ContactExplorer: Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation
arXiv:2603.10971v2 Announce Type: replace-cross Abstract: Reinforcement learning has achieved remarkable success in domains such as Atari games, navigation, and locomotion, where exploration can often be guided by novelty over states or dynamics. In contrast, dexterous manipulation requires rich physical hand--object interactions, but existing methods often suffer from unstable contact-based novelty signals, inefficient distance novelty signals, or reliance on task-specific priors. We propose ContactExplorer, a general exploration method for dexterous manipulation tasks. ContactExplorer repres
Advances in reinforcement learning and computational power are enabling more sophisticated approaches to robotic manipulation, pushing the boundaries of what is possible in dexterity.
Improved dexterous manipulation is a critical bottleneck for general-purpose robots, impacting industries from manufacturing and logistics to healthcare and domestic services.
The ContactExplorer method offers a more stable and efficient exploration strategy for complex hand-object interactions, potentially accelerating the development of capable general-purpose dexterous robots.
- · Robotics companies
- · Automation sector
- · Logistics and manufacturing
- · AI research institutions
- · Tasks requiring manual dexterity
- · Traditional manufacturing processes
More robust and adaptable robotic systems capable of intricate manipulation tasks become feasible.
Accelerated adoption of dexterous robots in new applications that currently rely on human labor.
Potential for humanoid robots to perform highly complex and nuanced physical tasks in unstructured environments.
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Read at arXiv cs.AI