Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Open accessAbstract
Dexterous multi-fingered hands are extremely versatile and provide a generic way to perform a multitude of tasks in human-centric environments.However, effectively controlling them remains challenging due to their high dimensionality and large number of potential contacts.Deep reinforcement learning (DRL) provides a model-agnostic approach to control complex dynamical systems, but has not been shown to scale to highdimensional dexterous manipulation.Furthermore, deployment of DRL on physical systems remains challenging due to sample inefficiency.Consequently, the success of DRL in robotics has thus far been limited to simpler manipulators and tasks.In this work, we show that model-free DRL can effectively scale up to complex manipulation tasks with a high-dimensional 24-DoF hand, and solve them from scratch in simulated experiments.Furthermore, with the use of a small number of human demonstrations, the sample complexity can be significantly reduced, which enables learning with sample sizes equivalent to a few hours of robot experience.The use of demonstrations result in policies that exhibit very natural movements and, surprisingly, are also substantially more robust.We demonstrate successful policies for object relocation, in-hand manipulation, tool use, and door opening, which are shown in the supplementary video.
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Robots still struggle to solve high-level tasks such as locomotion, autonomous driving and dexterous manipulation, even though low-level controllers and motion planning algorithms are robust enough to drive joints and end-effectors to desired configurations.
“Even though low-level controllers and motion planning algorithms are robust enough to drive the joints and end-effectors to reach their desired configuration, robots still struggle to solve high-level tasks, such as locomotion [3], autonomous driving [4] and dexterous manipulation [5].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [5]
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From Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey (2020) · cites this paper as [68]
The Related Work and Demo Augmented Policy Gradient sections discuss imitation learning and reinforcement learning for dexterous manipulation. A search for domain classifiers, source and target domains, and invariant features found no passage addressing the attributed claim; page numbers were not available in our copy. (LLM, conf=0.76)
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“References [1] Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine.”
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MethodSelf-citationKinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks
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“Kinetix is broad enough to represent robotics tasks like grasping (Rajeswaran et al., 2017) and locomotion (Todorov et al., 2012), classic RL environments such as Cartpole (Barto et al., 1983), Acrobot (DeJong & Spong, 1994) and Lunar Lander (Brockman et al., 2016), as well as video games like…”
- Authors
- Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., & Levine, S.
- Affiliations
- University of Washington (US)
- University of California, Berkeley (US)
- Italian Institute of Technology (IT)
- OpenAI (United States) (US)
- Venue
- Robotics: Science and Systems XIV
- Published
- 2018-06-26
- OpenAlex
- W2757631751
- Cited by
- 847
- References
- 56
- Citations by year
- 2026: 53 · 2025: 141 · 2024: 108 · 2023: 110 · 2022: 90 · 2021: 133 · 2020: 103 · 2019: 82 · 2018: 26 · 2017: 1
- FWCI
- 34.22
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Robotics: Science and Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- dexterous manipulation, multi-fingered hand, deep reinforcement learning, imitation learning, human demonstrations, sample efficiency, high-dimensional control, in-hand manipulation, tool use, door opening, object relocation, robotic grasping
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