arXiv:2607. 00033v1 Announce Type: cross Abstract: Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging.
arXiv:2610.14372v1 Announce Type: cross Abstract: Dexterous robot hands can receive useful motion intent from teleoperation or learned policies, but successful execution also depends on contact decis...
arXiv:2607. 11874v1 Announce Type: cross Abstract: Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them.
DexPIE is a post‑training framework that improves dexterous manipulation policies using real‑world experience. It introduces a dexterous‑hand‑adapted intervention system and multi‑stage DAgger‑style data collection to enhance exploration, aligns training and inference to reduce distribution shift, and conditions the policy on a continuous optimality indicator for fine‑grained data quality use. In three real‑world tasks, DexPIE boosts success rates by 37.3% over a demonstration‑based baseline, outperforming all other methods and showing stronger robustness.
arXiv:2607. 12105v1 Announce Type: cross Abstract: In-hand manipulation without external sensing is challenging due to uncertainties from finger-object contacts and disturbances by gravity.
Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them. But how does this recipe transfer to dexterous manipulation?
arXiv:2606. 14561v1 Announce Type: cross Abstract: Robotics manipulation research increasingly focuses on two-finger parallel grippers for their effectiveness, affordability, and ease of teleoperation.
ProxiDex is a dynamics‑guided proximity policy framework for multi‑finger dexterous manipulation that treats hand‑object proximity as an interaction state. It reconstructs interaction point clouds, converts geometric distances into proximity cues, and learns action‑conditioned proximity dynamics using a coupled forward‑inverse design. The framework adaptively reweights proximity tokens across manipulation phases and employs dynamics‑consistency supervision to stabilize action generation, leading to improved success rates and robustness in both simulation and real‑world experiments.
arXiv:2609.34182v2 Announce Type: replace-cross Abstract: Dexterous manipulation requires tactile feedback. However, robot tactile demonstrations are difficult to scale,because dexterous-hand teleope...
This paper introduces an energy-aware approach to robotic manipulation by defining a joint-space mechanical-work proxy based on joint torque and angular displacement. A differentiable energy predictor is trained to estimate this work from robot states and actions, enabling it to serve as a regularizer that fine‑tunes a pretrained manipulation policy. Applied to RVT‑2 on RLBench, the method reduces average mechanical work from 208.8 J to 204.4 J (a 2.1 % drop) while slightly improving task success from 86.2 % to 86.9 % across 12 manipulation tasks.
Multi-finger dexterous manipulation relies on stable hand-object interactions, yet these interactions are partially observable in practice. Visual observations are often occluded by the hand, tactile...
arXiv:2610.22340v1 Announce Type: cross Abstract: How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present DSP: Dexterity from supervised pla...