*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.
In a recent paper submitted to the arXiv* server, researchers introduced RoboHive, a comprehensive software platform and ecosystem for research in robot learning and embodied artificial intelligence (AI).
Background
In recent years, significant strides have been made in AI, notably in gaming, protein folding, and language modeling. However, Moravec's paradox proposes that the inherent complexity of sensorimotor behaviors has contributed to the relatively sluggish advancement in robot learning. Additionally, the intricate software frameworks and the lack of standardized benchmarks have hindered progress in this field. Unlike domains such as natural language processing or computer vision with established benchmarks, robotics remains fragmented.
In response to this, the authors introduce a comprehensive solution called RoboHive, a unified ecosystem customized for robot learning. RoboHive serves a dual purpose as a benchmarking suite and a research tool, offering a diverse range of environments, precise task definitions, and rigorous evaluation criteria for various learning paradigms, including reinforcement, imitation, and transfer learning.
RoboHive core components for realistic robot learning
The primary objective of RoboHive is the creation and open-sourcing of a unified framework for supporting research in robot learning. The various components of RoboHive are outlined below:
Abstract Robot Class for Simulation and Hardware Backends: At its core, RoboHive features an abstract robot class, a unifying simulator, and hardware backends. This class provides a unified application programming interface (API) for sensors and robots, simplifying the transition between simulation and the real world.
Environments: RoboHive offers a suite of environments organized into diverse categories, including hand manipulation, tabletop tasks, musculoskeletal systems, deformable materials, robotic suites, arm manipulation, multi-tasking, multi-agent interactions, and locomotion. These environments are accessible through the OpenAI Gym API.
Agents: By exposing environments through the OpenAI Gym API, RoboHive facilitates the development of various agents and algorithms for robot learning. It supports reinforcement learning (RL), offline RL, and imitation learning. Additionally, it provides teleoperation support for data collection.
RoboHive is designed to promote realistic robot learning by aligning its simulation models with real-world considerations. It is built on the MuJoCo physics engine and supports domain randomization, sensor noise, and delays. With out-of-the-box hardware support, it simplifies the deployment of results from simulation to the real world.
RoboHive's extensive suite of environments spans a wide range of challenges and offers a diverse array of tasks. The framework also decouples rewards and success criteria, enhancing the flexibility for evaluating agent performance. Baseline implementations and sample code are available for various environments, facilitating research in robot learning. The toolkit's teleoperation capabilities are applicable both in simulation and in the real world.
RoboHive: Empowering real-world robot learning
RoboHive is a versatile framework with capabilities and components that cater to diverse communities involved in robot learning. The key aspects of RoboHive are given below:
Repository of Environments and Agents: RoboHive provides a unified framework for environments and agents, offering a diverse range of robotics simulation assets and tasks. It is designed to meet the needs of research in RL and imitation learning. This framework simplifies the development and deployment of learning algorithms, both in simulation and in the real world. By matching simulation to real-world considerations, RoboHive enables researchers to deploy their ideas on robots, reducing barriers to entry while maintaining speed and accuracy.
Sim and Real Counterparts: RoboHive facilitates the seamless transition between simulation and reality, bridging the reality gap. It achieves this through near-photorealistic rendering, accurate physics modeling, and meticulous attention to real-world factors like latency and delays. The transfer between simulation and reality is simplified, making it a valuable tool for research on real-world robot learning.
Teleoperation and Dataset Collection: RoboHive addresses the need for meaningful datasets in robot learning. It offers pre-collected datasets known as RoboSet and teleoperation interfaces for data collection. The size and diversity of the dataset, along with the user-friendly teleoperation interface, facilitate data collection in both simulation and the real world. The RoboSet dataset includes one of the largest open-source real-world robotics datasets, covering a wide range of skills and tasks in multiple kitchen scenes.
Visual Imitation Learning and Offline RL: RoboHive supports a rich dataset for visual imitation learning and offline RL. Benchmarking various state-of-the-art visual representation learning algorithms on challenging domains is made possible with this dataset. The framework highlights the need for improved sensory-motor representations, particularly in the context of visual observations. It addresses the challenges posed by diverse visual observations during tasks and showcases its unique visual diversity.
Well-Packaged and Maintained Ecosystem: RoboHive is well-packaged and maintains a consistent structure that simplifies integration with environments of various natures. Its continuous integration workflow ensures the package's functionality and reliability. The framework boasts extensive documentation, tutorials, environment descriptions, issue tracking, discussion forums, and support commitments. The authors and the open-source community have actively maintained RoboHive for approximately five years, and they anticipate continuing this maintenance.
Reproducibility: RoboHive places a strong emphasis on reproducibility. Baselines are actively tracked across different software versions to monitor performance. Release notes provide detailed information on performance comparisons and runs across versions, ensuring transparency and reproducibility in research.
Conclusion
In summary, researchers introduced RoboHive, a comprehensive framework for robot learning. It facilitates efficient exploration and prototyping for researchers. Moreover, RoboHive seamlessly bridges the virtual and physical realms, enabling researchers to work with robotic hardware effortlessly.
*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.
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