arXiv:2606.11525v1 Announce Type: cross Abstract: Contrastive Reinforcement Learning (CRL) has seen recent success in a wide variety of goal-conditioned robotics tasks by learning structured representations of the dynamics. However, despite its success in locomotion and simpler control domains, CRL often struggles in interaction-rich manipulation. We argue that a key source of this difficulty is object-centric interaction, such as contact or grasping, that induces distinct changes in the underlying dynamic modes. In this work, we formulate manipulation dynamics as a piecewise-smooth Markov pro
The paper addresses current limitations of Contrastive Reinforcement Learning in complex robot manipulation, pushing the boundaries of what's possible for AI to learn from interaction.
Improved object manipulation capabilities in AI systems are crucial for advancing robotics, enabling more versatile and autonomous agents in diverse real-world applications.
This work introduces a new approach to handling dynamic mode changes in object-centric interaction, enabling more robust and effective learning for complex manipulation tasks.
- · AI/Robotics researchers
- · Robotics manufacturers
- · Industrial automation sector
- · Tasks requiring bespoke, hand-engineered robotic solutions
Robots will become more capable at handling a wider variety of objects and tasks in unstructured environments.
This could accelerate the deployment of autonomous robots in logistics, manufacturing, and even domestic settings.
Increased robotic dexterity may create new service industries and reduce labor costs in sectors reliant on fine motor skills.
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Read at arXiv cs.LG