In recent years, advances have been made in artificial intelligence (ai), especially in language modeling, protein folding, and gameplay. The development of learning robots has been modest. Moravec’s paradox, which holds that sensorimotor behaviors are intrinsically more difficult for ai agents than high-level cognitive activities, could be partly blamed for this slower progress. Furthermore, they must focus on a critical issue that is equally important: the complexity of software frameworks for robot learning and the absence of common benchmarks. As a result, the barrier to entry is raised, rapid prototyping is restricted, and the flow of ideas is restricted. The discipline of robotics remains more fragmented than others, such as computer vision or natural language processing, where benchmarks and data sets are standardized.
Researchers from the University of Washington, UC Berkeley, CMU, UT Austin, Open ai, Google ai, and Meta-ai provide RoboHive, an integrated environment designed specifically for robot learning, to close this gap. RoboHive is a platform that serves as a research and benchmarking tool. To enable a variety of learning paradigms, including reinforcement, imitation and transfer learning, it offers a wide range of contexts, specific task descriptions and strict evaluation criteria. For researchers, this makes efficient research and prototyping possible. Additionally, RoboHive offers customers hardware integration and teleoperation capabilities, enabling a seamless transition between virtual and real-world robots. They want to bridge the gap between the current state of robot learning and its development potential using RoboHive. The creation and open-sourcing of RoboHive, a unified framework for robot learning, is the main contribution of their work.
RoboHive’s notable features include:
1. The Environment Zoo: RoboHive offers several configurations that cover various academic fields. These configurations can be used for manipulation tasks, including dexterity manipulation, movement with bipedal and quadrupedal robots, and even manipulation using musculoskeletal models of the arm and hand. They use MuJoCo to power their virtual worlds, which offer fast physics simulation and are designed with a focus on physical realism.
2. RoboHive presents a unifying RobotClass abstraction that smoothly interacts with virtual and real robots through simhooks and hardware hooks. By changing a single flag, this special capability allows researchers to easily interact with robotic hardware and translate their discoveries from simulation to reality.
3. Teleoperation Support and Expert Dataset: RoboHive has out-of-the-box teleoperation capabilities across multiple modalities, including a keyboard, 3D spatial mouse, and virtual reality controllers. They are sharing RoboSet, one of the largest real-world manipulation data sets accumulated by human teleoperation, covering 12 skills in various culinary tasks. Researchers working in imitation learning, offline learning, and related disciplines will find these teleoperation data sets and capabilities especially useful.
4. Visual diversity and physical fidelity: RoboHive emphasizes projects with high physical realism and broad visual diversity, surpassing previous benchmarks, to reveal the next frontier of research in real-world robots. They link studies of visuomotor control to the visual difficulties of everyday life by including complex resources, rich textures, and enhanced scene layout. Additionally, RoboHive natively enables scene layout and randomization of the visual domain in various situations, increasing the adaptability of visual perception and delivering rich and realistic physical material.
5. Metrics and Baselines RoboHive uses brief and unambiguous metrics to evaluate the performance of the algorithm in various situations. The framework offers an easy-to-use gym-like API for seamless integration with learning algorithms, enabling accessibility for multiple academics and practitioners. Additionally, RoboHive contains comprehensive benchmark results for frequently researched algorithms within the research community in association with TorchRL and mjRL, providing a benchmark for performance comparison and study.
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Aneesh Tickoo is a consulting intern at MarktechPost. She is currently pursuing her bachelor’s degree in Data Science and artificial intelligence at the Indian Institute of technology (IIT), Bhilai. She spends most of her time working on projects aimed at harnessing the power of machine learning. Her research interest is image processing and she is passionate about creating solutions around it. She loves connecting with people and collaborating on interesting projects.
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