The human body moves smoothly through coordinated soft elements including muscles, tendons, ligaments, and cushioning tissues. Conventional musculoskeletal robots often simplify these using wires or rigid joints, limiting biological flexibility and requiring extensive customization. Thus, a unified framework for design...
Reconfigurable robots address diverse tasks, including locomotion and object manipulation, by changing body configurations. In particular, modular robots expand body functions by connecting multiple body elements into various structures. However, these elements are typically mechanically fixed, and the focus is on func...
Reconfigurable robots offer significant potential for adapting to diverse tasks; however, conventional centralized architectures often require dedicated actuators for each module, leading to substantial increases in overall system weight, volume, and cost. To address these challenges, this paper presents the"Detachable...
This work proposes a framework that leverages egocentric human videos with state-of-the-art 3D Structure-from-Motion and hand mesh reconstruction to extract actionable affordances such as visual, grasp, and trajectory affordances that explicitly encode where to interact, how to grasp, and how to move.
Gated VLA-Cache is proposed, a lightweight, training-free extension that augments visual-similarity caching with neural introspection that improves reliability when blind caching hurts.
Effective learning design guidelines for realizing arm-based locomotion on life-sized robotic hardware and expanding the traversable workspace of robots are provided.
Ayumu Iwata, Kento Kawaharazuka, Keita Yoneda et al.· 0 citations
This study proposes a new method, Wrench-Augmented Reinforcement Learning (WARL), which introduces a wrenche (force and torque) into the action space, and shows that introducing a wrench can encourage behaviors that do not sufficiently exploit the robot's physical embodiment.
