FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation (2305.12821v1)

Abstract: Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP) have demonstrated impressive performance across various robotic manipulation tasks. However, these approaches have been limited to learning simple behaviors in current real-world manipulation benchmarks, such as pushing or pick-and-place. To enable more complex, long-horizon behaviors of an autonomous robot, we propose to focus on real-world furniture assembly, a complex, long-horizon robot manipulation task that requires addressing many current robotic manipulation challenges to solve. We present FurnitureBench, a reproducible real-world furniture assembly benchmark aimed at providing a low barrier for entry and being easily reproducible, so that researchers across the world can reliably test their algorithms and compare them against prior work. For ease of use, we provide 200+ hours of pre-collected data (5000+ demonstrations), 3D printable furniture models, a robotic environment setup guide, and systematic task initialization. Furthermore, we provide FurnitureSim, a fast and realistic simulator of FurnitureBench. We benchmark the performance of offline RL and IL algorithms on our assembly tasks and demonstrate the need to improve such algorithms to be able to solve our tasks in the real world, providing ample opportunities for future research.

• The paper introduces FurnitureBench as a reproducible benchmark for long-horizon robotic manipulation, focusing on complex furniture assembly tasks.
• It provides comprehensive resources, including 3D printable models, a robotic control software stack, detailed setup guides, and over 5000 teleoperated demonstrations.
• Benchmarking results show that both imitation learning and offline reinforcement learning methods face challenges with intricate manipulation tasks, emphasizing the need for further advances.

Insights into FurnitureBench: A Novel Benchmark for Complex Robotic Manipulation

The paper "FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation" introduces a comprehensive real-world benchmark designed to address long-horizon and complex robotic manipulation tasks, specifically focusing on furniture assembly. The primary motivation behind FurnitureBench is the need for reproducible and standardized environments that facilitate the reliable testing and comparison of various algorithms across research efforts worldwide.

This benchmark targets a significant challenge in robotics: enabling robots to autonomously perform complex, long-duration tasks such as furniture assembly, a task that naturally embodies complex planning, dexterous control, and robust visual perception requirements. The benchmark provides essential resources that lower the barrier for entry into complex manipulation research. This is accomplished through the provision of 3D printable models, a robotic control software stack, detailed setup guides, and a rich library of over 200 hours of teleoperated demonstration data, which includes more than 5000 demonstrations.

System Design and Implementation

The benchmark is carefully designed to ensure reproducibility using widely accessible hardware, such as the Franka Emika Panda robot arm and Intel RealSense cameras. These components are used to create a reproducible robotic environment that can be set up using the detailed instructions provided. The hardware selection and setup aim to standardize experimental conditions across various laboratories by minimizing variations in hardware configurations.

In addition, a comprehensive simulator, FurnitureSim, built upon Isaac Gym and Factory, complements the real-world benchmark. The simulation environment addresses the challenge of iterative testing by enabling rapid algorithm validation in a controlled, fast-rendering, and realistic environment. It further provides a platform for researchers to evaluate their algorithms with reduced infrastructure concerns.

Complexity and Challenges

FurnitureBench addresses multiple inherent complexities of the furniture assembly task, incorporating elements such as long-horizon planning and diverse dexterous skill requirements. The tasks include phases such as picking, placing, inserting, and screwing, each demanding fine-tuned precision, sophisticated control, and significant planning over long time horizons. These tasks extend the scope of traditional robotic benchmarks, which typically involve simpler skills like pick-and-place or basic object manipulation.

The evaluation framework is designed with several levels of initialization randomness, emphasizing the need for generalization in robotic manipulation. This multilevel evaluation allows testing in both controlled and highly variable initial conditions, challenging the flexibility and robustness of evaluated algorithms.

Benchmarking Results and Implications

Benchmarking results from the paper reveal that traditional imitation learning methods (BC) and cutting-edge offline reinforcement learning methods (IQL) face substantial hurdles in solving even part of the assembly tasks, highlighting the demanding nature of FurnitureBench. The results exhibit that state-of-the-art approaches struggle with the manipulation skills required in the benchmark, such as inserting and screwing, emphasizing the need for further advancements in algorithmic approaches to long-horizon planning and skill learning.

The evaluation also underscores the importance of diverse and extensive training data, multimodal sensor inputs (especially visual), and robust handling of stochastic dynamics. These insights suggest promising avenues for future developments in reinforcement learning and skill transfer across tasks and domains.

Conclusion

FurnitureBench is positioned as a crucial tool to drive forward the research in complex, long-horizon robotic manipulation. It provides a unified and reproducible framework for the community, fostering innovation in overcoming current limitations in robotic intelligence and behavior. By addressing real-world challenges, FurnitureBench pushes the boundaries of what autonomous systems can achieve, paving the way for the next generation of intelligent machines capable of performing intricate tasks in dynamic environments. These contributions lay a foundation for achieving substantial progress in robotic faculties needed for practical and impactful everyday automation.