasks, (ii) pretraining data significantly improves downstream learning, with both scale and task diversity playing key roles, and (iii) lifelong learning remains an open challenge, with substantial trade-offss between acquiring new tasks and retaining prior knowledge.
conducted systematic study along three axes: multi-task learning at
RoboCasa365 opens several avenues for future work. First, the benchmark is currently limited to kitchen environments, raising the question of how well findings transfer to other household set- tings or broader domains. Second, while the dataset is large, it does not capture the full sensory and physical complexity of the real world, and bridging the gap between simulation and real-worr world deployment remains significant challenge. Addressing these limitations will be an important di- rection for future research.
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ACKNOWLEDGMENTS
We thank Qi Wang for his valuable assistance in coordinating project resources, particularly in data collection and asset preparation. We thank Steve Xie and the LightWheel team for their close col- laboration in providing simulation assets and support with data collection. We also thank Ajay Man- dlekar, Zi-ang Cao, and Kevin Lin for their assistance with running benchmarking experiments. Part of this work was done during Soroush Nasirian''s 's internship at NVIDIA Research. This work was partially supported by the National Science Foundation (FRR-2145283, EFRI-2318065 the Office of Naval Research (N00014- 24- 1-2500,, the DARPA TIAMAT program (HRO01 1----9-042225 the and the KIST-UT collaboration (UTAUS- FA0000557000
Army Research Lab (W91 It was also supported by the Institute of Information & Communications Technology Planning & Evaluation (ITPP) grant funded by the Korean Government (MSIT) (No. RS2024-00457882, Na- tional AI Research Lab Project).
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A USE OF LARGE LANGUAGE MODELS
We use the aid of large language models to create activity labels and task blueprints, using the process outlined by Nassiiany et al. (2024). We also use large language models for soliciting writing feedback for parts of this manuscript.
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B SIMULATION INFRASTRUCTURE
B.1 PHYSICS AND RENDERING ENGINE
RoboCasa365 is built on top of RoboSuite (Zhu et al., 2020), which uses the MuJoCo physics en- gine (Todorov 2012). While MuJoCo's core physics computations are CPU-based we leverage GaU-b rendering. RoboCasa365 simulates at 20 Hz, with the simulation running approximately real time, slightly faster slower depending on scene complexity and hardware specifications. Multiple asynchronous environments can be run in parallel, allowing overall throughput to scale with the number of available CPU cores and GPUs.
B.2 ACTION SPACE
We adopt the underlying controller from RoboCasa (Nasiriany et al., 2024). Specifically, we use an Operational Space Controller (Khatib 1995) running at 20 Hz that commands the arm through seven action dimensions: three for translation, three for rotation, and one for gripper opening and closing. In addition, we include five action dimensions for mobile base translation and rotation, torso height control, and an action mode that gates mobile base control.
C SIMULATION ASSETS
C.I 3D OBJECTS
We have added new objects across 57 categories: aluminum foil, basket, blender jug. cheese grater, chicken drumstick, cinnamon, colander, cookie dough ball, cream cheese stick, digital scale, dish brush, flour bag, glass cup, honey bottle, hotdog bun, ice cube, ice cube tray, jar, juicee, kehab skewer, lemon wedge, lettuce, marshmallow. mayonnaise, measuring cup, mustard, non electric kettle, oil and vinegar bottle, oven tray, pancake, paprika, peeler, pickle slice, pitcher, pizza, pizza cutter, placemat, pot, reamer, salt and pepper shaker, sandwich bread, saucepan, saucepan lid, shrimp, soap dispenser, spray, strainer, straw, sugar cube, syrup bottle, tomato slice, tongs, tupperware, turkey slice, turmeric, whisk, and wooden spoon.
C.2 INTERACTIVE FIXTURES AND APPLIANCES
We report an inventory of all fixtures and appliances in Table
Category
Unique models
Blender
Coffee machine
48
Dishwasher
25
Electric kettle
25
Fridge
Microwave
Oven
21
Sink
49
Stand mixer
Stove
Toaster
44
Toaster oven
47
Total
456
Table 6: Inventory of fixtures and appliances
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D SCENES
We build 50 kitchen layouts modeled after 50 homes on sale on Zillow.com. These homes span locations in the Bay Area (California), Austin (Texas), Denver (Colorado), Boston (Massachusetts), and Atlanta (Georgia).
E TASKS
E.
ATOMIC TASKS
We have 65 atomic tasks: sajusttToasseeeveeneaaerrrruu
adjustWaceerTeenperatueee htw.ee/eeeeeeeeeeeaooooeeeeeeeir
CloseCabinet,
GlossFridgge
wwradMieeeeeaee xerHead,
TurnOnStove,
E.2 TARGET TASKS
We provide an overview for the target tasks across Tables
g and
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Activity
Task
# Sub- tasks
MoMa
Description
req.
Close the lid blender by securely placing the lid on top.
Atomic
CloseBlenderLid
1
No
Atomic
CloseFridge
1
No
Close the fridge door(s).
Atomic
CloscToasterrvennDorr
No
Close the toaster oven door.
Pick the mug from the counter and place it under the coffee machine dispenser.
Atomic
CoffeeSetupMug
No
Atomic
NavigateKitchen
Yes
Navigate to the [kitchen location].
Atomic
OpenCabinet
No
Open the cabinet door(s).
Atomic
OpenDrawer
No
Open the [left/rght]] drawer.
Atomic
OpenStandMixerRead
No
Open the stand mixer head.
Pick the item from the counter and place in the cabinet.
Atomic
FickPlacecounterTT
No
Cabinet
Pick the item from the plate and place it in the pan.
Atomic
PickFlaceCounterTTo Stove
No
item the drawer and place on counter.
Atomic
FickFlaceDrawerto
No
Counter
Pick the item from the sink and place on the container located on the counter.
Atomic
PickPLaceSinkTo
Counter
Atomic
PickP LaceeaacTTaster111
No
Place the toasted item plate.
Counter
Atomic
S1ideDishwasherRacck
No
Fully slide the top dishwasher rack [in/out].
off the [hurner location] burner the stove.
Atomic
TurnOffStove
No
Atomic
TurnOnElectricketttee
No
the electric kettle. No Turn the sink faucet.
Press down the lever
Atomic
PurnOnMicrowave
No Press the start button on the microwave.
Atomic
TurnOnSinkraucet
Figure 7: Post-training Atomic-Seen Tasks (18)
Note: The "MoMa req." column indicates whether the task requires Mobile Manipulation base navigation.
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Activity
Task
MoMa
Description
Sub- tasks
req.
Take straw from the drawer front place inside the glass cup on the dining counter.
Serving beverages
DeliverStraw
4
Yes
Start the toaster. Once the lever pops up. take the bread to plate on the dining counter.
Toasting bread
GetlloastedHread
Yes
Pick the kettle from the counter and place it stove burner. Then turn the burner on.
Brewing
KettleBciling
Pick up the items from the counter, place them in the dishwasher, and close the dishwasher door.
Loading dishwasher
LoadDishwasher
Place two identical items of cach object in cach tupperware on the nearby counter, to pack identical lunches.
Packing lunches
PackIdentical Lunches
Yes
Pick the pan and sponge and place them into the sink. Then
Washing dishes
PreSoakPan
No
the water.
Pick the mug from the cabinet, place it under the coffee machine dispenser, and press the start button.
Brewing
PrepareCoffee
No
Turn on the sink and manuever the spout to wash all locations of the sink basin.
Cleaning sink
RinseSinkBasin
up the sponge from counter clean the cutting board by briefly scrubbing or pressing down the cutting board. Once finished, release the sponge.
Sanitizing cutting boards
ScrubCutting Board
Grab the pan from the cabinet and place it on the [burner location] burner on the stove. Then place the item the stove and turn the burner on.
Frying
SearingMeat:
Pick up the knife from the drawer and place it on the cutting board. Then place the meat from the plate the cutting board.
Slicing meat
SetUpCutting station
Yes
Pick up the bowls on the counter and stack them top of one another in the open cabinet. Place the smaller bowl on top of the larger bowl.
Organizing dishes and containers
StackBowls Cabinet
Yes
Pick the item from the sink and place in the howl. Then pick the howl and place it in the microwave. Then close the microwave door and press the start button.
Steaming food
SteamIn Micrewave
Yes
Put the items in the pot. Retrieve the spatula and lightly stir the vegetables in the pot.
Sauteing vegetables
StirVegetables
Yes
Pick chicken drumstick and item from their plates and place them the bowl. Then put the howl in the fridge
Storing leftovers
StoreLeftovers
Yes
InBow]
Wash the lettuce the sink by running water over it.
Making salads
WashLettuce
No
Figure 8: Post-training Composite-Seen Tasks (16)
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Activity
Task
# Sub- tasks
MoMa
Description
req.
Open the cabinet, pick up the item from the cabinet and place it in the hasket. Then the basket the dining counter.
Setting the table
ArrangeRread Basket
5
Yes
Pick the kettle from the counter and place it on the tray. Then pick the mug from the cabinet and place it the tray. Then close the cabinet doors.
Brewing
ArrangeTea
3 No
From the different types of pastries the counter, select croissant and place it on the cutting board.
Making toast
Bread
2
Yes
Then retrieve a jar of jam from cabinet and place alongside the croissant on the cutting board.
Selection
Put the shaker and condiment bottle from the counter next to counterparts in cabinet.
Arranging condiments
Categorize Condiments
2
No
Place the appropriate cutting tool for cutting the item skin on the cutting board.
Chopping vegetables
CustingTool Selection
2
No
Take the strawberry from the fridge and place top of the pancake, located on the dining counter.
Garnishing dishes
Garnish Pancake
4
Yes
Gather all objects into cabinet and the glasses and bowls to opposite sides.
Arranging cabinets
Gather
4
Yes
Tableware
Pick the kebab skewer and baguette bread, and place them inside the toaster oven. Close the toaster oven door and start by setting the timer.
Preparing sandwiches
HeatKebab Sandwich
6
Yes
Grab a lemon wedge from the fridge and one ice cube from the ice bowl, and put them in the glass of lemonade.
Adding ice to beverages
MakeIce Lemonade
5
Yes
Pick up the pan and dump the vegetables in it onto the plate. Then return the pan to the stove.
Serving food
PanTransfer
3
No
Place one bun and one sausage the bowl on each plate.
Portioning meals
PortionHot Dogs
4
Yes
Move the plastic bottles in the middle to the plastics group, and the glass bottles in the middle to the glass group.
Organizing recycling
Recycle
3
Yes
BottlesBy Type
Take the meat container that has the meat item(s) and place it the second highest rack of the freezer. Then vegetable container that has vegetable(s) and place on highest rack of the freezer.
Managing freezer space
Separate FreezerRack
7
Yes
Open the microwave, place the bowl with waffle inside the microwave, then close the microwave door and turn it on.
Reheating food
WaffleRcheat
Yes
Put the colander in the sink, put the item in the colander,
Washing produce
WashFruit Colander
4 No
on the sink faucet and pour water over colander.
Pick the item and place it the digital scale for weighing, and close the cabinet.
Measuring ingredients
Weigh Ingredients
2
No
Figure Post-training Composite-Unseed Tasks (16)
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Figure 10: Camera Images. Camera images from three views (rendered resolution are fed into the model.)
F DATASETS
We present overview of our datasets in Table 7
Setting
Num Tasks
Num Scenes
Demos per Task
Dataset Size (hrs)
Pretraining (Human)
300
2500
100
404
Pretraining (MimicGen)
60
2500
10,000
1615
Target (Human)
208
Table 7: Dataset statisties across pretraining and larget settings.
For each demonstration, we store the language instruction, proprioceptive information (robot base pose, robot end effector pose, gripper state information), images from three cameras (wrist camera, left third-persee camera, right third-person camera), and the actions.
G
POLICY LEARNING
G.1 MODEL ARCHITECTURES AND TRAINING PROTOCOL
Our experiments focus on training vision-based models. The model takes as input combination of low-level proprioceptive information (base pose, end effector pose, gripper state), task instruction language, and camera images (one wrist camera image and two third-person camera images).
Each of the camera images is at 256 x 256 resolution, and we show examples of the camera views in Figure 10
We experiment with four models:
Diffusion Policy. Diffusion Policy models trajectory generation as conditional denoising process action space, recovering actions from noisy expert trajectories handle multi-modal robot behaviors. We use lopen source diffusion policy codebase and add language conditioning by fusing CLIP-based language embeddings (Radford et al. (02I)) with the ResNet visual encoder via FiLM conditioning layers (Perez al. 2018). We use the transformer diffusion variant, with 12-layer transformer with an embedding dimension of 512. We train the model with a batch size of 192 and train for 250k steps for the multi-tast learning experiment.
To To.5- To and To.5 are vision aaioo--lnnaag-acttioo models which use PaLI Gemma (Beyer et al. 2024) as the underlying VLM and fuses action expert to output robot actions via flow matching (Lipman et al.] (003)). We use the official open source repository. We use the default full fine-tuning configuration, and
batch size of 64 (the highest batch size we fit on GH200 GPU). For the multi-task learning experiment, we train the model for 75k steps (48 hours of training time on a GH200 GPU).
GROOT N1.5. GROOT N1.5 is a iasiionlangugge-ee action model which uses a systeml-systemm architecture, with the Eagle2 VLM (Li et al. 2025) serving the high-level encoder (system2) and
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action decoder to produce actions via flow matching (systeml). We use the official open open source repository For all experiments, we freeze the vision encoder and language encoder (which are the default settings from the open source codebase), and size we can on a GH200 GPU). For the multi-task learning experiments, we train for 120k steps. For the foundation model training experiments, we pretrain for 80k steps and fine-tune data for 60k steps. Finally, for lifelong learning experiments, we train stagel I for 100k steps, followed by 60k steps for all subsequent stages of training. Generally, we find these settings to be sufficient to allow for model convergence.
batch size of 128 (the highest batch
target
G.2 EVALUATION PROTOCOL
After training, we evaluate the model a specified checkpoint on suite of evaluation tasks. For each evaluation task, we run 30 trials for specified maximum number of timesteps (the maximum duration is task-dependent). during this duration the agent achieves the task success condition (binary condition), the episode is counted as a success; otherwise, failure. We report the average success rate across tasks.
H ADDITIONAL EXPERIMENTS
H.1 PRETRAINING SCENE DIVERSITY
Our pretraining data spans 2,500 kitchen scenes (50 layouts 50 styles), and we compare restricting pretraining data to 25 scenes (5 layouts x 5 styles), and scenes (5 layouts x 1 style). To run fair comparison across these settings, we use MimicGen to generate demonstrations for each setting, generating data across 17 atomic tasks in pretraining kitchens. We run zero-shot evaluations on the 10 fixed target kitchen scenes, and also try learning on the atomic target data with 50 demonstrations per task. See Taalle8 for results. For zero-shot evaluation, notable performance gains as the number of pretraining scenes increases. These gains also hold in subsequent target task fine-tuning, highlighting the need for diverse pretraining data.
observe
Pretraining Data
Soenes
25 Scenes
2500 Soenes
Zero-shot Evaluation
29.6
39.6 56.7
44.7
Fine-tuning on Atomic Target Data (10%)
53.3
62.4
Table 8: Pretraining scene diversity results. Increasing the composition of scenes in pretraining data improves downstream task performance.
H.2 ROBUSTNESS EVALUATIONS
In order to examine the generalization capabilities endowed by pretraining on our data, we perform a set of robustness evaluations on the GROOT N1.5 model trained on the full pretraining and target mixture. We perturb an aspect the model's input and evaluate on our Composite teppossieeeee Seen and Composi teettee-Ueeeeeeeee tasks. Specifically, we look at the following perturbations:
Novel Language: We prompt an LLM for novel but semantically similar task instructions.
Novel Joint Angles: We sample Gaussian noise and add it to the starting joint angles of the robot.
Novel Base Pose: We sample Gaussian noise and add it to the starting position and yaw of the robot base.
Novel Camera Pose: We sample Gaussian noise and add to the default third-person and wrist camera poses.
We find that the model robust to language variations, but can suffer from novel camera poses, joint angles, and base poses.
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Table 9: Evaluation of robustness under different perturbations.
Task Split
No Perturbation
Novel Langunge
Camera Perturbations
Initial Joint Noise
Initial Rase Pose Noise
Compesite-Seet composite-bre Unsenn
42.1
39,2
31.5
32.1
30.2
H.3 JOINT CO-TRAINING OF PRETRAINING AND TARGET DATA
As an extension to the foundation model training experiments, examine separate variant in which we train on all pre- pre-training data and 100% of the target data jointly in one single phase. We trained the model for 120k steps and report the resulting task success rates in the target kitchens as follows:
Atemic-Seens 44.1%
Composite-Seen 9.0%
Homposite-Unseee 11.7%
Average: 22.5%
Compared to our two-stage learning framework (pretraining first, followed by fine-tuning on target data), performance under this co-training regime is substantially lower. This result underscores the importance of a dedicated fine-tuning phase for learning highly performant policies tailored to the target tasks.
H.4 LORA FINE-TUNING
For the multi-task learning experiments in section 4.2, GROOT N1.5 with LoRA fine-tuning, trained for the same number of steps, batch size, etc, as the full fine-tuning variant. The results are follows:
Table 10: Policy success rates (%) comparing full LoRA fine-tuning for GROOT N1.5.
At Atomi-Seeen
Composite-Seen
Composite-Unseen
Average
Full fine-tuning
43.0
4.4
20.0
LoRA fine-tuning
2.4
0.8
1.2
Full fine-tuning is critical to model performance.
I ADDITIONAL ANALYSIS
1.1 FOUNDATION MODEL TRAINING ANALYSIS
We break down the per-task task performance for the best performing variant, pretraining followed by fine-tuning on target data (on 100% of data), in Table I1 Among the Atomi c Seen tasks, the pernst-erformnn tasks are TurnOffSt. precision and dexterity. However, for Composite-Seds and Composite-Unsens worst-performing tasks span many diverse characteristics. We run qualitative analysis, outlining common failure modes for the tasks where the model performs at a 30% or less success rate:
and
which involve high tasks, the
SteamT nMi crowave: difficulty placing the bowl in the microwave, either placing on the edge of the microwave or dropping the bowl in the air right before placing it in the microwave
Seari ngMeat: typically does not on the incorrect stove burner; or does not place the pan on a valid location on the stovetop
the stove burner correctly, attempts
PackTdent ica Lunches: unreliable picking from fridge; not moving to the tupperware to place items; placing items in wrong tupperware
PrepareCoffee: often does not place the coffee mug correctly under the coffee machine
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reliverStraw: range of failures: difficulty opening drawers, difficulty transporting straw (dropping it), difficulty placing straw into cup
GetToastedBread: often does not press down on lever fully; sometimes presses down lever but then acts randomly
Portionoot Dogs: unreliable picks from crowded bowl; unreliable place by placing item on counter instead of plate; placing items on the wrong plate
PanTrans generally picks up the pan but does not reliably flip the contents of the pan into the plate; this a dynamic task that's quite unique, does not have much overlap with other tasks in the benchmark
ch: often fails to pull out the toaster rack; other times often after placing the first item on the rack inadvertently pushes the rack in by accident and does not place the second item in
He
Categori zeCond i ments: pick and place is not reliable, does not place matching condiments next to each other
often fails reliably place the tupperware into the freezer, as the freezer is tight space
eeezatterreezeRaaee
GatherTabl ewa re: must locate the other mug from the kitchen, and bring it back; the navigation ability here is not reliable; also sometimes does not place the mug inside the cabinet, drops it in the air without reaching far into the cabinet.
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Task
Success Rate (%)
Stages
MoMa Required
At Atomic-Ses
TumoonElecticKettee
No
OpenStandMixerrHeal
90
No
Close TosserttteOvee
87
No
OpenCabinet
87
No
SlideDishwasherRcck
No
PickPlace ToasterToCounter
73
No
Turn Mumorniieowvvee
70
No
OranDr
70
No
PickPlaceSinkToCCoo ToCunnter
No
PestPlacCounteTTSSSoo ToSooe
No
CloseFridge
67
No
63
No
63
No
Mug
No
Navigate
Yes
PrakPlacDrrawerr ToConterr
50
No
TurnOffStove
37
No
CloseBlenderl.id
37
No
Composi te-
StackBowlsCabinet
2
Yes
PreSoakPan
3
No
ScrubCutingBoard
70
2
Yes
WashLettuce
67
2
No
BisseSinkBBain
60
2
No
KettleBolling
2
No
LoadDishwasher
3
No
StoreLeftoverssnnoww/
43
5
Yes
SelUpCattingStation
33
2
Yes
StirVegetables
33
4
Yes
SteamInMicrowave
SearingMeat
27
3
Yes
mnckldenticalLLnnhes
17
15
Yes
PrepareCoffee
13
2
No
DeliverStraw
Yes
GerToastedBread
4
Yes
Composite-Unsert
Recycle Typee
3
Yes
WefftcReheat
83
4
Yes
Braassttr
77
5
Yes
WeighIngredients
67
2
No
BreadSelection
CuttingTooole Seolecetonnnn
47
2
No
GamishPancake
47
4
Yes
ArrangeTea
43
3
No
WashFruiCClandee
No
Makelce.cmooaade .emonade
5
Yes
PortionHotDogs.
23
4
Yes
PanTrraste Transfer
20
3
No
Seandicha
13
6
Yes
GllegorizConddmment
2
No
SeparateFreezzennn
10
7
Yes
MatherTablewate
4
Yes
Table 11: Foundation Model Training Results.
25


Reviews:
[{"review_id": "kgmrYQVNc9", "reviewer": "Reviewer_rSty", "summary": "This work introduces RoboCasa365, a simulation benchmark for household robotic manipulation tasks. The benchmark is an extension of RoboCasa, and introduces 365 everyday tasks across 2500 diverse kitchen environments. The authors use this benchmark to evaluate multi-task learning, foundation policy model training, and lifelong learning. Extensive experiment results on the proposed benchmark provide insights into how task diversity, dataset scale, and environment variation influence generalization in robot manipulation.", "questions": "1. How are the robot arm controllers implemented, and what's the control frequency? What's the action space -- is it end effector poses and gripper open/close?\n\n2. How is the task conditioning implemented in multi-task policy training? The paper mentioned it's language-conditioned, but is there more investigations on how to embed the language inputs or alternative ways to do task conditioning.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The additional assets and scenes significantly increases the task diversity of the original RoboCasa benchmark. The simulated kitchen environments have highly realistic and diverse layouts; and the authors ensure the task activities are from a diverse set of categories.\n\n2. The paper presents systematic experiments to study key factors in training generalist robot policies. The authors have dedicated effort into implementing various policy training baselines and compared results on both seen and unseen tasks. \n\n3. Good presentation quality and easy-to-follow writing. Especially sections 3 and 4 are well-organized and provide a good amount of high-level and low-level details about both the dataset statistics and experiment settings.", "weaknesses": "1. Lack of real world evaluations. Although a diverse simulation task suite provides many opportunities for studying algorithms, without a real world digital twin or policy transfer evaluation, the value of the benchmark is limited and it's unclear if any assumptions or implementation bugs in simulation environments would hinder transfer to the real world.\n\n2. Lack of qualitative results. The submission did not include any supplementary materials. For policy learning, it would have been much clearer to show videos of the successful policy rollouts and failure modes. For the task scene designs, it would also improve clarity if the authors can provide more renderings from more camera views into the scenes. \n \n3. In Table 4, the authors claimed \"Comparing the Human50 and Human300 settings, we see that increasing the number of pretraining tasks enables a significant improvement in downstream posttraining\" -- but the numbers between 2nd and 3rd columns are not really \"significantly\" different, e.g. 41.0 vs 41.2, or 26.2 vs 28.7. If increasing the number of pretraining tasks from 50 to 300, which is a big change, still yields very minimal performance gains, it makes it questionable whether the training and testing tasks are setup to have enough diversity and/or correlation.", "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": "dCnIqtp9jZ", "reviewer": "Reviewer_jCDL", "summary": "The authors propose a large scale imitation learning benchmark for household manipulation tasks, for studying pretraining and postraining of large scale generalist policy architectures. There are 2500 distinct kitchens, and 365 (some mobile) manipulation tasks, ranging from atomic to compositional.  The authors collect 100 demos for each pretraining task (300 out of 365), and also use the MimicGen framework to generate an additional 10k synthetic demonstrations for each task. The authors evaluate generalist policies in zero-shot task generalization after pretraining, improvement from pretraining and posttraining, and lifelong learning. They find that GROOT is the best for generalization, lifelong learning is still challenging, and that pretraining data composition matters over pure quantity.", "questions": "See weaknesses and questions above.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The benchmark seems quite comprehensive and the experiments are sensible and  systematic.\n\nOne very interesting result is that synthetic demos do not help at all. In the pretraining data study, they show that just using the human demos is good enough. I would like to see additional explanation in why MimicGen is not helpful, since MimicGen itself reports positive gains in using synthetic demos. \n\nThe paper is easy to read.", "weaknesses": "This is not really a substantial weakness, but the experiments and results are very straightforward and almost boring. It would be nice to see more interesting or qualitative phenomena, such as why synthetic demos are harmful, why GROOT outperforms other generalist policies (i.e. is hierarchy helpful), seeing if LoRA finetuning affects performance, both in 4.2 and 4.3, etc. Another suggestion is to stratify the tasks, like contact rich tasks, mobile manipulation tasks, etc. and see performance profiles across different tasks.", "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": "NMNZTv4rRP", "reviewer": "Reviewer_tQ4v", "summary": "The authors presented RoboCasa365, a large-scale simulation framework for benchmarking generalist robot policies, and offer large pre-training and post-training datasets as well. It provides 2500 kitchen environments with 2000 hours of simulated robot data.", "questions": "See my main points in the weakness section.\nOther small questions:\n- There is no discussion of the underlying simulation framework and how it compares to other simulation frameworks: how fast is it? Is GPU rendering supported?\n- Can you say more about how the MG60 dataset is generated and how diverse it is?\n- How optimal are the human teleoperated demonstration data?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The authors provide a valuable resource for the community to benchmark generalist robot policies: there is really high diversity in simulated scenes, and a large amount of demonstrations for pre-training and post-training\n- There are a large number (365) of tasks spanning across 50 different categories. There is a large number of scenes, with 50 different layouts and each have 50 different styles. This is a huge improvement over prior work.\n- The authors also provide a good tool to benchmark lifelong learning, and the dataset includes tasks with various numbers of subtasks.\n- The authors provide good experiments to demonstrate the use of the benchmark and the dataset", "weaknesses": "Overall this is a good paper and a great resource for the community. However, I think the experiments section is still a bit lacking, and misses a good opportunity to provide more insights into the dataset collected by the authors.\n\n- The writing in the experiments section is somewhat unclear: (1) It is unclear what the evaluation protocol is and how different models are compared (aka do you report success rate? Are there partial success? etc.). It is also unclear what the numbers mean in Table 1: is it success rate? (2) There lack discussion in the main text on the difference between the three models being compared, and lacks discussion on why GR00T N1.5 may be performing the best.  (3) Table 2 doesn’t bold the best numbers \n- In section 4.2 (specifically Figure 5 and Table 2), in would be interesting to see comparison with: finetune GR00NT 1.5 public checkpoint on a mix of pre-training + post-training data, but does not first do pre-training and then do fine-tuning, and instead just do “pre-training” over the entire data mix.\n- It is unclear what checkpoints the authors chose to evaluate for the experiments, and how they chose it. It is unclear how long “pre-training” is and how many epochs over the dataset it’s taken.\n- In Section 4.4, the authors observed that Human300 outperforms Human300+MG60, and they hypothesized that it is because “adding MimicGen data dilutes the contribution of other human datasets”. However, there is not support for this hypothesis. One experiment to verify this hypothesis would be to downweight the MG60 data in pre-training and compare. This is very important as a huge chunk of the dataset is composed of MimicGen data, and it would be important to know how useful this data is (or whether it is useful at all). If it is not useful, then the actual dataset would be much less than the 2000h claimed in the paper, and would need to adjust the writing to reflect that.\n- Related work section lacks full discussion on benchmarks for generalist robots outside of simulation, such as [1][2][3] and live competitions like [4] [5]\n\n[1] Train offline, test online: A real robot learning benchmark\n[2] Homerobot: Open-vocabulary mobile manipulation\n[3] AutoEval: Autonomous Evaluation of Generalist Robot Manipulation Policies in the Real World\n[4] The DARPA robotics challenge finals: Results and perspectives\n[5] Analysis and observations from the first amazon picking challenge", "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": "QkvLP31qKm", "reviewer": "Reviewer_S6tn", "summary": "This paper introduces RoboCasa365, a large-scale simulation framework designed for training and benchmarking generalist robot policies for everyday household tasks. Building upon the existing RoboCasa platform, the authors make significant contributions by massively scaling up the content and creating a structured evaluation suite. The framework includes 2,500 diverse kitchen environments derived from real-world home layouts, a comprehensive set of 365 everyday tasks (65 atomic and 300 composite), and over 2,200 hours of robot interaction data (both human-collected and synthetically generated via MimicGen). The work establishes benchmarks for three critical research areas: massively multi-task training, foundation model training (pre-training and fine-tuning), and lifelong learning. Through extensive experiments, the authors evaluate several state-of-the-art models (Diffusion Policy, π₀, and GR00T N1.5), demonstrating the utility of the benchmark for analyzing the impact of data scale, task diversity, and pre-training on policy generalization.", "questions": "The multi-task evaluation includes GR00T N1.5, a model primarily developed for humanoid robots. While its strong performance is interesting, could you discuss the implications of evaluating a humanoid-centric foundation model on a mobile manipulator task set? Does this suggest a high degree of embodiment-agnostic knowledge transfer, or are there specific architectural features (e.g., in its vision-language processing) that are particularly well-suited to the benchmark's challenges, independent of the final embodiment?", "limitations": "", "rating": 10, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- The primary strength of this work is the sheer scale and diversity of the simulation environment and accompanying datasets. The introduction of 2,500 unique kitchen scenes modeled after real-world homes and 365 distinct tasks represents a monumental step up from prior simulation benchmarks, which are often limited to a few scenes or a narrow set of tasks. This scale is critical for training and rigorously evaluating the generalization capabilities of high-capacity foundation models.\n- The authors have thoughtfully structured the entire framework to facilitate rigorous scientific inquiry. The explicit separation of assets, scenes, and tasks into \"pre-training\" and \"post-training\" splits  is a key design choice that enables controlled studies on generalization, data efficiency, and transfer learning, which are central questions in the field.\n- The paper provides not just a dataset, but a full suite of defined benchmarks for multi-task learning, foundation model training, and lifelong learning. This provides a ready-to-use toolkit for researchers to compare methods reproducibly and systematically analyze model performance across different learning paradigms.\n- The significant expansion of interactable and articulated appliances (from 20 to 456 instances across 12 categories)  is a substantial contribution. This allows for the creation of more complex, realistic, and long-horizon tasks  that better reflect the challenges of real-world household robotics, moving beyond simple pick-and-place scenarios.", "weaknesses": "While acknowledging the sim-to-real gap as a limitation, the paper lacks any empirical grounding or discussion of how its simulation-based success metrics might correlate with real-world performance. This is a critical omission for a benchmark intended to gauge progress towards deployable generalist robots. Recent work such as STAR-Gen[1]  proposes a detailed taxonomy for evaluating real-world generalization across distinct visual, semantic, and behavioral axes. The binary task success metric used in RoboCasa365 is coarse and may not be predictive of a policy's performance on these fine-grained real-world challenges. For instance, a policy might succeed across many simulated scenes but still be brittle to specific real-world semantic or behavioral perturbations not adequately modeled in simulation (e.g., changes in object mass or nuanced language instructions).\n\n[1] Gao et al. A Taxonomy for Evaluating Generalist Robot Policies.", "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)