BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation
BEHAVIOR-1K: A benchmark with 1,000 everyday activities using OMNIGIBSON for realistic physics simulation.
Key Findings
Methodology
BEHAVIOR-1K benchmark includes 1,000 everyday activities based on 50 scenes and over 9,000 objects. The OMNIGIBSON simulation environment supports realistic physics simulation of rigid bodies, deformable bodies, and liquids. A survey of 1,461 participants was conducted to determine activities people want robots to perform, and these data were used to design the benchmark.
Key Results
- Experiments indicate that activities in BEHAVIOR-1K require long-horizon and complex manipulation skills, posing challenges even for state-of-the-art robot learning solutions.
- Solutions learned with a mobile manipulator in a simulated apartment show initial promise for real-world transfer.
- Benchmarking with reinforcement learning algorithms reveals that even a single activity is extremely challenging for current AI algorithms.
Significance
BEHAVIOR-1K provides a valuable resource for embodied AI and robot learning research through its human-grounded nature, diversity, and realism. It not only fills gaps in existing benchmarks but also guides the development of future AI agents and robots.
Technical Contribution
BEHAVIOR-1K significantly enhances diversity and realism in existing benchmarks by introducing a wide range of activities and a realistic simulation environment. OMNIGIBSON's physics simulation capabilities surpass existing environments, supporting more complex physical processes.
Novelty
BEHAVIOR-1K is the first robot benchmark designed based on human needs, covering a wide range of everyday activities. Its innovation lies in the diversity of activities and the realism of the simulation compared to existing work.
Limitations
- Current AI algorithms show limited performance in long-horizon and complex manipulation activities.
- The simulation-to-reality transfer remains challenging.
- The diversity of activities may increase the complexity of the simulation environment.
Future Work
Future work can focus on closing the simulation-to-reality gap, improving AI algorithm performance in complex tasks, and extending the benchmark to cover more types of activities.
AI Executive Summary
BEHAVIOR-1K is a human-centered robotics benchmark featuring 1,000 everyday activities, designed to meet the demand for robots performing tasks that people desire. Existing robotics benchmarks often lack diversity and realism, failing to effectively simulate the complex scenarios of human daily life.
BEHAVIOR-1K defines these activities through a survey of 1,461 participants and implements realistic physics simulation in the OMNIGIBSON environment. This environment supports the physical rendering of rigid bodies, deformable bodies, and liquids, providing a diverse and realistic testing platform.
Experimental results show that activities in BEHAVIOR-1K pose significant challenges to current AI algorithms, especially in long-horizon and complex manipulation skills. Future research will focus on closing the simulation-to-reality gap and improving AI algorithm performance.
Deep Analysis
Background
Advancements in robotics have made it possible to develop robots that meet human needs. However, existing robotics benchmarks are often designed by researchers and lack focus on actual human needs. BEHAVIOR-1K fills this gap by surveying the everyday activities people want robots to perform.
Core Problem
Existing robotics benchmarks lack diversity and realism, failing to effectively simulate the complex scenarios of human daily life. This limits the application and development of AI algorithms in the real world.
Innovation
BEHAVIOR-1K defines 1,000 everyday activities based on human needs and implements realistic physics simulation in OMNIGIBSON. Its innovation lies in the diversity of activities and the realism of the simulation.
Methodology
- �� Survey 1,461 participants to determine activity needs.
- �� Implement realistic physics simulation in OMNIGIBSON.
- �� Define initial and goal conditions for activities.
- �� Evaluate existing AI algorithms' performance in the benchmark.
Experiments
Experiments used reinforcement learning algorithms to evaluate the success rate and efficiency of performing activities in BEHAVIOR-1K. Benchmarking reveals that even a single activity is extremely challenging for current AI algorithms.
Results
Experimental results show that activities in BEHAVIOR-1K require long-horizon and complex manipulation skills, posing challenges even for state-of-the-art robot learning solutions.
Applications
BEHAVIOR-1K can be used to evaluate and improve AI algorithms' performance in complex tasks, especially those requiring long-horizon and complex manipulation skills.
Limitations & Outlook
Current AI algorithms show limited performance in long-horizon and complex manipulation activities. The simulation-to-reality transfer remains challenging.
Plain Language Accessible to non-experts
Imagine a large amusement park with various rides. BEHAVIOR-1K is like this amusement park, containing 1,000 different activity scenarios, each with its own challenges and fun. OMNIGIBSON is like the park's management system, ensuring each ride operates smoothly. Through this system, researchers can test their robots to see if they can successfully complete tasks in these complex scenarios.
ELI14 Explained like you're 14
Imagine you're in a huge amusement park with all sorts of rides. BEHAVIOR-1K is like this park, with 1,000 different activities, each with its own challenges. OMNIGIBSON is the park's management system, making sure everything runs smoothly. Researchers use this system to test their robots to see if they can complete tasks in these complex scenarios. Isn't that cool?
Glossary
BEHAVIOR-1K
A robotics benchmark with 1,000 everyday activities designed to evaluate AI algorithms' performance.
Used to test robots' performance in complex scenarios.
OMNIGIBSON
An environment supporting realistic physics simulation of rigid bodies, deformable bodies, and liquids.
Used to implement activities in BEHAVIOR-1K.
Reinforcement Learning
A machine learning method that optimizes strategies through trial and error.
Used to train robots' performance in BEHAVIOR-1K.
Physics Simulation
Simulating real-world physical processes in a computer.
Used to create realistic testing environments.
Human-Robot Interaction
The study of interactions between humans and computer systems.
Used to design robots that better meet human needs.
Open Questions Unanswered questions from this research
- 1 How to close the simulation-to-reality gap to improve AI algorithms' performance in the real world.
- 2 How to extend the benchmark to cover more activity types without increasing complexity.
Applications
Immediate Applications
Robot Testing
Researchers can use BEHAVIOR-1K to evaluate and improve AI algorithms' performance in complex tasks.
Long-term Vision
Smart Homes
In the future, robots could perform complex everyday tasks in homes, improving quality of life.
Abstract
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.