arXiv:2606. 31494v1 Announce Type: cross Abstract: Humans and animals exhibit remarkable robustness in physical manipulation, yet robots remain far behind.
arXiv:2410. 24035v2 Announce Type: replace-cross Abstract: Generating robust and reactive manipulation strategies that can adapt to changing context information is a challenging task in robotics.
arXiv:2606. 05660v1 Announce Type: cross Abstract: Embodied AI systems are increasingly expected to reason and act over extended horizons in physical environments.
The paper introduces Watch, Infer, Coordinate, a benchmark and inference method for robots to deduce a partner’s physical constraints by observing joint behavior in physically coupled manipulation tasks. It demonstrates that understanding these constraints enables zero‑shot coordination on new tasks, achieving performance close to an oracle with true constraint knowledge. The study focuses on scenarios where a robot’s limitations may be unknown to its partner, such as hardware degradation or actuator faults.
arXiv:2607. 15641v1 Announce Type: cross Abstract: Humans combine reasoning and motor control to solve complex manipulation tasks under diverse constraints.
arXiv:2608. 01452v1 Announce Type: cross Abstract: Dynamic manipulation is a critical capability for robots operating in complex and dynamic environments, where robots must interact with objects that are moving or require rapid adjustments.
UniCross proposes a unified framework that models the four canonical dexterous manipulation skills—grasping, relocation, in-hand rotation, and in-hand translation—within a single formulation sharing state, action spaces, and objective structure. This approach enables distillation of a single cross-skill policy that performs well across all skills, generalizes to unseen objects, remains robust to disturbances, and can chain skills into long-horizon manipulation without policy switching. The framework also transfers effectively across different hand morphologies, suggesting that these skills can be viewed as instantiations of a shared task formulation.
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:2604. 25897v2 Announce Type: replace-cross Abstract: Contact variability, sensing uncertainty, and external disturbances make grasp execution stochastic.
arXiv:2602. 13197v2 Announce Type: replace-cross Abstract: The ability to learn manipulation skills by watching videos of humans has the potential to unlock a new source of highly scalable data for robot learning.
The paper introduces ORPA, a framework that adds a lightweight, feedback-conditioned module to a pretrained robotic manipulation policy, enabling real‑time residual adjustments in joint space without retraining the base policy. ORPA allows immediate correction of execution errors and distribution shifts, improving success rates and recovery on precision‑sensitive tasks compared to baseline policies and rule‑based inverse kinematics. The method is evaluated on the ALOHA platform, showing its effectiveness in real‑time deployment scenarios.
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:2606. 08414v1 Announce Type: cross Abstract: Diffusion policies have achieved remarkable success in robotic manipulation, yet they often fail to satisfy strict physical constraints required for safe deployment.