Vision-Language-Action Model and Diffusion Policy Switching Enables Dexterous Control of an Anthropomorphic Hand

• URL: http://arxiv.org/abs/2410.14022v1
• Date: Thu, 17 Oct 2024 20:49:45 GMT
• Title: Vision-Language-Action Model and Diffusion Policy Switching Enables Dexterous Control of an Anthropomorphic Hand
• Authors: Cheng Pan, Kai Junge, Josie Hughes,
• Abstract summary: We propose a hybrid control method that combines the relative advantages of a fine-tuned Vision-Language-Action model and diffusion models. We demonstrate this model switching approach results in a over 80% success rate compared to under 40% when only using a VLA model.
• Score: 2.7036595757881323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To advance autonomous dexterous manipulation, we propose a hybrid control method that combines the relative advantages of a fine-tuned Vision-Language-Action (VLA) model and diffusion models. The VLA model provides language commanded high-level planning, which is highly generalizable, while the diffusion model handles low-level interactions which offers the precision and robustness required for specific objects and environments. By incorporating a switching signal into the training-data, we enable event based transitions between these two models for a pick-and-place task where the target object and placement location is commanded through language. This approach is deployed on our anthropomorphic ADAPT Hand 2, a 13DoF robotic hand, which incorporates compliance through series elastic actuation allowing for resilience for any interactions: showing the first use of a multi-fingered hand controlled with a VLA model. We demonstrate this model switching approach results in a over 80\% success rate compared to under 40\% when only using a VLA model, enabled by accurate near-object arm motion by the VLA model and a multi-modal grasping motion with error recovery abilities from the diffusion model.

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