Source-linked AI summary
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation
Chengshu Li, Ruohan Zhang, Josiah Wong, Cem Gokmen, Sanjana Srivastava, Roberto Martín-Martín, Chen Wang, Gabrael Levine, Wensi Ai, Benjamin Martinez, Hang Yin, Michael Lingelbach, Minjune Hwang, Ayano Hiranaka, Sujay Garlanka, Arman Aydin, Sharon Lee, Jiankai Sun, Mona Anvari, Manasi Sharma, Dhruva Bansal, Samuel Hunter, Kyu-Young Kim, Alan Lou, Caleb R Matthews, Ivan Villa-Renteria, Jerry Huayang Tang, Claire Tang, Fei Xia, Yunzhu Li, Silvio Savarese, Hyowon Gweon, C. Karen Liu, Jiajun Wu, Li Fei-Fei
TL;DR
Existing embodied-AI benchmarks do not establish whether their tasks reflect human needs, motivating a benchmark grounded in people’s desired everyday activities. BEHAVIOR-1K combines 1,000 human-grounded activities and richly annotated scenes and objects with OMNIGIBSON’s realistic physics and rendering. Evaluations find that these activities remain extremely challenging for current AI systems, while initial sim-to-real experiments expose practical transfer difficulties.
Problem
Existing robotic benchmarks are often researcher-designed, so whether their tasks address humans’ actual needs remains unclear.
Method
BEHAVIOR-1K combines a human-grounded dataset of 1,000 activities with 50 scenes and 9,000+ annotated objects, instantiated in OMNIGIBSON.
Results
BEHAVIOR-1K activities are extremely challenging for current AI algorithms because they involve long horizons and complex physical manipulation.
Takeaways & Limitations
The benchmark provides a human-grounded, diverse, and realistic testbed for embodied-AI and robot-learning research.
Takeaways & Limitations
OMNIGIBSON excludes activities requiring human interaction and trades rendering speed for visual realism, reaching around 60 fps in a 60-object house scene.
Abstract
from arXiv · showhide
We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an extensive survey on "what do you want robots to do for you?". The first is the definition of 1,000 everyday activities, grounded in 50 scenes (houses, gardens, restaurants, offices, etc.) with more than 9,000 objects annotated with rich physical and semantic properties. The second is OMNIGIBSON, a novel simulation environment that supports these activities via realistic physics simulation and rendering of rigid bodies, deformable bodies, and liquids. Our experiments indicate that the activities in BEHAVIOR-1K are long-horizon and dependent on complex manipulation skills, both of which remain a challenge for even state-of-the-art robot learning solutions. To calibrate the simulation-to-reality gap of BEHAVIOR-1K, we provide an initial study on transferring solutions learned with a mobile manipulator in a simulated apartment to its real-world counterpart. We hope that BEHAVIOR-1K's human-grounded nature, diversity, and realism make it valuable for embodied AI and robot learning research. Project website: https://behavior.stanford.edu.
1 Introduction
BEHAVIOR-1K addresses the gap between researcher-designed robotic benchmarks and human needs by combining a human-grounded activity set with realistic simulation. Its evaluations indicate that long-horizon activities and complex manipulation remain challenging for current AI algorithms.
- Existing robotic benchmarks are typically designed by researchers, leaving unclear whether they address people’s actual needs.
- A survey of 1,461 participants identifies diversity and simulation realism as priorities for human-centered robotic benchmarks.
- BEHAVIOR-1K combines 1,000 activities with 50 scenes and 9,000+ annotated object models in a dataset grounded in human needs.
- OMNIGIBSON simulates rigid bodies, deformable bodies, fluids, and extended object states while supporting valid activity configurations and goals.
- Current reinforcement-learning baselines struggle with BEHAVIOR-1K because activities are long-horizon and require complex physical manipulation, motivating an initial simulation-to-real transfer study.
2 Creating a Benchmark Grounded in Human Needs: A Survey Study
The survey grounds BEHAVIOR-1K in activities that people want robots to perform, revealing broad and uneven preferences across everyday tasks. The resulting benchmark prioritizes highly preferred activities while retaining substantial task diversity.
- The survey asks people what they want robots to do and sources around 2,000 activities from time-use surveys.
- A Gini index of 0.158 indicates broad dispersion in preferences, spanning cleaning chores, cooking, and recreation.
- Tedious tasks such as scrubbing the bathroom floor rank highest, while recreational activities such as gameplay rank lowest.
- BEHAVIOR-1K selects 909 highest-preference activities and 91 activities from BEHAVIOR-100, totaling the top-ranked 1,000 activities.
3 Related Work: Embodied AI Benchmarks
BEHAVIOR-1K extends embodied-AI benchmarks toward human-grounded task selection, broader environments, and more realistic simulation. Compared with prior benchmarks, it combines greater activity and scene diversity with physics and rendering features for complex everyday interactions.
- BEHAVIOR-1K differs from prior benchmarks by grounding activity selection in laypeople’s needs rather than researcher choice.
- Its benchmark design targets a diversity-realism tradeoff that often limits prior simulation benchmarks.
- Compared with BEHAVIOR-100, BEHAVIOR-1K expands from 100 to 1,000 activities, from 15 to 50 scenes, and from 300+ to 1,900+ object categories.
- OMNIGIBSON adds realistic simulation of fluids, deformable bodies, flexible materials, and advanced rendering for the benchmark’s diverse activities.
4 BEHAVIOR-1K DATASET
The BEHAVIOR-1K DATASET turns human-grounded activities into semantically defined, ecologically plausible simulation tasks. It combines crowdsourced activity logic with richly annotated objects, states, transitions, scenes, and interactive 3D models.
- The dataset contains crowdsourced activity definitions, relevant objects and states, and a large repository of high-quality interactive 3D models.
- BDDL uses predicate logic to express intuitive object-based initial and goal conditions for each activity.
- The knowledge base maps objects from 5,000 WikiHow articles to 2,964 leaf-level synsets and associates them with simulatable states and transition rules.
- Five experienced annotators verified a subset of annotations with approval rates above 96.8%.
- The dataset includes 35 additional interactive scenes and 9,000+ object instances across 1,900+ categories with physical and semantic annotations.
5 OMNIGIBSON: Instantiating BEHAVIOR-1K with Realistic Simulation
OMNIGIBSON provides the simulation capabilities needed to instantiate BEHAVIOR-1K’s diverse activities, including realistic rendering and physics for rigid bodies, deformable objects, fluids, and flexible materials. Its extended object states and configuration tools further support activity initialization and goal evaluation.
- OMNIGIBSON extends iGibson 2.0 with simulation of rigid bodies, deformable objects, fluids, and flexible materials.These capabilities address physical processes required by diverse everyday activities.
- OMNIGIBSON generates highly realistic ray-traced or path-traced virtual images, significantly increasing benchmark realism.
- OMNIGIBSON models extended object states such as temperature and soaked level using heuristic transition rules.It also generates valid physical configurations for initial conditions and evaluates logical goal conditions.
- Over half of BEHAVIOR-1K activities would not be simulatable without realistic support for fluids, deformable bodies, and cloths.Examples include folding towels and pouring water.
6 Experiments: Evaluating Embodied AI Solutions in BEHAVIOR-1K
The experiments evaluate reinforcement-learning baselines in simulation and analyze the sim-real gap with a mobile manipulator. Results show that long-horizon activities, realistic grasping, memory, perception, and execution assumptions remain central challenges.
- Task Completion: RL-VMC fails on all three activities, whereas RL-Prim. and RL-Prim.Hist. achieve over 40% success across them.The evaluated activities are CollectTrash, StoreDecoration, and CleanTable.
- Task Completion: Longer-horizon activities are more difficult: CollectTrash requires at least 16 primitive steps, compared with 6 for CleanTable.History information particularly improves performance when observations are aliased in long-horizon tasks.
- Efficiency: RL-Prim.Hist. improves distance navigated, simulated time, and kinematic object disarrangement across all reported efficiency metrics.The history input helps remove redundant actions.
- Ablations: Fully physics-based grasping causes a radical performance drop during evaluation, while full trajectory motion execution causes much less performance loss.The training setup uses an assistive pick primitive and simplified feasibility checks.
- Sim-Real Gap: The real-world study transfers a policy from a simulated apartment to a real Tiago mobile manipulator using a digitally modeled counterpart scene.
- Sim-Real Gap: In simulation, most failures arise from visual-policy errors, while on the real robot grasping contributes around 40% of failures.The real-world trained policy also experiences errors from simulated-to-real visual differences and navigation-related base placement.
- Sim-Real Gap: The analysis identifies sim-real failure sources and severity as directions for future BEHAVIOR-1K research.
7 Discussion and Limitations
BEHAVIOR-1K combines a human-grounded activity benchmark with realistic simulation and finds that autonomous solutions remain beyond current state-of-the-art capabilities. Its scope is constrained by simulation performance, the exclusion of human interactions, and remaining sim-to-real limitations.
- BEHAVIOR-1K combines a semantic knowledge base and 3D model library with OMNIGIBSON’s realistic physics and rendering for 1,000 human-grounded activities.The benchmark includes rigid and deformable objects, flexible materials, and fluids.
- Current state-of-the-art AI algorithms cannot autonomously solve all 1,000 activities, which involve long horizons and complex manipulation.The authors studied a handful of activities with action primitives to identify challenging components.
- OMNIGIBSON reaches around 60 fps for a ray-traced house scene with around 60 objects, trading rendering speed for visual realism.The authors contrast this with around 100 fps in iGibson 2.0 and report ongoing performance optimization.
- The benchmark excludes activities requiring human interaction because realistic simulation of human behavior, motion, and appearance remains an open research problem.The authors plan to include simulated humans as the technology matures.
- OMNIGIBSON still has room to improve sim-to-real transfer through additions such as perception and actuation noise models.
A.3 Demographic Information
The survey participants broadly resemble the Mechanical Turk population but are not demographically representative of the U.S. population in every respect. Disability representation is limited, motivating future targeted surveys.
- Respondents were concentrated in the 30–40 age group, around 75% white, and generally earned $30,000–$150,000.The racial distribution was similar to Mechanical Turk proportions but had a larger white share than the U.S. population.
- 43.41% of participants were women, 55.50% men, 0.83% non-binary, and 0.26% other.
- Disability representation was limited: 92.56% reported no disability, 5.74% reported a disability, and 1.70% preferred not to answer.The authors identify people with disabilities and elderly people as candidates for future targeted surveys.
B Activity Annotation
BEHAVIOR-1K activity annotation builds ecologically plausible object and property representations from curated sources, then encodes realistic state transitions and validates annotation quality. The resulting BDDL extensions support substances, three-valued predicates, and object composition or decomposition.
- Object and activity annotation: Annotators collected five WikiHow articles per activity, extracted noun phrases, and manually filtered them into tangible objects.This process creates a natural and ecological object space for activity definitions.
- Object and activity annotation: The object space uses WordNet and custom synsets, with 1,538 WordNet leaf synsets and 1,426 custom synsets reported.Synset matching reduces word-sense ambiguity and creates a hierarchy for the knowledge base.
- Property annotation: Object properties are annotated for leaf-level synsets and inferred for higher-level synsets, enabling abstract definitions while preserving varied object instantiations.The hierarchy can let a definition request categories such as edible fruit rather than specific apple and banana instances.
- Property annotation: Property parameters encode object-specific physical requirements, such as the temperature needed to change a cookable object into a cooked object.The dataset includes object-property pairs and parameters because realistic processes depend on interactions between objects and states.
- Transition rules: Transition rules represent complex processes beyond current simulation capabilities while keeping the corresponding agent actions within reach.Examples include blending ingredients into a smoothie and sanding a rusted surface.
- Quality assessment: Annotation quality was high, with all labeling-task accuracies above 96%, F1 scores above 91%, and false-positive and false-discovery rates between 2–3%.Activity definitions also received consistently high Likert averages with low standard deviations across topics.
- New BDDL features: BEHAVIOR-1K extends BDDL with substances, three-valued predicates, and composition and decomposition of objects.These features expand the representation needed for diverse activities and realistic state changes.
D Scene and Object Models
BEHAVIOR-1K combines diverse scene and object models with extensive annotation and simulation-specific processing to support its 1,000 activities. OMNIGIBSON adds physical-state logic, multimodal sensing, and configurable action abstractions for realistic interaction.
- Scene coverage: BEHAVIOR-1K covers eight scene types across 50 scenes selected to support activities beyond household settings.The scene collection includes houses, houses with gardens, hotels, offices, grocery stores, generic halls, restaurants, and schools.
- Object coverage: 9,000+ object models from 1,900+ categories were added because initial scene models did not cover all activity requirements.The object categories support the diversity required by the activity annotation process.
- Asset processing: Scene and object assets required manual annotation and cleanup for categories, lighting, segmentation, articulation, interaction elements, coordinate frames, and physical properties.These changes were needed before vendor assets could be used for realistic simulation in OMNIGIBSON.
- Release: The project releases scenes, models, annotation documentation, and pipeline code, while encrypting models for use within OMNIGIBSON.The released pipeline also supports importing additional objects and scenes.
- Simulation interface: OMNIGIBSON maps physical states to binary logical predicates and supports visual, LiDAR, proprioceptive, and multiple action-space modalities.Predicate checks can use pose, contact, or temperature, while action spaces range from low-level control to primitive execution.
E.4 Performance Benchmark
The performance benchmark evaluates OMNIGIBSON and learning baselines under configurable simulation and control settings. The activities require long-horizon planning, manipulation primitives, and sparse-reward learning.
- Simulator performance: OMNIGIBSON performance is benchmarked in scenes containing 81 and 621 objects under different evaluation conditions.The benchmark reports simulation steps per second, with higher values indicating better performance.
- Simulator performance: OMNIGIBSON runs at comparable speed to iGibson 2.0 while providing higher ray-traced rendering quality.Users can trade simulation fidelity for speed by disabling fluids, cloth, object-state updates, or the robot.
- Tasks: The evaluated activities include storing decorations, collecting trash, and cleaning a table across three different B1K scenes.Their action spaces combine navigation, picking, placing, pushing, dipping, and wiping.
- Learning setup: The baselines use PPO or SAC with activity-definition success signals as rewards, creating a sparse-reward setting without intermediate subgoal rewards.For example, opening a drawer before placing a decoration receives no separate reward.
- Control: BEHAVIOR-1K activities require hundreds or thousands of environment steps, motivating time-extended action primitives for navigation and manipulation.The primitives compose collision-free motion, Cartesian trajectories, gripper actions, and interaction sequences.
F.5 Additional Metrics: success score Q
The additional evaluation reports success score Q and examines transfer between a simulated apartment and its real-world counterpart. The setup varies robot and object configurations and characterizes remaining sensor discrepancies.
- Success score Q: Success score Q is reported for each task as an additional performance metric.The authors state that Q follows the same trend as success rate.
- Real-world transfer: The real-world experiments use a Tiago++ mobile manipulator in a mockup apartment with a digital simulation counterpart.The simulated apartment was created from a scan of the real-world scene and then adapted for simulation.
- Experimental setup: Experiments randomize robot location, object positions, object orientations, and, for vision policies, lighting conditions.These factors are evaluated uniformly across the experimental runs.
- Sim-real gap: OMNIGIBSON produces high-fidelity sensor signals, but unmodeled real-sensor noise remains a source of sim-real gap.Examples include limited RGB dynamic range and depth-map shadows caused by projected light.
G.2 Additional Experiments
Additional real-world experiments compare three reinforcement-learning baselines and find the same performance ordering observed in simulation. Observation history improves the action-primitive baseline in this preliminary transfer study.
- Performance comparison: The real-world performance ordering is RL-VMC < RL-Prim. < RL-Prim.Hist., matching the trend observed in simulation.RL-VMC achieves zero success because sparse reward makes exploration difficult.
- Performance comparison: 0.64 cups/bottles were successfully placed by RL-Prim.Hist. on average, compared with 0 for RL-Prim.RL-Prim. tends to repeat actions because it lacks action-history information.
- Implication: The preliminary transfer result provides confidence that OMNIGIBSON can serve as a test bed for future sim-to-real robotics research.This conclusion is tied to the similar performance trend between simulation and the real world.
H Ethical Statement
BEHAVIOR-1K acknowledges that its human-need claims are shaped by survey-population biases. The authors identify demographic underrepresentation and propose broader community contribution to mitigate these limitations.
- Survey limitations: The survey results may be biased because researchers, annotators, and data providers represent only a small population relative to potential technology users.The authors plan to open-source the dataset and invite wider community contributions to its knowledge base.
- Survey limitations: The survey population skews white, male, non-disabled, middle-income, and toward ages 30–40 compared with the U.S. population.The authors state that this demographic skew may bias responses and create potential ethical limitations.
- Affected populations: These biases may underrepresent people who would be most affected by autonomous agents.The authors therefore asked additional questions about household work, assistance needs, and paying others for household work.