POISE (Palm-relative Object reaching In SE(3), a sim-to-real reinforcement learning framework for in-hand 6D object pose reaching, combines diverse stable-grasp initialization, goal- and geometry-conditioned control, an adaptive 6D goal curriculum, and a compact reward scheme for pose reaching and grasp preservation.
This research presents an embodied control approach based on real-time task Jacobian estimation of the combined hand and object system on the physical robot and showcases an alternative to compute- and data-heavy approaches such as RL and IL for achieving dexterous manipulation through computationally simple and data-e...
Simulation Pre-training for Dexterity (SPD) is introduced, a pre-training framework for dexterous manipulation that uses data entirely collected in simulation and outperforms training behavior cloning policies from scratch, showing that simulation teleoperation is a viable pre-training source for real-world dexterous m...
DemoMimic (Dexterous Motion Mimic), a policy that manipulates objects by focusing on their geometry local to the contact points to improve sim-to-real consistency, yielding a single real-world policy that transfers across objects of varying shape, scale, mass, and friction wherever local contact structure is preserved.
NestDex, a nested policy-learning framework that reduces this burden by using learned hand skills to assist demonstration collection, improves demonstration reliability and efficiency, and the resulting empirical evaluations support effective autonomous policy learning.
This work proposes a real-world bimanual grasping framework that includes a multimodal dataset capturing joint angles, visual observations and force signals; a Denoising Diffusion Probabilistic Model (DDPM)-based module that generates joint-level grasp configurations from segmented point clouds; and an execution strate...
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