Results show that humans can integrate autonomous wearable limbs as out-of-sight partners in dynamic whole-body collaboration, and delineate behavioral and control principles underpinning fluency, stability, and usability in human augmentation.
Abstract
Supernumerary robotic limbs (SLs) could extend human motor capabilities beyond the natural body, yet most demonstrations have been limited to simple, quasi-static tasks or direct teleoperation under visual control. Whether humans can safely and intuitively collaborate with autonomous SLs during complex, dynamic tasks remains unknown. We studied this question in a demanding assembly task requiring coordinated transport and handovers of a long object, which could not be completed by a single person. We developed a reconfigurable backpack platform mounting up to four high-payload robotic arms, together with safety-aware motion planning that generates human-compatible trajectories from task states without training data. Despite the SLs operating out of sight, participants adapted quickly, completed the task reliably, and reported low cognitive demand alongside high perceived safety and predictability. Kinematic and force analyses showed that control design governed both movement fluency and whole-body stability: faster limb motions yielded smoother interactions and shorter completion times, and a mirrored coordination strategy–in which one robotic limb counterbalanced the other–further shortened duration and reduced variability in left-right ground reaction force differences during co-manipulation. These results show that humans can integrate autonomous wearable limbs as out-of-sight partners in dynamic whole-body collaboration, and delineate behavioral and control principles underpinning fluency, stability, and usability in human augmentation.
Supernumerary robotic limbs (SLs) have the potential to extend human sensorimotor capabilities by increasing effective degrees of freedom. However, current systems are designed for a single task and cannot flexibly coordinate with natural limbs (NLs). Here we propose a hierarchical control architecture for human–SL a...
D. Verdel, Jonathan Eden, Héctor Cervantes-Culebro et al.· The international journal of...· 1 citation
Supernumerary robotic fingers augment human dexterity by enabling a single hand to perform complex"hold-and-manipulate"tasks that typically require bimanual coordination. However, existing devices are often hindered by bulky hardware that limits interaction to large objects and rely on explicit hand motion triggers tha...
Jian-Yu Hou, Jia-Wei Fu, Wei-Kang Wan et al.· 0 citations
Humanoid robots are envisioned to perform diverse human-level tasks through their anthropomorphic morphology. Their dual-arm configuration enables a broad range of everyday actions. Yet, as in humans, this morphology can constrain loco-manipulation when both arms are occupied or additional contacts are required beyond...
Fu-Kang Liu, Fei-Yang Wu, Paul V. Barsa et al.· 0 citations
This work presents a gated residual coordination framework that keeps both modules frozen and applies bounded corrections to their outputs, and describes the contributions of reward gating, adaptive correction authority, and separate body and hand correction heads.
Rui Wu, Shuang Li, Li-Ding Zhang et al.· 1 citation
Human video offers a scalable source of robot demonstrations, yet most human-to-humanoid retargeting methods assume a legged robot with human-like kinematics. This assumption does not hold for mobile-base humanoids equipped with a wheeled base, vertical lift, and two arms. Human walking must be expressed through base m...
Jiyeon Koo, Eunseom Pyo, Jeonghee Seo et al.· 0 citations
Changing a humanoid robot's manipulation policy should not require retraining the controller that maintains the robot's balance. This study introduces DUET, a decoupled execution framework that enables locomotion and manipulation controllers to operate simultaneously by combining their commands only at the robot interf...
Kapalik Khanal, Biswash Khatiwada, S. Afrifa et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.