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Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation
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Many real-world manipulation tasks consist of a series of subtasks that are significantly different from one another. Such long-horizon, complex tasks highlight the potential of dexterous hands, which possess adaptability and versatility, capable of seamlessly transitioning between different modes of functionality without the need for re-grasping or external tools. However, the challenges arise due to the high-dimensional action space of dexterous hand and complex compositional dynamics of the long-horizon tasks. We present Sequential Dexterity, a general system based on reinforcement learning (RL) that chains multiple dexterous policies for achieving long-horizon task goals. The core of the system is a transition feasibility function that progressively finetunes the sub-policies for enhancing chaining success rate, while also enables autonomous policy-switching for recovery from failures and bypassing redundant stages. Despite being trained only in simulation with a few task objects, our system demonstrates generalization capability to novel object shapes and is able to zero-shot transfer to a real-world robot equipped with a dexterous hand. Code and videos are available at https://sequential-dexterity.github.io
Forward citations
Cited by 7 Pith papers
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WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation
WristMimic achieves comparable or superior object manipulation retargeting by supervising wrist kinematics while letting finger behavior emerge from object and contact dynamics.
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Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?
Task-agnostic RL play pretraining on diverse objects yields a reusable dexterous prior that makes sparse-reward assembly learning ~33× more sample-efficient and enables zero-shot sim-to-real transfer on tight insertio...
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A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards
IKER uses VLM-generated keypoint rewards to train manipulation policies in simulation that transfer to a real robot, enabling multi-step tasks and replanning.
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CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World
CordViP achieves strong real-world dexterous manipulation by feeding a diffusion policy with pose-tracked 3D object models and hand point clouds, pretrained on contact maps and arm-hand coordination.
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ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks
MS-HAB is a GPU-accelerated low-level manipulation benchmark based on HAB, with RL and IL baselines and rule-based trajectory filtering for controlled data generation.
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DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation
Using object affordance maps as priors and constraints improves success rates of dexterous manipulation RL policies by an average of 15.4% in simulation.
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Modality-Driven Design for Multi-Step Dexterous Manipulation: Insights from Neuroscience
A neuroscience-inspired, modality-driven pipeline with classical control, a vision-language-action model, and force-feedback RL performs pick-and-rotate on a real robot, but only 5 of 35 trials complete all steps.
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